thesis big data supply chain

• Survey Paper
• Open access
• Published: 25 July 2020

Predictive big data analytics for supply chain demand forecasting: methods, applications, and research opportunities

• Mahya Seyedan 1 &
• Fereshteh Mafakheri   ORCID: orcid.org/0000-0002-7991-4635 1  

Journal of Big Data volume  7 , Article number:  53 ( 2020 ) Cite this article

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Big data analytics (BDA) in supply chain management (SCM) is receiving a growing attention. This is due to the fact that BDA has a wide range of applications in SCM, including customer behavior analysis, trend analysis, and demand prediction. In this survey, we investigate the predictive BDA applications in supply chain demand forecasting to propose a classification of these applications, identify the gaps, and provide insights for future research. We classify these algorithms and their applications in supply chain management into time-series forecasting, clustering, K-nearest-neighbors, neural networks, regression analysis, support vector machines, and support vector regression. This survey also points to the fact that the literature is particularly lacking on the applications of BDA for demand forecasting in the case of closed-loop supply chains (CLSCs) and accordingly highlights avenues for future research.

Introduction

Nowadays, businesses adopt ever-increasing precision marketing efforts to remain competitive and to maintain or grow their margin of profit. As such, forecasting models have been widely applied in precision marketing to understand and fulfill customer needs and expectations [ 1 ]. In doing so, there is a growing attention to analysis of consumption behavior and preferences using forecasts obtained from customer data and transaction records in order to manage products supply chains (SC) accordingly [ 2 , 3 ].

Supply chain management (SCM) focuses on flow of goods, services, and information from points of origin to customers through a chain of entities and activities that are connected to one another [ 4 ]. In typical SCM problems, it is assumed that capacity, demand, and cost are known parameters [ 5 ]. However, this is not the case in reality, as there are uncertainties arising from variations in customers’ demand, supplies transportation, organizational risks and lead times. Demand uncertainties, in particular, has the greatest influence on SC performance with widespread effects on production scheduling, inventory planning, and transportation [ 6 ]. In this sense, demand forecasting is a key approach in addressing uncertainties in supply chains [ 7 , 8 , 9 ].

A variety of statistical analysis techniques have been used for demand forecasting in SCM including time-series analysis and regression analysis [ 10 ]. With the advancements in information technologies and improved computational efficiencies, big data analytics (BDA) has emerged as a means of arriving at more precise predictions that better reflect customer needs, facilitate assessment of SC performance, improve the efficiency of SC, reduce reaction time, and support SC risk assessment [ 11 ].

The focus of this meta-research (literature review) paper is on “demand forecasting” in supply chains. The characteristics of demand data in today’s ever expanding and sporadic global supply chains makes the adoption of big data analytics (and machine learning) approaches a necessity for demand forecasting. The digitization of supply chains [ 12 ] and incoporporation Blockchain technologies [ 13 ] for better tracking of supply chains further highlights the role of big data analytics. Supply chain data is high dimensional generated across many points in the chain for varied purposes (products, supplier capacities, orders, shipments, customers, retailers, etc.) in high volumes due to plurality of suppliers, products, and customers and in high velocity reflected by many transactions continuously processed across supply chain networks. In the sense of such complexities, there has been a departure from conventional (statistical) demand forecasting approaches that work based on identifying statistically meannignful trends (characterized by mean and variance attributes) across historical data [ 14 ], towards intelligent forecasts that can learn from the historical data and intelligently evolve to adjust to predict the ever changing demand in supply chains [ 15 ]. This capability is established using big data analytics techniques that extract forecasting rules through discovering the underlying relationships among demand data across supply chain networks [ 16 ]. These techniques are computationally intensive to process and require complex machine-programmed algorithms [ 17 ].

With SCM efforts aiming at satisfying customer demand while minimizing the total cost of supply, applying machine-learning/data analytics algorithms could facilitate precise (data-driven) demand forecasts and align supply chain activities with these predictions to improve efficiency and satisfaction. Reflecting on these opportunities, in this paper, first a taxonmy of data sources in SCM is proposed. Then, the importance of demand management in SCs is investigated. A meta-research (literature review) on BDA applications in SC demand forecasting is explored according to categories of the algorithms utilized. This review paves the path to a critical discussion of BDA applications in SCM highlighting a number of key findings and summarizing the existing challenges and gaps in BDA applications for demand forecasting in SCs. On that basis, the paper concludes by presenting a number of avenues for future research.

Data in supply chains

Data in the context of supply chains can be categorized into customer, shipping, delivery, order, sale, store, and product data [ 18 ]. Figure  1 provides the taxonomy of supply chain data. As such, SC data originates from different (and segmented) sources such as sales, inventory, manufacturing, warehousing, and transportation. In this sense, competition, price volatilities, technological development, and varying customer commitments could lead to underestimation or overestimation of demand in established forecasts [ 19 ]. Therefore, to increase the precision of demand forecast, supply chain data shall be carefully analyzed to enhance knowledge about market trends, customer behavior, suppliers and technologies. Extracting trends and patterns from such data and using them to improve accuracy of future predictions can help minimize supply chain costs [ 20 , 21 ].

Taxonomy of supply chain data

Analysis of supply chain data has become a complex task due to (1) increasing multiplicity of SC entities, (2) growing diversity of SC configurations depending on the homogeneity or heterogeneity of products, (3) interdependencies among these entities (4) uncertainties in dynamical behavior of these components, (5) lack of information as relate to SC entities; [ 11 ], (6) networked manufacturing/production entities due to their increasing coordination and cooperation to achieve a high level customization and adaptaion to varying customers’ needs [ 22 ], and finally (7) the increasing adoption of supply chain digitization practices (and use of Blockchain technologies) to track the acitivities across supply chains [ 12 , 13 ].

Big data analytics (BDA) has been increasingly applied in management of SCs [ 23 ], for procurement management (e.g., supplier selection [ 24 ], sourcing cost improvement [ 25 ], sourcing risk management [ 26 ], product research and development [ 27 ], production planning and control [ 28 ], quality management [ 29 ], maintenance, and diagnosis [ 30 ], warehousing [ 31 ], order picking [ 32 ], inventory control [ 33 ], logistics/transportation (e.g., intelligent transportation systems [ 34 ], logistics planning [ 35 ], in-transit inventory management [ 36 ], demand management (e.g., demand forecasting [ 37 ], demand sensing [ 38 ], and demand shaping [ 39 ]. A key application of BDA in SCM is to provide accurate forecasting, especially demand forecasting, with the aim of reducing the bullwhip effect [ 14 , 40 , 41 , 42 ].

Big data is defined as high-volume, high-velocity, high-variety, high value, and high veracity data requiring innovative forms of information processing that enable enhanced insights, decision making, and process automation [ 43 ]. Volume refers to the extensive size of data collected from multiple sources (spatial dimension) and over an extended period of time (temporal dimension) in SCs. For example, in case of freight data, we have ERP/WMS order and item-level data, tracking, and freight invoice data. These data are generated from sensors, bar codes, Enterprise resource planning (ERP), and database technologies. Velocity can be defined as the rate of generation and delivery of specific data; in other words, it refers to the speed of data collection, reliability of data transferring, efficiency of data storage, and excavation speed of discovering useful knowledge as relate to decision-making models and algorithms. Variety refers to generating varied types of data from diverse sources such as the Internet of Things (IoT), mobile devices, online social networks, and so on. For instance, the vast data from SCM are usually variable due to the diverse sources and heterogeneous formats, particularly resulted from using various sensors in manufacturing sites, highways, retailer shops, and facilitated warehouses. Value refers to the nature of the data that must be discovered to support decision-making. It is the most important yet the most elusive, of the 5 Vs. Veracity refers to the quality of data, which must be accurate and trustworthy, with the knowledge that uncertainty and unreliability may exist in many data sources. Veracity deals with conformity and accuracy of data. Data should be integrated from disparate sources and formats, filtered and validated [ 23 , 44 , 45 ]. In summary, big data analytics techniques can deal with a collection of large and complex datasets that are difficult to process and analyze using traditional techniques [ 46 ].

The literature points to multiple sources of big data across the supply chains with varied trade-offs among volume, velocity, variety, value, and veracity attributes [ 47 ]. We have summarized these sources and trade-offs in Table  1 . Although, the demand forecasts in supply chains belong to the lower bounds of volume, velocity, and variety, however, these forecasts can use data from all sources across the supply chains from low volume/variety/velocity on-the-shelf inventory reports to high volume/variety/velocity supply chain tracking information provided through IoT. This combination of data sources used in SC demand forecasts, with their diverse temporal and spatial attributes, places a greater emphasis on use of big data analytics in supply chains, in general, and demand forecasting efforts, in particular.

The big data analytics applications in supply chain demand forecasting have been reported in both categories of supervised and unsupervised learning. In supervised learning, data will be associated with labels, meaning that the inputs and outputs are known. The supervised learning algorithms identify the underlying relationships between the inputs and outputs in an effort to map the inputs to corresponding outputs given a new unlabeled dataset [ 48 ]. For example, in case of a supervised learning model for demand forecasting, future demand can be predicted based on the historical data on product demand [ 41 ]. In unsupervised learning, data are unlabeled (i.e. unknown output), and the BDA algorithms try to find the underlying patterns among unlabeled data [ 48 ] by analyzing the inputs and their interrelationships. Customer segmentation is an example of unsupervised learning in supply chains that clusters different groups of customers based on their similarity [ 49 ]. Many machine-learning/data analytics algorithms can facilitate both supervised learning (extracting the input–output relationships) and unsupervised learning (extracting inputs, outputs and their relationships) [ 41 ].

Demand management in supply chains

The term “demand management” emerged in practice in the late 1980s and early 1990s. Traditionally, there are two approaches for demand management. A forward approach which looks at potential demand over the next several years and a backward approach that relies on past or ongoing capabilities in responding to demand [ 50 ].

In forward demand management, the focus will be on demand forecasting and planning, data management, and marketing strategies. Demand forecasting and planning refer to predicting the quantities and timings of customers’ requests. Such predictions aim at achieving customers’ satisfaction by meeting their needs in a timely manner [ 51 ]. Accurate demand forecasting could improve the efficiency and robustness of production processes (and the associated supply chains) as the resources will be aligned with requirements leading to reduction of inventories and wastes [ 52 , 53 ].

In the light of the above facts, there are many approaches proposed in the literature and practice for demand forecasting and planning. Spreadsheet models, statistical methods (like moving averages), and benchmark-based judgments are among these approaches. Today, the most widely used demand forecasting and planning tool is Excel. The most widespread problem with spreadsheet models used for demand forecasting is that they are not scalable for large-scale data. In addition, the complexities and uncertainties in SCM (with multiplicity and variability of demand and supply) cannot be extracted, analyzed, and addressed through simple statistical methods such as moving averages or exponential smoothing [ 50 ]. During the past decade, traditional solutions for SC demand forecasting and planning have faced many difficulties in driving the costs down and reducing inventories [ 50 ]. Although, in some cases, the suggested solutions have improved the day’s payable, they have pushed up the SC costs as a burden to suppliers.

The era of big data and high computing analytics has enabled data processing at a large scale that is efficient, fast, easy, and with reduced concerns about data storage and collection due to cloud services. The emergence of new technologies in data storage and analytics and the abundance of quality data have created new opportunities for data-driven demand forecasting and planning. Demand forecast accuracy can be significantly improved with data-mining algorithms and tools that can sift through data, analyze the results, and learn about the relationships involved. This could lead to highly accurate demand forecasting models that learn from data and are scalable for application in SCM. In the following section, a review of BDA applications in SCM is presented. These applications are categorized based on the employed techniques in establishing the data-drive demand forecasts.

BDA for demand forecasting in SCM

This survey aims at reviewing the articles published in the area of demand and sales forecasting in SC in the presence of big data to provide a classification of the literature based on algorithms utilized as well as a survey of applications. To the best of our knowledge, no comprehensive review of the literature specifically on SC demand forecasting has been conducted with a focus on classification of techniques of data analytics and machine learning. In doing so, we performed a thorough search of the existing literature, through Scopus, Google Scholar, and Elsevier, with publication dates ranging from 2005 to 2019. The keywords used for the search were supply chain, demand forecasting, sales forecasting, big data analytics, and machine learning.

Figure  2 shows the trend analysis of publications in demand forecasting for SC appeared from 2005 to 2019. There is a steadily increasing trend in the number of publications from 2005 to 2019. It is expected that such growth continues in 2020. Reviewing the past 15 years of research on big data analysis/machine learning applications in SC demand forecasting, we identified 64 research papers (excluding books, book chapters, and review papers) and categorized them with respect to the methodologies adopted for demand forecasting. The five most frequently used techniques are listed in Table  2 that includes “Neural Network,” “Regression”, “Time-series forecasting (ARIMA)”, “Support Vector Machine”, and “Decision Tree” methods. This table implies the growing use of big data analysis techniques in SC demand forecasting. It shall be mentioned that there were a few articles using multiple of these techniques.

Distribution of literature in supply chain demand forecasting from 2005 to 2019

It shall be mentioned that there are literature review papers exploring the use of big data analytics in SCM [ 10 , 16 , 23 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 ]. However, this study focuses on the specific topic of “demand forecasting” in SCM to explore BDA applications in line with this particular subtopic in SCM.

As Hofmann and Rutschmann [ 58 ] indicated in their literature review, the key questions to answer are why, what and how big data analytics/machine-learning algorithms could enhance forecasts’ accuracy in comparison to conventional statistical forecasting approaches.

Conventional methods have faced a number of limitations for demand forecasting in the context of SCs. There are a lot of parameters influencing the demand in supply chains, however, many of them were not captured in studies using conventional methods for the sake of simplicity. In this regard, the forecasts could only provide a partial understanding of demand variations in supply chains. In addition, the unexplained demand variations could be simply considered as statistical noise. Conventional approaches could provide shorter processing times in exchange for a compromise on robustness and accuracy of predictions. Conventional SC demand forecasting approaches are mostly done manually with high reliance on the planner’s skills and domain knowledge. It would be worthwhile to fully automate the forecasting process to reduce such a dependency [ 58 ]. Finally, data-driven techniques could learn to incorporate non-linear behaviors and could thus provide better approximations in demand forecasting compared to conventional methods that are mostly derived based on linear models. There is a significant level of non-linearity in demand behavior in SC particularly due to competition among suppliers, the bullwhip effect, and mismatch between supply and demand [ 40 ].

To extract valuable knowledge from a vast amount of data, BDA is used as an advanced analytics technique to obtain the data needed for decision-making. Reduced operational costs, improved SC agility, and increased customer satisfaction are mentioned among the benefits of applying BDA in SCM [ 68 ]. Researchers used various BDA techniques and algorithms in SCM context, such as classification, scenario analysis, and optimization [ 23 ]. Machine-learning techniques have been used to forecast demand in SCs, subject to uncertainties in prices, markets, competitors, and customer behaviors, in order to manage SCs in a more efficient and profitable manner [ 40 ].

BDA has been applied in all stages of supply chains, including procurement, warehousing, logistics/transportation, manufacturing, and sales management. BDA consists of descriptive analytics, predictive analytics, and prescriptive analytics. Descriptive analysis is defined as describing and categorizing what happened in the past. Predictive analytics are used to predict future events and discover predictive patterns within data by using mathematical algorithms such as data mining, web mining, and text mining. Prescriptive analytics apply data and mathematical algorithms for decision-making. Multi-criteria decision-making, optimization, and simulation are among the prescriptive analytics tools that help to improve the accuracy of forecasting [ 10 ].

Predictive analytics are the ones mostly utilized in SC demand and procurement forecasting [ 23 ]. In this sense, in the following subsections, we will review various predictive big data analytics approaches, presented in the literature for demand forecasting in SCM, categorized based on the employed data analytics/machine learning technique/algorithm, with elaborations of their purpose and applications (summarized in Table  3 ).

Time-series forecasting

Time series are methodologies for mining complex and sequential data types. In time-series data, sequence data, consisting of long sequences of numeric data, recorded at equal time intervals (e.g., per minute, per hour, or per day). Many natural and human-made processes, such as stock markets, medical diagnosis, or natural phenomenon, can generate time-series data. [ 48 ].

In case of demand forecasting using time-series, demand is recorded over time at equal size intervals [ 69 , 70 ]. Combinations of time-series methods with product or market features have attracted much attention in demand forecasting with BDA. Ma et al. [ 71 ] proposed and developed a demand trend-mining algorithm for predictive life cycle design. In their method, they combined three models (a) a decision tree model for large-scale historical data classification, (b) a discrete choice analysis for present and past demand modeling, and (c) an automated time-series forecasting model for future trend analysis. They tested and applied their 3-level approach in smartphone design, manufacturing and remanufacturing.

Time-series approach was used for forecasting of search traffic (service demand) subject to changes in consumer attitudes [ 37 ]. Demand forecasting has been achieved through time-series models using exponential smoothing with covariates (ESCov) to provide predictions for short-term, mid-term, and long-term demand trends in the chemical industry SCs [ 7 ]. In addition, Hamiche et al. [ 72 ] used a customer-responsive time-series approach for SC demand forecasting.

In case of perishable products, with short life cycles, having appropriate (short-term) forecasting is extremely critical. Da Veiga et al. [ 73 ] forecasted the demand for a group of perishable dairy products using Autoregressive Integrated Moving Average (ARIMA) and Holt-Winters (HW) models. The results were compared based on mean absolute percentage error (MAPE) and Theil inequality index (U-Theil). The HW model showed a better goodness-of-fit based on both performance metrics.

In case of ARIMA, the accuracy of predictions could diminish where there exists a high level of uncertainty in future patterns of parameters [ 42 , 74 , 75 , 76 ]. HW model forecasting can yield better accuracy in comparison to ARIMA [ 73 ]. HW is simple and easy to use. However, data horizon could not be larger than a seasonal cycle; otherwise, the accuracy of forecasts will decrease sharply. This is due to the fact that inputs of an HW model are themselves predicted values subject to longer-term potential inaccuracies and uncertainties [ 45 , 73 ].

Clustering analysis

Clustering analysis is a data analysis approach that partitions a group of data objects into subgroups based on their similarities. Several applications of clustering analysis has been reported in business analytics, pattern recognition, and web development [ 48 ]. Han et al. [ 48 ] have emphasized the fact that using clustering customers can be organized into groups (clusters), such that customers within a group present similar characteristic.

A key target of demand forecasting is to identify demand behavior of customers. Extraction of similar behavior from historical data leads to recognition of customer clusters or segments. Clustering algorithms such as K-means, self-organizing maps (SOMs), and fuzzy clustering have been used to segment similar customers with respect to their behavior. The clustering enhances the accuracy of SC demand forecasting as the predictions are established for each segment comprised of similar customers. As a limitation, the clustering methods have the tendency to identify the customers, that do not follow a pattern, as outliers [ 74 , 77 ].

Hierarchical forecasts of sales data are performed by clustering and categorization of sales patterns. Multivariate ARIMA models have been used in demand forecasting based on point-of-sales data in industrial bakery chains [ 19 ]. These bakery goods are ordered and clustered daily with a continuous need to demand forecasts in order to avoid both shortage or waste [ 19 ]. Fuel demand forecasting in thermal power plants is another domain with applications of clustering methods. Electricity consumption patterns are derived using a clustering of consumers, and on that basis, demand for the required fuel is established [ 77 ].

K-nearest-neighbor (KNN)

KNN is a method of classification that has been widely used for pattern recognition. KNN algorithm identifies the similarity of a given object to the surrounding objects (called tuples) by generating a similarity index. These tuples are described by n attributes. Thus, each tuple corresponds to a point in an n-dimensional space. The KNN algorithm searches for k tuples that are closest to a given tuple [ 48 ]. These similarity-based classifications will lead to formation of clusters containing similar objects. KNN can also be integrated into regression analysis problems [ 78 ] for dimensionality reduction of the data [ 79 ]. In the realm of demand forecasting in SC, Nikolopoulos et al. [ 80 ] applied KNN for forecasting sporadic demand in an automotive spare parts supply chain. In another study, KNN is used to forecast future trends of demand for Walmart’s supply chain planning [ 81 ].

Artificial neural networks

In artificial neural networks, a set of neurons (input/output units) are connected to one another in different layers in order to establish mapping of the inputs to outputs by finding the underlying correlations between them. The configuration of such networks could become a complex problem, due to a high number of layers and neurons, as well as variability of their types (linear or nonlinear), which needs to follow a data-driven learning process to be established. In doing so, each unit (neuron) will correspond to a weight, that is tuned through a training step [ 48 ]. At the end, a weighted network with minimum number of neurons, that could map the inputs to outputs with a minimum fitting error (deviation), is identified.

As the literature reveals, artificial neural networks (ANN) are widely applied for demand forecasting [ 82 , 83 , 84 , 85 ]. To improve the accuracy of ANN-based demand predictions, Liu et al. [ 86 ] proposed a combination of a grey model and a stacked auto encoder applied to a case study of predicting demand in a Brazilian logistics company subject to transportation disruption [ 87 ]. Amirkolaii et al. [ 88 ] applied neural networks in forecasting spare parts demand to minimize supply chain shortages. In this case of spare parts supply chain, although there were multiple suppliers to satisfy demand for a variety of spare parts, the demand was subject to high variability due to a varying number of customers and their varying needs. Their proposed ANN-based forecasting approach included (1) 1 input demand feature with 1 Stock-Keeping Unit (SKU), (2) 1 input demand feature with all SKUs, (3) 16 input demand features with 1 SKU, and (4) 16 input demand features with all SKUs. They applied neural networks with back propagation and compared the results with a number of benchmarks reporting a Mean Square Error (MSE) for each configuration scenario.

Huang et al. [ 89 ] compared a backpropagation (BP) neural network and a linear regression analysis for forecasting of e-logistics demand in urban and rural areas in China using data from 1997 to 2015. By comparing mean absolute error (MAE) and the average relative errors of backpropagation neural network and linear regression, they showed that backpropagation neural networks could reach higher accuracy (reflecting lower differences between predicted and actual data). This is due to the fact that a Sigmoid function was used as the transfer function in the hidden layer of BP, which is differentiable for nonlinear problems such as the one presented in their case study, whereas the linear regression works well with linear problems.

ANNs have also been applied in demand forecasting for server models with one-week demand prediction ahead of order arrivals. In this regard, Saha et al. [ 90 ] proposed an ANN-based forecasting model using a 52-week time-series data fitted through both BP and Radial Basis Function (RBF) networks. A RBF network is similar to a BP network except for the activation/transfer function in RBF that follows a feed-forward process using a radial basis function. RBF results in faster training and convergence to ANN weights in comparison with BP networks without compromising the forecasting precision.

Researchers have combined ANN-based machine-learning algorithms with optimization models to draw optimal courses of actions, strategies, or decisions for future. Chang et al. [ 91 ] employed a genetic algorithm in the training phase of a neural network using sales/supply chain data in the printed circuit board industry in Taiwan and presented an evolving neural network-forecasting model. They proposed use of a Genetic Algorithms (GA)-based cost function optimization to arrive at the best configuration of the corresponding neural network for sales forecast with respect to prediction precision. The proposed model was then compared to back-propagation and linear regression approaches using three performance indices of MAPE, Mean Absolute Deviation (MAD), and Total Cost Deviation (TCD), presenting its superior prediction precision.

Regression analysis

Regression models are used to generate continuous-valued functions utilized for prediction. These methods are used to predict the value of a response (dependent) variable with respect to one or more predictor (independent) variables. There are various forms of regression analysis, such as linear, multiple, weighted, symbolic (random), polynomial, nonparametric, and robust. The latter approach is useful when errors fail to satisfy normalcy conditions or when we deal with big data that could contain significant number of outliers [ 48 ].

Merkuryeva et al. [ 92 ] analyzed three prediction approaches for demand forecasting in the pharmaceutical industry: a simple moving average model, multiple linear regressions, and a symbolic regression with searches conducted through an evolutionary genetic programming. In this experiment, symbolic regression exhibited the best fit with the lowest error.

As perishable products must be sold due to a very short preservation time, demand forecasting for this type of products has drawn increasing attention. Yang and Sutrisno [ 93 ] applied and compared regression analysis and neural network techniques to derive demand forecasts for perishable goods. They concluded that accurate daily forecasts are achievable with knowledge of sales numbers in the first few hours of the day using either of the above methods.

Support vector machine (SVM)

SVM is an algorithm that uses a nonlinear mapping to transform a set of training data into a higher dimension (data classes). SVM searches for an optimal separating hyper-plane that can separate the resulting class from another) [ 48 ]. Villegas et al. [ 94 ] tested the applicability of SVMs for demand forecasting in household and personal care SCs with a dataset comprised of 229 weekly demand series in the UK. Wu [ 95 ] applied an SVM, using a particle swarm optimization (PSO) to search for the best separating hyper-plane, classifying the data related to car sales and forecasting the demand in each cluster.

Support vector regression (SVR)

Continuous variable classification problems can be solved by support vector regression (SVR), which is a regression implementation of SVM. The main idea behind SVR regression is the computation of a linear regression function within a high-dimensional feature space. SVR has been applied in financial/cost prediction problems, handwritten digit recognition, and speaker identification, object recognition, etc. [ 48 ].

Guanghui [ 96 ] used the SVR method for SC needs prediction. The use of SVR in demand forecasting can yield a lower mean square error than RBF neural networks due to the fact that the optimization (cost) function in SVR does not consider the points beyond a margin of distance from the training set. Therefore, this method leads to higher forecast accuracy, although, similar to SVM, it is only applicable to a two-class problem (such as normal versus anomaly detection/estimation problems). Sarhani and El Afia [ 97 ] sought to forecast SC demand using SVR and applied Particle swarm optimization (PSO) and GA to optimize SVR parameters. SVR-PSO and SVR-GA approaches were compared with respect to accuracy of predictions using MAPE. The results showed a superior performance by PSO in terms time intensity and MAPE when configuring the SVR parameters.

Mixed approaches

Some works in the literature have used a combination of the aforementioned techniques. In these studies, the data flow into a sequence of algorithms and the outputs of one stage become inputs of the next step. The outputs are explanatory in the form of qualitative and quantitative information with a sequence of useful information extracted out of each algorithm. Examples of such studies include [ 15 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 ].

In more complex supply chains with several points of supply, different warehouses, varied customers, and several products, the demand forecasting becomes a high dimensional problem. To address this issue, Islek and Oguducu [ 100 ] applied a clustering technique, called bipartite graph clustering, to analyze the patterns of sales for different products. Then, they combined a moving average model and a Bayesian belief network approaches to improve the accuracy of demand forecasting for each cluster. Kilimci et al. [ 101 ] developed an intelligent demand forecasting system by applying time-series and regression methods, a support vector regression algorithm, and a deep learning model in a sequence. They dealt with a case involving big amount of data accounting for 155 features over 875 million records. First, they used a principal component analysis for dimension reduction. Then, data clustering was performed. This is followed by demand forecasting for each cluster using a novel decision integration strategy called boosting ensemble. They concluded that the combination of a deep neural network with a boosting strategy yielded the best accuracy, minimizing the prediction error for demand forecasting.

Chen and Lu [ 98 ] combined clustering algorithms of SOM, a growing hierarchical self-organizing mapping (GHSOM), and K-means, with two machine-learning techniques of SVR and extreme learning machine (ELM) in sales forecasting of computers. The authors found that the combination of GHSOM and ELM yielded better accuracy and performance in demand forecasts for their computer retailing case study. Difficulties in forecasting also occur in cases with high product variety. For these types of products in an SC, patterns of sales can be extracted for clustered products. Then, for each cluster, a machine-learning technique, such as SVR, can be employed to further improve the prediction accuracy [ 104 ].

Brentan et al. [ 106 ] used and analyzed various BDA techniques for demand prediction; including support vector machines (SVM), and adaptive neural fuzzy inference systems (ANFIS). They combined the predicted values derived from each machine learning techniques, using a linear regression process to arrive at an average prediction value adopted as the benchmark forecast. The performance (accuracy) of each technique is then analyzed with respect to their mean square root error (RMSE) and MAE values obtained through comparing the target values and the predicted ones.

In summary, Table  3 provides an overview of the recent literature on the application of Predictive BDA in demand forecasting.

Discussions

The data produced in SCs contain a great deal of useful knowledge. Analysis of such massive data can help us to forecast trends of customer behavior, markets, prices, and so on. This can help organizations better adapt to competitive environments. To forecast demand in an SC, with the presences of big data, different predictive BDA algorithms have been used. These algorithms could provide predictive analytics using time-series approaches, auto-regressive methods, and associative forecasting methods [ 10 ]. The demand forecasts from these BDA methods could be integrated with product design attributes as well as with online search traffic mapping to incorporate customer and price information [ 37 , 71 ].

Predictive BDA algorithms

Most of the studies examined, developed and used a certain data-mining algorithm for their case studies. However, there are very few comparative studies available in the literature to provide a benchmark for understanding of the advantages and disadvantages of these methodologies. Additionally, as depicted by Table  3 , there is no clear trend between the choice of the BDA algorithm/method and the application domain or category.

Predictive BDA applicability

Most data-driven models used in the literature consider historical data. Such a backward-looking forecasting ignores the new trends and highs and lows in different economic environments. Also, organizational factors, such as reputation and marketing strategies, as well as internal risks (related to availability of SCM resources), could greatly influence the demand [ 107 ] and thus contribute to inaccuracy of BDA-based demand predictions using historical data. Incorporating existing driving factors outside the historical data, such as economic instability, inflation, and purchasing power, could help adjust the predictions with respect to unseen future scenarios of demand. Combining predictive algorithms with optimization or simulation can equip the models with prescriptive capabilities in response to future scenarios and expectations.

Predictive BDA in closed-loop supply chains (CLSC)

The combination of forward and reverse flow of material in a SC is referred to as a closed-loop supply chain (CLSC). A CLSC is a more complex system than a traditional SC because it consists of the forward and reverse SC simultaneously [ 108 ]. Economic impact, environmental impact, and social responsibility are three significant factors in designing a CLSC network with inclusion of product recycling, remanufacturing, and refurbishment functions. The complexity of a CLSC, compared to a common SC, results from the coordination between backward and forward flows. For example, transportation cost, holding cost, and forecasting demand are challenging issues because of uncertainties in the information flows from the forward chain to the reverse one. In addition, the uncertainties about the rate of returned products and efficiencies of recycling, remanufacturing, and refurbishment functions are some of the main barriers in establishing predictions for the reverse flow [ 5 , 6 , 109 ]. As such, one key finding from this literature survey is that CLSCs particularly deal with the lack of quality data for remanufacturing. Remanufacturing refers to the disassembly of products, cleaning, inspection, storage, reconditioning, replacement, and reassembling. As a result of deficiencies in data, optimal scheduling of remanufacturing functions is cumbersome due to uncertainties in the quality and quantity of used products as well as timing of returns and delivery delays.

IoT-based approaches can overcome the difficulties of collecting data in a CLSC. In an IoT environment, objects are monitored and controlled remotely across existing network infrastructures. This enables more direct integration between the physical world and computer-based systems. The results include improved efficiency, accuracy, and economic benefit across SCs [ 50 , 54 , 110 ].

Radio frequency identification (RFID) is another technology that has become very popular in SCs. RFID can be used for automation of processes in an SC, and it is useful for coordination of forecasts in CLSCs with dispersed points of return and varied quantities and qualities of returned used products [ 10 , 111 , 112 , 113 , 114 ].

Conclusions

The growing need to customer behavior analysis and demand forecasting is deriven by globalization and increasing market competitions as well as the surge in supply chain digitization practices. In this study, we performed a thorough review for applications of predictive big data analytics (BDA) in SC demand forecasting. The survey overviewed the BDA methods applied to supply chain demand forecasting and provided a comparative categorization of them. We collected and analyzed these studies with respect to methods and techniques used in demand prediction. Seven mainstream techniques were identified and studied with their pros and cons. The neural networks and regression analysis are observed as the two mostly employed techniques, among others. The review also pointed to the fact that optimization models or simulation can be used to improve the accuracy of forecasting through formulating and optimizing a cost function for the fitting of the predictions to data.

One key finding from reviewing the existing literature was that there is a very limited research conducted on the applications of BDA in CLSC and reverse logistics. There are key benefits in adopting a data-driven approach for design and management of CLSCs. Due to increasing environmental awareness and incentives from the government, nowadays a vast quantity of returned (used) products are collected, which are of various types and conditions, received and sorted in many collection points. These uncertainties have a direct impact on the cost-efficiency of remanufacturing processes, the final price of the refurbished products and the demand for these products [ 115 ]. As such, design and operation of CLSCs present a case for big data analytics from both supply and demand forecasting perspectives.

Availability of data and materials

The paper presents a review of the literature extracted from main scientific databases without presenting data.

Abbreviations

Adaptive neural fuzzy inference systems

Auto regressive integrated moving average

Artificial neural network

• Big data analytics

Backpropagation

Closed-loop supply chain

Extreme learning machine

Enterprise resource planning

Genetic algorithms

Growing hierarchical self-organizing map

Holt-winters

Internet of things

K-nearest-neighbor

Mean absolute deviation

Mean absolute error

Mean absolute percentage error

Mean square error

Mean square root error

Radial basis function

Particle swarm optimization

Self-organizing maps

Stock-keeping unit

Supply chain analytics

Supply chain

• Supply chain management

Support vector machine

Support vector regression

Total cost deviation

Theil inequality index

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Seyedan, M., Mafakheri, F. Predictive big data analytics for supply chain demand forecasting: methods, applications, and research opportunities. J Big Data 7 , 53 (2020). https://doi.org/10.1186/s40537-020-00329-2

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Artificial intelligence and big data analytics for supply chain resilience: a systematic literature review

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Artificial Intelligence (AI) and Big Data Analytics (BDA) have the potential to significantly improve resilience of supply chains and to facilitate more effective management of supply chain resources. Despite such potential benefits and the increase in popularity of AI and BDA in the context of supply chains, research to date is dispersed into research streams that is largely based on the publication outlet. We curate and synthesise this dispersed knowledge by conducting a systematic literature review of AI and BDA research in supply chain resilience that have been published in the Chartered Association of Business School (CABS) ranked journals between 2011 and 2021. The search strategy resulted in 522 studies, of which 23 were identified as primary papers relevant to this research. The findings advance knowledge by (i) assessing the current state of AI and BDA in supply chain literature, (ii) identifying the phases of supply chain resilience (readiness, response, recovery, adaptability) that AI and BDA have been reported to improve, and (iii) synthesising the reported benefits of AI and BDA in the context of supply chain resilience.

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1 Introduction

Exogenous shocks transcend our previous experiences and have significant impacts, altering the competitive landscape within which businesses operate (Zamani et al., 2022 ). The Covid-19 pandemic has been characterised as such a shock (Wenzel et al., 2021 ) and since its outbreak, it has resulted in significant loss of life. Within the world of business, we have been witnessing a number of negative impacts and business failures, such as layoffs, closures and bankruptcies (Amankwah-Amoah et al., 2020 ). To a large extent, these outcomes were the result of adopting required social distancing measures to minimise the spread of the virus, which negatively impacted the sustainability and profitability of several businesses.

Within the context of operations and supply chains in particular, an important implication of this exogenous shock relates to great uncertainties. In many cases, the latter have been observed due to the pervasive dissemination of false news that has caused further disruption for businesses and everyday life (Verma & Gustafsson, 2020 ), to the tune of leading to what has been termed as an ‘infodemic’, spreading through online and mainstream media (Zarocostas, 2020 ). This infodemic has impacted consumer behaviour, whereby consumers turned to panic buying and hoarding of medical, cleaning and non-perishable supplies, motivated by fear of potential product unavailability (Zwanka & Buff, 2021 ). Not unexpectedly, this abrupt change in consumer behaviour has resulted in turn in supply chain disruptions, too (Kirk & Rifkin, 2020 ), with businesses trying to cope with and forecast demand so as to adjust their supply chain and operations.

From an operations perspective, such disruptions are often considered through the lens of risk management, because ultimately, disruptions are seen as potential risks that need to be anticipated and mitigated against. More precisely, as far as the impacts stemming from such misinformation and media hype are concerned, supply chain professionals are required to manage the risk of potential stock-outs versus stock holding stocks (Jüttner et al., 2003 ). Indeed, recent research has shown that the bullwhip effect created by Covid-19 misinformation resulted quite quickly to inventory excess, stockpiling and critical issues with managing stocks (Kapoor et al., 2021 ).

To address such challenges, businesses traditionally develop business continuity plans alongside risk management strategies to mitigate against disruptions (Azadegan et al., 2020 ). Some typical tangible strategies that safeguard against such risks are the implementation of vendor-managed inventory contractual agreements (Lee, 2016 ) and the configuration of leagile supply chains that increase the performance of the firm in light of uncertainties (Fadaki et al., 2020 ). However, studies have shown that emerging technologies, such as Artificial Intelligence (AI)and Business Data Analytics (BDA) among others, are indispensable towards providing business continuity, especially during exogenous shocks (Papadopoulos et al., 2020 ). Supply chains are today enhanced by sensors and actuators, such as RFIDs, GPS and POS, tags and other smart devices, all of which (continuously) send and receive data (Fosso Wamba et al., 2018 ), thus making the Internet of Things (IoT) a potential avenue for accurate predictions. However, it is technologies like BDA and AI that make such data streams useful and actionable for risk mitigation and for overcoming the challenges of misinformation: insights, for example, from BDA can support the incremental improvement and transformation of the operation model, through accurate and on-time insights regarding the supply network (Roden et al., 2017 ), while AI can be leveraged for developing proactive strategies for predicting the likelihood of risks occurring and their impact (Baryannis et al., 2019 ). As such, emerging technologies like BDA and AI can play a pivotal role in mitigating the negative impacts and support decision-makers in forming appropriate decisions and actions to tackle challenging situations.

To date, there is an ever growing interest with regards to the use and application of AI and BDA for risk management and developing and maintaining resilience in supply chains (e.g., Baryannis et al., 2019 ; Modgil, Singh, et al., 2021; Sanders 2016 ). Despite this interest, however, there are still areas less understood. A recent major review of supply chain resilience focused on research conducted over the past 20 years, detailing the types of disruptions along with their impact on the supply chain and recovery strategies for mitigating these, while technology has been examined at a rather abstract level (Katsaliaki et al., 2021 ). Other studies have focused on identifying and classifying the different AI techniques used for risk management (Baryannis et al., 2019 ; Hamdi et al., 2018 ), and on evaluating different such techniques as part of supply chain resilience (Belhadi, Kamble, Fosso Wamba, et al., 2021). In both cases, scholars seem less focused on how AI contributes towards resilience and along the different phases of risk management (readiness, response, recovery, adaptability). Others have found that AI supports the development of dynamic capabilities, which can in turn facilitate resilience within the firm as far as its supply chain is concerned (Modgil, Singh, et al., 2021). However, such a perspective does not necessarily explain what the exact role of AI and BDA is in supporting resilience beyond supporting organisational dynamic capabilities. Therefore, further research is needed to explore the contribution of technologies such as AI and BDA for building and maintaining resilience in the supply chain. We posit that there is still scope for consolidating existing findings regarding the benefits of BDA and AI in the supply chain resilience (SCR) literature and further exploring the phases of SCR that these technologies can improve in light of significant misinformation and disruption.

The overarching research question that drives this research is: “How do BDA and AI contribute towards supply chain resilience?” To address this question, we specifically examine: “what is the current state of AI and BDA in the SC literature on resilience”, “what phases of SCR (readiness, response, recovery, adaptability) has BDA and AI been shown to improve” and finally, “what are the claimed benefits of BDA and AI in SCR literature”. We adopt a systematic literature review approach that first helps us explore uses and applications of BDA and AI over the last ten years to provide a holistic understanding of the field. Second, this approach helps us explore in more detail what are the claimed benefits of BDA and AI in SCR. By synthesising the findings of prior studies, we identify the exact functions of these technologies that contribute towards resilience in supply chains. In doing so, the paper then focuses on the current challenges that either prohibit or inhibit (externally or internally, respectively) the application and exploitation of BDA and AI for overcoming risks and the misinformation impacts.

2 Resilience in supply chains

Supply chains today operate within an increasingly uncertain and competitive environment, where disruptions can have a significant impact on business performance (Azadegan et al., 2020 ). Such disruptions can be the result of accidents (Stecke & Kumar, 2009 ), natural but also man-made disasters (Elluru et al., 2019 ), including events as for example the 2008 global financial crisis, the UK’s withdrawal from the European Union (Brexit) (Belhadi, Kamble, Fosso Wamba, et al., 2021), loss of critical suppliers (Ponomarov & Holcomb, 2009 ), and many others.

Supply chain systems during the Covid-19 pandemic have been particularly susceptible to disruptions because of the volatile demand, stemming from incomplete and often misleading information circulated, that resulted in misinformation with regards to “procurement, capacity allocation, contracting, scheduling, postponement and demand forecasting” (Gunessee & Subramanian, 2020 , p. 1202). Such misinformation has resulted to negative implications regarding consumer behaviour, and triggered in turn substantial and often difficult to handle fluctuations in demand (Ivanov, 2020 ), thus resulting in increased uncertainty.

To address such disruptions, the literature has highlighted the need to consider resilience of supply chains and to further delve on this concept, rather than restricting the discourse to solely risks (Gunessee & Subramanian, 2020 ). Resilience in general reflects a company’s ability to return to a business-as-usual state with regards to production and services following a major disruption (Rezapour et al., 2017 ). Specifically for supply chains, resilience describes the readiness of an organisation or business to address risks, uncertainty, and generally disruptions that may originate from customers, suppliers or other business processes and supply chain integration mechanisms used (Purvis et al., 2016 ).

Because disruptions can have significant repercussions for both revenues and costs (Ponomarov & Holcomb, 2009 ) and may lead to reputational damages(Elluru et al., 2019 ), to date, the literature has highlighted that overcoming disruptions is of critical importance for businesses. As such, within the operations and supply chain literatures, the concept of resilience is well integrated, as part of preparedness strategies, adopted by businesses for addressing disruptions (Pettit et al., 2019 ). Resilience can be defined as a system’s ability to return to its normal operating capacity within some identified bounds (Ioannidis et al., 2019 ), or to be more specific to supply chain systems, as the supply chain’s adaptive capacity to deal with disruption and quickly resume its previous performance (Belhadi, Kamble, Fosso Wamba, et al., 2021). Ponomarov and Holcomb have formally defined supply chain resilience as “the adaptive capability of the supply chain to prepare for unexpected events, respond to disruption and recover from them by maintaining continuity of operations at the desired level of connectedness and control over structures and function” (Ponomarov & Holcomb, 2009 , p. 131).

Maintaining supply chain resilience allows businesses to ensure a continuous supply for their products (or services) to customers, despite turbulence in the environment. There are different approaches to ensuring supply chain resilience and these can be proactive, reactive or a combination of both, with the view to mitigate potential risks emerging in the environment (Lohmer et al., 2020 ). Existing scholarship suggests that, irrespective of the exact strategy adopted, disruptions in supply chain systems necessitate monitoring and controlling the environment on the one hand, and on the other hand being responsive and flexible in resource orchestration and reconfiguration as and when needed (Ralston & Blackhurst, 2020 ). To achieve this, scholars indicate that what is needed is superior information processing capabilities (Belhadi, Kamble, Fosso Wamba, et al., 2021), particularly because knowledge and information exchange among supply chain partners is considered conducive to risk reduction (Brandon-Jones et al., 2014 ). Indeed, information sharing can minimise ambiguities along the supply chain and increase visibility and performance (Wong et al., 2021 ), especially at times when these are put to the test. Wong et al., ( 2021 ) indicate that having access to accurate information before, during and after disruptions is of paramount importance, and that this can be achieved via real-time information exchange amongst supply chain partners. Such information exchange can support timely decision making and enhance the efficiency of the supply chain system (Li & Lin, 2006 ).

Belhadi et al., ( 2021a , b , c ) note that there has been an increasing interest in building supply chain resilience via technological means, such as the exploitation of advanced technologies, like BDA and AI. Indeed, such emerging technologies can significantly support building supply chain resilience through the lens of accurate and timely data, information, and knowledge exchange among partners. For example, predictive analytics can support the design of disaster-resilient supply chains because it facilitates forecasting, decision making and speedier return to business-as-usual states (Hazen et al., 2018 ). Similarly, AI can be deployed, often as part of an overall Industry 4.0 approach, for supporting adaptation and evolution of smart information systems along the supply chain and as part of operations management (Ralston & Blackhurst, 2020 ).

To date, the literature on the use of AI and BDA for developing supply chain resilience seems to be somewhat fragmented and largely focused on the available computational techniques for supporting different mitigation strategies. However, the rapid evolution of these technologies and the ongoing misinformation-driven disruption of supply chain systems present an opportunity to focus in more detail on what are the exact benefits that these technologies have to offer and what role they can play at the different phases of supply chain resilience efforts, while considering the observed and anticipated challenges regarding their implementation and use. Motivated by the above, the present study adopts a systematic literature review to synthesise prior research on AI, BDA, and supply chain resilience to consolidate existing findings, to inform scholars and practitioners with regards the benefits and challenges of these technologies along each distinct phase of establishing supply chain resilience, and to propose a future research agenda that will shape future work in this field.

The section outlines the systematic literature review (SLR)process adopted in this study. We follow the established guidelines proposed by Tranfield et al., ( 2003 ) which have been used in other SLR studies in varying contexts (e.g., Ahmad et al., 2018 ; Patyal et al., 2021 ; Spanaki et al., 2021 ; Tandon et al., 2020 ). By conducting an evidence-based review, an SLR “identifies key scientific contributions to a field or question, meta-analysis offers a statistical procedure for synthesizing findings in order to obtain overall reliability unavailable from any single study alone” (Tranfield et al., 2003 , p. 209). It is widely accepted that conducting a SLR is a “fundamental scientific activity” (Mulrow, 1994 , p. 597).

The SLR process is illustrated in Fig. 1 and consists of 9 steps across three phases, namely, planning (3 steps), conducting (3 steps), and documenting (3 steps). Each of these three phases and 9 steps are discussed in detail in the remainder of this section.

Protocol for systematic literature review

3.1 Planning the SLR study

This section presents steps 1, 2 and 3 that are related to the planning of this SLR study. The motivation of this study is to classify and synthesise extant literature on supply chain resilience, through thematic analysis of the primary studies. The main objectives of this study (Step 1), as previously stated, are to (i) establish the body of knowledge of supply chain resilience by identifying and categorising extant research on the topic, (ii) identify the most relevant supply chain resilience articles, (iii) synthesise the reported benefits and challenges of AI and BDA in the context of supply chain resilience and (v) identify the opportunities for future research. To achieve these objectives, the research questions (Step 2) listed in Table 1 will be answered.

As RQ1 is a broad research question, three sub questions (RQ1.1 - RQ1.4) have been identified to answer this question, while RQ2 and RQ3 will provide a synthesis of the reported challenges and benefits of AI and BDA in the context of supply chain resilience and disruptions.

We focus specifically on AI and BDA because these two technologies leverage and create opportunities for exploiting the numerous data streams that typically flow through and within a supply chain system. Namely, they can take advantage of the data that currently exist and flow through information systems such as Enterprise Resource Planning (ERP) ones, and which presently “track more data than we can digest”. ERPs can monitor and alert supply chain and operations management for shipping updates, stock levels, demand and supply (Pettit et al., 2019 ). While currently these systems can inform managers about the past and the present, if enhanced with BDA and AI capabilities, they can also inform them about potential future states and thus incorporate resilience-oriented concepts. At this point, it is also important to define how we interpret AI and BDA, as currently there are numerous definitions in the literature (Collins et al., 2021 ). We understand AI as “the ability of a system to identify, interpret, make inferences, and learn from data to achieve predetermined organizational and societal goals” (Mikalef & Gupta, 2021 ). Similarly, we approach BDA as the portfolio of technologies, techniques, and organisational resources that allow a company to analyse large-scale and complex datasets, so as to improve their performance and develop actionable insights from big data (Mikalef et al., 2018 ).

3.2 Conducting the SLR study

This section presents steps 4, 5 and 6 that are related to conducting this SLR study. The search string (Step 4) was developed based on the scope of this study. Keyword combinations (e.g., Artificial intelligence, big data analytics, supply chain resilience) that were used in previous SLR studies (i.e., Baryannis et al., 2019 ; Hosseini & Ivanov, 2019 ; Ngai et al., 2014 ; Sharma et al., 2020 ) in this field were used for searching the databases. Forward and backward citation review was also conducted to ensure we accumulated a relatively complete census of relevant literature (Webster & Watson, 2002 ). A search of the Scopus database retrieved 522 articles. We choose the Scopus database as it is the most extensive database for engineering and management focused academic journal articles. Further, it provides various fields on which the user can search for research papers (Grover & Kar, 2017 ).

Screening of the retrieved publications (Step 5) was achieved by following the best practices proposed by Tranfield et al., ( 2003 ) and Watson and Webster ( 2002 ). We focused exclusively on studies published in CABS ranked journals as it a common practice within the broader field of Business and Management (Hiebl, 2021 ). In addition, most typically, studies published in CABS ranked journals undergo a more rigorous review process. One author screened the articles to identify non-CABS ranked journals. The process resulted in 202 articles being excluded. Two authors then independently screened the articles to remove (i) duplicates (ii) non-English articles, (iii) non-peer reviewed scientific papers, and (iv) full articles not available. The process resulted in 241 articles being excluded. Next, two authors independently reviewed articles and excluded articles not explicitly focused on AI or BDA in the context of supply chain resilience. This process resulted in 56 articles being excluded as they refer to AI and BDA in the article but do not explicitly study these technologies. At the end of the process, 23 primary studies were identified (Step 6). The primary studies are listed in Appendix A.

3.3 Documenting the SLR study

This section presents steps 7, 8 and 9 that are related to the planning of this SLR study. Once the primary studies were identified, they were subject to in-depth analysis (Step 7) by three authors to mitigate validity threats due to researcher bias. Further, to mitigate this threat, data and researcher triangulation was established. The primary studies were analysed based on the research questions of this SLR study. The findings were then synthesised (Step 8) and written up as per the aims of this SLR study. Finally,the first author reviewed each activity (Step 7 and 8) to ensure consistency in the analysis of data, consolidation of the findings, and an evidence-based review SLR was written up.

4 Synthesis of results

This section presents the results from the analysis of the 23 primary studies, which is based on the research questions previously mentioned. The results represent the current state of AI and BDA research in the context of supply chain resilience between 2011 and 2021. We address our first research question: ‘What is the current state of AI and BDA in SC literature between 2011–2021?, by examining the following: (i) publication by year, (ii) publication outlets, (iii) research methodology adopted, (v) data collection techniques, and reported benefits, and (ix) reported challenges.

4.1 RQ 1.1 What number of academic studies on AI and BDA for SCR have been published between 2011 and 2021?

The aim of this research question is to establish the annual number of academic studies on AI and BDA within the context of supply chain resilience between 2011 and 2021. Figure 2 shows the number of publications by year of the primary studies over the 10-year period. This timeline is valuable as it indicates that academic studies on AI and BDA in the context of supply chain resilience were not published in CABS ranked journals between 2011 and 2015. Since 2016 there is an accumulative increase (see amber scale) in publications. In this context there is a significant number of publications (13) in 2021. The 23 primary studies were published between 2016 and 2021 which are based on the inclusion and exclusion criteria used in this study.

Publication period

4.2 RQ 1.2 What SC industries has AI and BDA research been applied to?

The aim of this research question is to identify the supply chain industries in which AI and BDA research have been applied to. Figure 3 shows that most primary studies (13) applied AI and BDA to a mix of supply chain industries, followed by 8 primary studies that applied these technologies to manufacturing. By mix, we refer to supply chain industries whereby studies did not focus on a particular industry (e.g., automotive sector, following a case study approach or purposeful sampling of companies from the sector) but rather investigated these technologies across industries (e.g., automotive, manufacturing, agricultural and others). One study applied these technologies to humanitarian aid supply chains, and one to agricultural supply chains.

Supply Chain industries

4.3 RQ 1.3 What journals are publishing AI and BDA related research in the context of the SC resilience & interruptions?

The aim of this research question is to identify what CABS ranked journals are publishing AI and BDA studies in the context of supply chain resilience and interruptions. Figure 4 shows that The International Journal of Logistics Management published 4 such primary studies, followed by four journals that each published 2 primary studies, and the eleven other journals each published one primary study.

Publication Outlets

4.4 RQ 1.4 What research methods and data collection techniques have been used in AI and BDA studies that focus on SCR?

The aim of this research question is to categorize the research method and data collection techniques that have been used to study AI and BDA in the context of supply chain resilience. Figure 5 shows that quantitative methods (19) are the most popular, followed by qualitative (3) and mixed methods (1).

A deeper analysis of the methods used in the 23 primary studies was conducted to establish the data collection techniques used in the respective studies. Figure 6 shows that surveys (13) are the most popular techniques, where the data collection instrument is typically a questionnaire. Other data research designs have been used, but to a much lesser degree, namely semi structured interviews with experts (3), case studies (1), experiments (3), and modelling (3). We note that modelling and experiments often refer to simulation-based studies.

Methodological Approaches

Research Methods

4.5 RQ 2 What phases of SCR (readiness, response, recovery, adaptability) has AI and BDA been shown to improve?

Our systematic review revealed several benefits of BDA and AI towards supply chain resilience, which, in this section, we present them by organising them across the phases of supply chain resilience. Existing studies indicate that supply chain resilience draws from four separate phases: readiness, response, recovery and adaptability strategies (Chowdhury & Quaddus, 2016 ; Ponomarov & Holcomb, 2009 ). The readiness phase reflects a business’ anticipation of and preparedness for disruptive events (Fahimnia & Jabbarzadeh, 2016 ). Readiness involves identifying and observing any changes happening within the micro- and macro-environment of the business (Maitlis & Sonenshein, 2010 ). Responsiveness indicates how a business enacts its preconceived mitigation strategies when experiencing disruptions (Stone & Rahimifard, 2018 ). The recovery phase entails repairing losses and returning to business-as-usual (Brandon-Jones et al., 2014 ) or moving to a future desired state as soon as possible (Fahimnia & Jabbarzadeh, 2016 ). Further extending the scope of supply chain resilience, Hohenstein et al., ( 2015 ) propose that there is an additional phase, that of growth, whereby businesses proceed with adapting and adjusting their operations and strategies on the basis of their experience during disruptions in order to prepare for future potential disruptions. We refer to this as the adaptation phase, following the approach espoused by Dennehy et al., ( 2021 ).

BDA and AI have proven to support and shape all supply chain resilience phases. Table 2 provides a classification of existing studies across the four resilience phases. As shown, BDA and AI are emerging technologies that contribute towards all the phases of supply chain resilience, including the adaptation phase. Equally, it is shown that BDA is far more explored for its ability to support supply chain resilience comparatively to AI, and that AI for shaping adaptive strategies is somewhat underexplored.

With regards to the readiness phase , Dubey et al., ( 2021a , b ) consider BDA as part of the dynamic capabilities of a business, which can minimise disruptions and particularly under volatile conditions, such as the Covid-19 pandemic. Extending these findings, Dennehy et al., ( 2021 ) highlight that the use of BDA as part of supply chain resilience supports the business to anticipate disruptions, specifically because this technology allows decision makers to sense and forecast such events, as well with tracking and monitoring activities within the context of their operations. Such findings directly explain why BDA and AI can help businesses mitigate misinformation along the supply chain within the context of the readiness phase. These technologies are in position to support businesses in anticipating disruptions, rather than remaining idle until these happen, by directly enabling the monitoring of any deviations from the business-as-usual state in the environment, and the recognition of early warning signals (Zouari et al., 2020 ) by leveraging accurate and real-time big data (Belhadi, Kamble, Jabbour, et al., 2021 ). With regards to this and for AI in particular, supervised machine learning techniques allow the removal of generalisations and noise and the analysis of historical data so as to arrive to better outcomes, grounded on the data (Cavalcante et al., 2019 ). In addition, as shown by Bag et al., ( 2021 ), descriptive, predictive and prescriptive analytics can support tracing suppliers’ performance in real time, which effectively contributes towards supply chain resilience as managers can sense disruptive events earlier on. The existing literature indicates similar benefits using AI with regards to readiness. Namely, Modgil et al., ( 2021a , b ) have found that the use of AI supports sensing multidimensional and multi-layered risks in the environment, and that, especially during Covid-19, AI is being used as part of the daily routine to analyse and sense risks.

A critical phase for supply chain resilience is that of the response phase . As Modgil et al., ( 2021a , b ) find, for a supply chain to be capable to respond in light of disruptive events, the business needs to exhibit the appropriate information processing capabilities, that will help align suppliers, retailers and distributors. The authors also found that, despite the misinformation and fake news that resulted in great fluctuations in demand during Covid-19, AI can be employed to cluster consumer behaviour and therefore address such demands more effectively. Despite delays and the shortages in materials, AI has been able to align stakeholders by enriching their information processing capabilities and supporting the rearrangement of distribution channels in a smart and effective way (Modgil, Gupta, Modgil et al., 2021a , b ). Similar findings have been reported by Dennehy et al., ( 2021 ), with BDA facilitating resource reconfiguration in light of environmental changes and, most importantly, doing so in a timely fashion. In other words, emerging technologies such as AI and BDA can support the development of response mechanisms that can mitigate disruptions. This is because these technologies directly contribute towards developing dynamic capabilities that progressively become institutionalised, and then shift into risk resilience capabilities, that enable firms to restructure and reconfigure their resources, if and when needed (Singh & Singh, 2019 ).

As far as the recovery phase is concerned, several studies indicate the value of AI (e.g., Belhadi, Mani, Kamble, et al., 2021 ; Modgil, Singh, et al., 2021, 2021) and BDA (e.g., Dennehy et al., 2021 ; Ivanov, 2017 ; Khan et al., 2021 ; Sheng & Saide, 2021 ). Work in this area illustrates how these technologies facilitate recovery during and after disruptions by enabling firms to rebuild their supply chain operations, reconnect potentially fragmented supply chain components and coordinate recovery plans (Ivanov et al., 2019 ). For example, BDA and AI, coupled with other digital technologies, can speed up the execution of recovery plans to allow the timely mitigation of misinformation and disruption impacts, thus halting further propagation of the negative effects (Ivanov et al., 2019 ). In more detail, some of the benefits of AI and BDA for the recovery phase relate to the last mile delivery, whereby predictive analytics can help with managing disruptions in the workforce, and adopting paperless working patterns to create efficiencies across the supply chain (Modgil, Singh, et al., 2021). However, Rajesh ( 2016 ) has further found that it is not sufficient to use big data or more data for decision making during the recovery phase, but that instead what is needed is the use of the right data indicators, which will ensure accuracy and exactness in executing the recovery. Similarly, Cavalcante et al., ( 2019 ) have further emphasised the need for accurate data as well as the use of AI techniques that reduce potential abstractions from the datasets, and evaluated this approach through simulation and machine learning models, whereby their results indicate that decision making that is based on these approaches can enhance supplier selection when attempting to restore operations to the business-as-usual state, because managers can predict supplier performance following the disruption (Cavalcante et al., 2019 ) and achieve stabilisation and supply continuity (Dolgui et al., 2018 ).

The adaptive phase represents the firm’s efforts towards developing capabilities for dealing with current but also future disruptions: knowledge and experience are extracted from the firm’s current response and then become institutionalised within the business, thus informing future responses (Singh & Singh, 2019 ). Along these lines, using AI techniques has a significant positive impact on the firm’s adaptive capabilities because these techniques allow the firm to learn from the external environment and to reduce the complexity of highly complex systems, and thus facilitate resilience (Belhadi, Mani, Kamble, et al., 2021 ). In addition, AI can contribute and inform the firm’s restorative capacity, directly influencing the supply chain’s recovery (Cavalcante et al., 2019 ; Modgil, Singh, et al., 2021). Specifically, like in earlier phases, BDA and AI indicate ways for optimised resource allocation, especially when resources are scant (Ivanov et al., 2019 ), which is essential and part of the business’ core capabilities for resilient supply chains (Dennehy et al., 2021 ).Dennehy et al. ( 2021 ) in particular have found that BDA supports supply chains, not only to become flexible and adaptable following the recovery phase, but also that this technology can support moving even to a more desirable stage after the disruption. Modgil et al., ( 2021a , b ) argue that, AI can equally support the adaptation of the supply chain following disruption, because it facilitates design thinking when human and non-human entities are involved in highly complex systems, and supporting managers and decision makers to learn directly from AI insights. Such insights may refer to identifying whether there is a gap between the existing information processing capabilities and the information that needs to be processed in the future to respond to disruptions, and how such a gap can be bridged. Further, BDA and AI, can support the adaptive phase by identifying the more vulnerable parts of the supply chain, recognising the required stock levels of (strategic) resources, and developing pathways for responding to future events, building on advance techniques such as simulations and gaming (Zouari et al., 2020 ).

As shown in Table 2 , several of the analysed studies exhibit an overlap with regards to the benefits stemming from AI and BDA. Specifically, in their majority, studies identify and discuss the benefits of AI and BDA for most if not all of the SCR phases. For example, Dennehy et al., ( 2021 ) indicate that BDA can support SCR with regards to all four phases (readiness, response, recovery and adaptation). With regards to AI, the examined studies show a similar pattern; yet, in most cases scholars identify benefits for three out of the four phases. For instance, Dubey et al., ( 2021a , b ) indicate that AI may contribute towards readiness, response and adaptation, but do not discuss the recovery phase, whereas Cavalcante et al., ( 2019 ) explore benefits during the phases of response, recovery and adaptation but not readiness. The chart in Fig. 7 visualises the aforementioned overlap of benefits across SCR phases for the two technologies. It also clearly illustrates that to date, far more studies focus on BDA, and that AI focused studies in their majority explore and consider benefits for recovery.

Spider chart comparing the number of studies across the four phases of SCR for AI and BDA

4.6 RQ 3 What are the claimed benefits of AI & BDA in SCR literature?

Across the collected studies, the main benefits identified with regards to supply chain resilience are shown in Fig. 8 . In Table 3 , we summarise the identified benefits of AI and BDA for SCR. Our analysis revealed that most of the studies identify BDA and AI as supportive of improving visibility and transparency across the supply chain and enabling effective decision-making.

With regards to improving visibility and transparency , Zouari et al., ( 2020 ) have found that the digitalisation of the supply chain, as facilitated by the use of BDA and AI, supports visibility along the supply chain because these technologies contribute towards gathering critical knowledge with respect to environment and the status of operating assets. Similarly, Belhadi et al., ( 2021a , b , c ) have shown that BDA in particular, enhance transparency, which in turn supports supply chain resilience, because visibility combined with transparency enable quick buy-in and commitment of those involved in tackling disruptions, and in turn make recovery speedier and better. Such commitment and buy-in essentially relates to effective decision-making , i.e., the second most frequently cited benefit of BDA and AI. Indeed, the study by Dennehy et al., ( 2021 ) revealed that during crises, BDA, as a technological capability, supports supply chain professionals generate insights and intelligence, which can then empower top management to make decisions informed by data, rather than solely on the basis of experience and/or intuition. Examples of such decision-making include choices regarding the ideal location of facilities and specifically for times of crisis, regarding optimal prediction, distribution and inventory levels, when such decisions are modelled on the basis of big data (Mishra & Singh, 2020 ).

Frequency of reported benefits across studies

Another major benefit of BDA and AI lies with improving supply chain responsiveness . Responsiveness denotes how the supply chain responds to the demand, for example by adjusting production, modifying operations for both exploiting opportunities and addressing challenges, as well as at the human resource level, by organising and coordinating key personnel to do so (Zouari et al., 2020 ). Focusing on BDA specifically, Rajesh ( 2016 ) approach such responsiveness as corresponding to the speed with which a business addresses customer needs during disruptions and which can be measured as a function of on-time delivery ratio, the contract issue time, the contract approval time and the put-away ratio. The role of BDA in this respect relates to facilitating information sharing among supply chain partners and which results in managing and reducing risks. Considering BDA in combination to AI, studies have shown that responsiveness during crises can only be supported if these technologies are well integrated and interoperable (Frederico et al., 2021 ; Nayal et al., 2021 ). This highlights the importance of the quality, as well as the nature and form of the data used, that will enable such interoperability, and therefore real time information sharing, and thus extracting and responding to generated insights within highly complex environments (Nayal et al., 2021 ).

We found fewer studies investigating the use of BDA and AI for identifying the possible sources of disruption which are however quite illustrative regarding the role of these technologies for identifying risks ahead of and during disruptive events. Dubey et al., ( 2021a , b ), for example, showcase the role of BDA for supporting managers to identify the possible threats, by making visible the vulnerabilities in the supply chain, thereby developing more accurate and relevant business continuity plans. Namely, BDA can be used for identifying risks stemming from the suppliers, but also as a medium for assessing their probability and their impact for operations. In addition, they can be used for identifying bottlenecks and insights for rescheduling tasks and events if and when needed (Cavalcante et al., 2019 ). Others have indicated ways for leveraging information stemming from social media in order to identify emerging risks. For example, Janjua et al., ( 2021 ) have developed a framework that draws information from social media (pertaining to e.g., natural disasters and labour disputes), which they then analyse using AI techniques to analyse threats, capture approximate location of said threats and their timing.

As a result, leveraging AI and BDA for identifying the source of disruption can also speed up the recovery of operations (Singh, 2020 ). Analytics and big data generally facilitate shorter order-to-delivery cycle times and the crafting of demand-driven operations. (Rajesh, 2016 ). In more detail, it is argued that BDA informs the planning, coordination, and control activities of firms during disruptive events, as part of their preparedness, alertness and agility. On the basis of these, firms can develop IT infrastructure capabilities that enable them to support quicker reactions during disruptions (Singh, 2020 ) because this technology, applied in the aforementioned ways, facilitates reduced lead times and the acquiring and processing of reliable information (Dubey, Gunasekaran, et al., 2021 ). In addition, the above approach is applicable even when extending the above methods to incorporate the suppliers’ side, because firms are then able to identify disruptive events impacting their suppliers, and quickly adapt to unpredictable changes within the greater supply chain environment (Sheng & Saide, 2021 ).

How and why BDA and AI help identify the source of disruptions and speed up recovery is addressed by few studies, whereby it is suggested that they provide superior insights regarding threats and improve information processing and quality . For example, it has been found that insights stemming from the processing of large datasets using BDA techniques can improve operation performance especially when such data come from multiple sources (Dubey, Gunasekaran, et al., 2021 ). For instance, BDA insights can inform resource orchestration and allocation, and thus facilitate grounded-on-the-data decision making (Dennehy et al., 2021 ). It is also argued that research and practice need to go beyond than simply leveraging BDA to develop ‘hard to imitate’ capabilities, but instead that other resources are also required, whereby firms exploit BDA in order to improve the quality of information flowing through their supply chain and operations (Singh & Singh, 2019 ). Indeed, BDA may influence supply chain resilience because it improves the quality of information (Bahrami & Shokouhyar, 2021 ) and empowers a strong IT infrastructure (e.g., cyberphysical systems, RFID technology, Industry 4.0 sensors), which in turn enables the collection of precise information with regards to operations and processes, which is more accurate and visually more legible, as data is captured automatically at the source and communicated in real time via graphs and charts (Ivanov et al., 2019 ). These then directly support quicker decision-making because they positively impact the organisation’s information capabilities and, by extension, supply chain resilience, because information planning, coordination and control are its core enablers (Belhadi, Mani, Kamble, et al., 2021 ).

Among the least explored benefits are those of the enhancement of innovative capabilities. The way that BDA and AI influence innovative capabilities is particularly important and interesting because such capabilities relate to improved information processing and quality (Bahrami & Shokouhyar, 2021 ; Belhadi, Mani, Kamble, Belhadi et al., 2021a , b , c ), supply chain visibility and transparency (Belhadi, Mani, Kamble, Belhadi et al., 2021a , b , c ), thereby enabling the development and the actioning of recovery strategies. Under the umbrella of innovative capabilities, scholars identify the firm’s abilities to generate and implement new ideas and insights, processes and products, whereby analytical capabilities are part of these, too (Bahrami & Shokouhyar, 2021 ). Lastly, we identified two studies whereby scholars link BDA and AI to superior and resilient supplier selection. These technologies enable firms to identify and manage suppliers on the basis of insights deriving from spending patterns, service level and penalty data, thereby developing a supply chain ecosystem that can respond to disruptions (Mandal, 2018 ). In addition, machine learning approaches can be particularly useful for supplier selection when combined with simulation techniques to practically examine how decisions regarding suppliers may influence the reliability of the supply chain, thus influencing its performance and overall resilience (Cavalcante et al., 2019 ).

5 Current challenges

Big data analytics and AI can be seen as emerging technologies with the potential to ‘equalise’ the impacts of uncertainty and enable organisations to predict supply and demand despite misinformation (Belhadi, Mani, Kamble, et al., 2021 ; Verma & Gustafsson, 2020 ). For example, Big Data Analytics can be applied for internal and external process sensing (e.g., inefficiencies), for forecasting, scheduling, real-time resource allocation, and for transforming operational inefficiencies, including real-time process reconfiguration (e.g., automated alerts when there is a high risk of failure) (Conboy et al., 2020 ). Other application areas are the inventory, capacity and labour scheduling, and sourcing. Sourcing, in particular, is potentially one of the areas of most concern during disruptions, and Big Data Analytics can support decision makers to measure risks and negotiate with suppliers “by providing factual leverage” (Sanders, 2016 , p. 32). In addition, other benefits include coordination and knowledge sharing across the entire supply chain (Chen et al., 2015 ) as shown from several of the identified studies (e.g., Belhadi, Kamble, Jabbour, et al., 2021 ; Mandal, 2018 ; Rajesh, 2016 ; Singh & Singh, 2019 ). Despite the benefits from AI and BDA in SCR, most companies are facing difficulties owing to the large investments and challenges related to their deployment and integration (Cadden et al., 2021 ), privacy and security issues, lack of appropriate business cases (Dennehy, 2020 ), in-house capabilities (Rajesh, 2016 ), and sustainability (Patyal et al., 2021 ).

5.1 The challenge of identifying appropriate use cases

While AI and BDA can directly influence and impact positively on a company’s supply chain resilience, it is important for the company to identify relevant application areas in order to benefit from these technologies. This can help the company avoid potential issues arising from bandwagon effects due to the adoption of emerging technologies. Since there is a pressure from other organisations that have already adopted those technologies (Abrahamson & Bartner, 1990 ), organisations need to primarily make an assessment of the usefulness of the technological innovations for building SCR in accordance to their requirements (Abrahamson, 1991 ). We consider that the use of BDA can be useful in this respect by helping identify such use cases. Recent studies have shown that BDA can be employed to: support organizations to prioritise and coordinate activities on specific projects (Dennehy et al., 2021 ; Zamani et al., 2021 ), sense and respond to changes in the business environment (Barlette & Baillette, 2022 ; Zamani et al., 2022 ), and create business value (Oesterreich et al., 2022 ; Papadopoulos et al., 2022 ). We thus posit that BDA can be both the instrument that helps identify a problem and be part of the solution that addresses that problem in the context of supply chain resilience.

5.2 The challenge of scarce resources and investments

The funding practices adopted by businesses for the development of BDA contradict the traditional funding model (Dennehy et al., 2021 ). With new ideas and capabilities emerging from the use of BDA and AI, the business models have evolved which in turn demands for new management skills along with the technical competencies (Bahrami & Shokouhyar, 2021 ). The feasibility of adopting BDA by understanding the time to acquire and develop SCR and expected return on investment is a critical component. In fact, there are claims that many organisations have failed in realising the feasibility of BDA to meet supply chain resilience (Mikalef et al., 2020 ; Ross et al., 2013 ). The exploitative capabilities of the organisation (in terms of what, how and when) to harness the potential of BDA is crucial considering the huge investments made to build SCR. Organisations face enormous challenges in extracting and translating the information into decision making in relation to managing supply chain networks and there are many instances where the organisations have failed to yield positive return on investment (Dubey, Bryde, et al., 2021 ).

Further, the use of AI and BDA helps in the integration of key areas like managing supply chain networks and knowledge resources. However, for the effective use of these technologies a robust collaboration with the supply chain networks, knowledge resource capabilities and infrastructure is required (Sheng & Saide, 2021 ). Moreover, if there is a lack of knowledge about the use of real time big data for generating insights and if there are knowledge gaps in the adoption of the AI-based platforms, survivability of SCR may be impacted (Pinto et al., 2019 ). As such, a major challenge is that of the skill sets and expertise required in handling data analytics to address supply chain disruption (Ergun et al., 2009 ).

5.3 The challenge of organisational culture and change

There is a need to understand the importance of the organisational cultural change that is critical to harness the value of big data analytics (Vidgen et al., 2017 ). For instance, digital maturity helps in the adoption of digital supply chains and has a stronger impact on SCR. Developing digital maturities implies improving information sharing and data architectures, formalising processes, training and engaging all employees towards a digital mindset (Zouari et al., 2020 ). However, many companies make plans to implement digitalisation without considering the need to develop and enhance their degree of digital maturity. Additionally, a data-driven culture in firms may give rise to makeshift supplier-customer relationships thus affecting the bargaining power of firms represented by supplier selection predictive models based smart contracts (Cavalcante et al., 2019 ).

5.4 Challenges in relation to the wider ecosystem

The mere development of AI and BDA capabilities is not sufficient to prevent or solve the undefined impacts of exogenous shocks (Lawson et al., 2019 ; Sohrabi et al., 2020 ). Prior to considering AI and BDA for building up SCR, practitioners need to analyse the level of stress the supply chain can absorb and evaluate the degree of reconfiguration for the supply chains to withstand a disruption as well as their capability of analysing changing dynamics. It is essential for all the supply chain partners of a firm, including its lower tier suppliers, to implement a data-driven supply chain (Khan et al., 2021 ) when the firm develops AI and BDA capabilities for building SCR. The supply chain operations in risk management are also benefitted by supply chain resilience arising due to the collaboration among supply chain partners (Yen & Zeng, 2011 ). However, lower tier suppliers may not posses the required technological sophistication and be exposed to incompatible interface standards, legacy systems etc, nor have access to skilled resources required for developing AI and BDA capabilities. Moreover, poor decision-making and unreliable contingencies may arise due to inaccuracy of information and data shared that feed AI algorithms amidst uncertainty in supply chain.

6 Conclusions and future research

This study performed a systematic literature review on the contributions of artificial intelligence and big data analytics in supply chain resilience. We note that our study is not the first systematic literature in the domain of supply chain management with the focus on the use of technologies for addressing disruptions and enabling resilience. For example, Katsaliaki et al., ( 2021 ) conducted a similar investigation and identified some of the most popular modelling techniques and IT tools used for enhancing resilience. However, in our study, we focus specifically on AI and BDA. This allows us to provide a more nuanced understanding specifically with regards to these two technologies’ role towards supply chain resilience, and in turn, delineating the challenges for adopting them as part of supply chain management.

When investigating the thematic area of supply chain disruptions and resilience, this study can serve as a normative reference for the operations and supply chain disciplines. To this end, three broad-based research questions were identified. The first research question explored the current state of AI and BDA in the supply chain literature during the last decade. This research question investigated to which supply chain industries has AI and BDA been applied, which journals are publishing research on AI and BDA with a specific focus on SC resilience and disruptions, and what data collection techniques and research methods have been used in these studies. This research question has been addressed by providing a detailed summary in Sect.4.1 to 4.4 using Figs. 2 , 3 , 4 , 5 and 6 . The second research question was aimed at understanding the phases of supply chain resilience that AI and BDA have improved. This question was addressed by integrating insights derived from the posited advantages of AI and BDA across the readiness, response, recovery, and adaptation phases of supply chain resilience in Sect.4.5 and summarizing them in Table 2 . The third research question sought to reveal the benefits of AI and BDA in supply chain resilience. This research question was answered by delineating and summarizing key themes pertaining to benefits of AI and BDA in SCR in Sect.4.6 using Fig. 8 ; Table 3 .

The findings of this systematic literature review should be considered in light of its methodological limitations. First this review focussed only on articles in CABS ranked journals available in Scopus database. Other databases such as ACM and IEEE could have been included for an exhaustive search of papers. Second, the review considered “artificial intelligence”, “big data analytics” and “Supply chain resilience” as the keywords for querying the database and didn’t consider other related and interchangeably used terms such as “machine learning”, “business intelligence” or “natural language processing” as keywords. These limitations may be addressed in future research to mitigate the shortcomings of relying on a single database and a set of few umbrella keywords. Moreover, future research may inform the security and privacy related aspects of AI and BDA adoption in supply chain, thus aiding in authenticated use of supply chain systems and avoid data breaches.

This study is an endeavour to encapsulate the research conducted by leading researchers and published in top publication outlets in the field of business. The study has several implications including a need for broader coverage of data collection and methodological approaches such as case-study approach, simulation, and mixed methods. For instance, research based on case-study and mixed method approaches can supplement the understanding of barriers in adoption of AI and BDA for improving supply chain resilience. Moreover, amidst an era of unprecedented exogenous shocks to businesses, there is a growing interest in the use of AI and BDA for supporting supply chain resilience by speeding up recovery times, supplier selection, improving supply chain visibility, transparency, responsiveness. We believe that the structured insights of this review will aid academics and practitioners in the field of supply chain management to develop AI and BDA based interventions for supply chain resilience.

Concluding, we underline that the majority of the studies investigated relate to the use of BDA and AI to superior decision making (e.g., Bag et al., 2021 ; Nayal et al., 2021 ; Singh, 2020 ). However, insights from these technologies may not be sufficient. Supply chain disruptions are often characterised by an impetus to make accurate but quick decisions, under complex and difficult conditions. While BDA and AI can help clarify uncertainties and reduce risks by filling in informational gaps, whether, when and how an organisation will move from insights to actions rests with its decision makers, who need to make sense of such insights but often feel more comfortable turning to their intuitive judgement and prior experience to decide on next steps (Constantiou et al., 2019 ; Zamani et al., 2021 ). We would thus like to invite future research in this area, that will delve deeper into the behavioural perspective and decision-making to explore supply chain and operations decision makers’ behaviours towards the use of emerging technologies during disruptions.

Data availability

Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

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All authors contributed equally to the study conception and design. Material preparation, data collection and analysis were performed by E.Z., C.S. and D.D. S.G. developed the manuscript’s conclusions. The first draft of the manuscript was written by all authors and all authors commented on previous versions of the manuscript. D.D. critically revised the manuscript. All authors read and approved the final manuscript.

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Appendix A: Studies included in the systematic literature review

Bag et al., ( 2021 ). How big data analytics can help manufacturing companies strengthen supply chain resilience in the context of the COVID-19 pandemic. The International Journal of Logistics Management , ahead-of-print (ahead-of-print). https://doi.org/10.1108/IJLM-02-2021-0095 .

Bahrami, M., & Shokouhyar, S. (2021). The role of big data analytics capabilities in bolstering supply chain resilience and firm performance: A dynamic capability view. Information Technology & People , ahead-of-print (ahead-of-print). https://doi.org/10.1108/ITP-01-2021-0048 .

Belhadi, A., Kamble, S., Jabbour, C. J. C., Gunasekaran, A., Ndubisi, N. O., & Venkatesh, M. (2021). Manufacturing and service supply chain resilience to the COVID-19 outbreak: Lessons learned from the automobile and airline industries. Technological Forecasting and Social Change , 163 , 120,447. https://doi.org/10.1016/j.techfore.2020.120447 .

Belhadi, A., Mani, V., Kamble, S. S., Khan, S. (A) R., & Verma, S. (2021). Artificial intelligence-driven innovation for enhancing supply chain resilience and performance under the effect of supply chain dynamism: An empirical investigation. Annals of Operations Research . https://doi.org/10.1007/s10479-021-03956-x .

Cavalcante, I. M., Frazzon, E. M., Forcellini, F. A., & Ivanov, D. (2019). A supervised machine learning approach to data-driven simulation of resilient supplier selection in digital manufacturing. International Journal of Information Management , 49 , 86–97. https://doi.org/10.1016/j.ijinfomgt.2019.03.004 .

Dennehy et al., ( 2021 ). Supply chain resilience in mindful humanitarian aid organizations: The role of big data analytics. International Journal of Operations & Production Management , 41 (9), 1417–1441. https://doi.org/10.1108/IJOPM-12-2020-0871 .

Dubey, R., Bryde, D. J., Blome, C., Roubaud, D., & Giannakis, M. (2021). Facilitating artificial intelligence powered supply chain analytics through alliance management during the pandemic crises in the B2B context. Industrial Marketing Management , 96 , 135–146. https://doi.org/10.1016/j.indmarman.2021.05.003 .

Dubey, R., Gunasekaran, A., Childe, S. J., Fosso Wamba, S., Roubaud, D., & Foropon, C. (2021). Empirical investigation of data analytics capability and organizational flexibility as complements to supply chain resilience. International Journal of Production Research , 59 (1), 110–128. https://doi.org/10.1080/00207543.2019.1582820 .

Frederico, G. F., Kumar, V., Garza-Reyes, J. A., Kumar, A., & Agrawal, R. (2021). Impact of I4.0 technologies and their interoperability on performance: Future pathways for supply chain resilience post-COVID-19. The International Journal of Logistics Management , ahead-of-print (ahead-of-print). https://doi.org/10.1108/IJLM-03-2021-0181 .

Ivanov ( 2017 ). Simulation-based single vs. Dual sourcing analysis in the supply chain with consideration of capacity disruptions, big data and demand patterns. International Journal of Integrated Supply Management , 11 (1), 24–43. https://doi.org/10.1504/IJISM.2017.083005 .

Ivanov, D., Dolgui, A., & Sokolov, (B) ( 2019 ). The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics. International Journal of Production Research , 57 (3), 829–846. https://doi.org/10.1080/00207543.2018.1488086 .

Janjua, N. K., Nawaz, F., & Prior, D. D. (2021). A fuzzy supply chain risk assessment approach using real-time disruption event data from Twitter. Enterprise Information Systems , 0 (0), 1–22. https://doi.org/10.1080/17517575.2021.1959652 .

Khan, S. A. R., Yu, Z., Umar, M., Lopes de Sousa Jabbour, (A) B., & Mor, R. S. (2021). Tackling post-pandemic challenges with digital technologies: An empirical study. Journal of Enterprise Information Management , ahead-of-print (ahead-of-print). https://doi.org/10.1108/JEIM-01-2021-0040 .

Mandal ( 2018 ). The influence of big data analytics management capabilities on supply chain preparedness, alertness and agility: An empirical investigation. Information Technology & People , 32 (2), 297–318. https://doi.org/10.1108/ITP-11-2017-0386 .

Mishra, S., & Singh, S. P. (2020). A stochastic disaster-resilient and sustainable reverse logistics model in big data environment. Annals of Operations Research . https://doi.org/10.1007/s10479-020-03573-0 .

Modgil et al., ( 2021 ). AI technologies and their impact on supply chain resilience during COVID-19. International Journal of Physical Distribution & Logistics Management , ahead-of-print (ahead-of-print). https://doi.org/10.1108/IJPDLM-12-2020-0434 .

Modgil, S., Singh, R. K., & Hannibal, C. (2021). Artificial intelligence for supply chain resilience: Learning from Covid-19. The International Journal of Logistics Management , ahead-of-print (ahead-of-print). https://doi.org/10.1108/IJLM-02-2021-0094 .

Nayal, K., Raut, R., Priyadarshinee, P., Narkhede, (B) E., Kazancoglu, Y., & Narwane, V. (2021). Exploring the role of artificial intelligence in managing agricultural supply chain risk to counter the impacts of the COVID-19 pandemic. The International Journal of Logistics Management , ahead-of-print (ahead-of-print). https://doi.org/10.1108/IJLM-12-2020-0493 .

Sheng, M. L., & Saide, S. (2021). Supply chain survivability in crisis times through a viable system perspective: Big data, knowledge ambidexterity, and the mediating role of virtual enterprise. Journal of Business Research , 137 , 567–578. https://doi.org/10.1016/j.jbusres.2021.08.041 .

Singh ( 2020 ). Developing Business Risk Resilience through Risk Management Infrastructure: The Moderating Role of Big Data Analytics. Information Systems Management , 0 (0), 1–19. https://doi.org/10.1080/10580530.2020.1833386 .

Singh & Singh ( 2019 ). Building supply chain risk resilience: Role of big data analytics in supply chain disruption mitigation. Benchmarking: An International Journal , 26 (7), 2318–2342. https://doi.org/10.1108/BIJ-10-2018-0346 .

Rajesh ( 2016 ). Forecasting supply chain resilience performance using grey prediction. Electronic Commerce Research and Applications , 20 , 42–58. https://doi.org/10.1016/j.elerap.2016.09.006 .

Zouari et al., ( 2020 ). Does digitalising the supply chain contribute to its resilience? International Journal of Physical Distribution & Logistics Management , 51 (2), 149–180. https://doi.org/10.1108/IJPDLM-01-2020-0038 .

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Zamani, E.D., Smyth, C., Gupta, S. et al. Artificial intelligence and big data analytics for supply chain resilience: a systematic literature review. Ann Oper Res 327 , 605–632 (2023). https://doi.org/10.1007/s10479-022-04983-y

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Received : 14 December 2021

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Issue Date : August 2023

DOI : https://doi.org/10.1007/s10479-022-04983-y

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The impact of big data analytics on company performance in supply chain management.

1. Introduction

2. literature review, 2.1. big data and big data analytics, 2.2. big data analytics in supply-chain management, 2.3. importance and application of bda in scm, 2.4. benefits and constraints of bda for scm, 2.5. big data analytics and supply-chain management in romania, 3. research methodology, 4. results and discussions, 5. conclusions, author contributions, conflicts of interest.

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Oncioiu, I.; Bunget, O.C.; Türkeș, M.C.; Căpușneanu, S.; Topor, D.I.; Tamaș, A.S.; Rakoș, I.-S.; Hint, M.Ș. The Impact of Big Data Analytics on Company Performance in Supply Chain Management. Sustainability 2019 , 11 , 4864. https://doi.org/10.3390/su11184864

Oncioiu I, Bunget OC, Türkeș MC, Căpușneanu S, Topor DI, Tamaș AS, Rakoș I-S, Hint MȘ. The Impact of Big Data Analytics on Company Performance in Supply Chain Management. Sustainability . 2019; 11(18):4864. https://doi.org/10.3390/su11184864

Oncioiu, Ionica, Ovidiu Constantin Bunget, Mirela Cătălina Türkeș, Sorinel Căpușneanu, Dan Ioan Topor, Attila Szora Tamaș, Ileana-Sorina Rakoș, and Mihaela Ștefan Hint. 2019. "The Impact of Big Data Analytics on Company Performance in Supply Chain Management" Sustainability 11, no. 18: 4864. https://doi.org/10.3390/su11184864

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Big data and the supply chain: The big-supply-chain analytics landscape (Part 1)

Big data and the era of digital means a big analytics landscape for supply chain to work with.

Your supply chains generate big data. Big supply-chain analytics turn that data into real insights.

The explosive impact of e-commerce on traditional brick and mortar retailers is just one notable example of the data-driven revolution that is sweeping many industries and business functions today. Few companies, however, have been able to apply to the same degree the "big analytics" techniques that could transform the way they define and manage their supply chains.

In our view, the full impact of big data in the supply chain is restrained by two major challenges. First, there is a lack of capabilities. Supply chain managers—even those with a high degree of technical skill—have little or no experience with the data analysis techniques used by data scientists. As a result, they often lack the vision to see what might be possible with big data analytics. Second (and perhaps more significantly), most companies lack a structured process to explore, evaluate and capture big data opportunities in their supply chains.

In the second part of this article series, we will show how companies can take control of the big data opportunity with a systematic approach. Here, we will look at the nature of that opportunity and at how some companies have managed to embed data driven methodologies into their DNA. Exhibit 1 provides an overview of the landscape of supply chain analytics opportunities.

What is big supply-chain analytics?

Big supply chain analytics uses data and quantitative methods to improve decision making for all activities across the supply chain. In particular, it does two new things. First, it expands the dataset for analysis beyond the traditional internal data held on Enterprise Resource Planning (ERP) and supply chain management (SCM) systems. Second, it applies powerful statistical methods to both new and existing data sources. This creates new insights that help improve supply chain decision-making, all the way from the improvement of front-line operations, to strategic choices, such as the selection of the right supply chain operating models.

Let's look at each main area in turn.

A. Sales, Inventory and Operations Planning

Typically, planning is already the most data-driven process in the supply chain, using a wide range of inputs from Enterprise Resource Planning (ERP) and SCM planning tools. There is now significant potential to truly redefine the planning process, however, using new internal and external data sources to make real-time demand and supply shaping a reality.

We can think about managing inventory in a supply chain similar to the way electricity is managed: Storing electricity is expensive and difficult; power companies bring in additional consumers or start and stop plants to ensure a balanced power grid. Retailers now have the opportunity to use a similar approach. Visibility of point of sale (POS) data, inventory data, and production volumes can be analyzed in real time to identify mismatches between supply and demand. These can then drive actions, like price changes, the timing of promotions or the addition of new lines, to realign things.

Retailers can also use new data sources to improve planning processes and their demand-sensing capabilities. For example, Blue Yonder has developed data intensive forecasting methods now deployed into retailing where 130,000 SKUs and 200 influencing variables generate 150,000,000 probability distributions every day. This has dramatically increased forecast accuracy; enabled a better view of the company's logistics capacity needs; and reduced obsolescence, inventory levels, and stockouts. The recent growth of third party cloud-based services like Blue Yonder is making such activities more accessible for other retailers, too.

Similarly, IBM has helped develop links between production planning and weather forecasts for bakeries. By incorporating temperature and sunshine data, baking companies are able to more accurately predict demand for different product categories based on factors that influence consumer preferences. Amazon, meanwhile, has patented an "anticipatory shipping" approach, in which orders are packaged and pushed into the delivery network before customers have actually ordered them.

Having truly mastered big-data forecasting, the next level of sophistication is to start actively shaping demand. Leading online retailers, for example, use big data analytics, inventory data, and forecasting to change the products recommended to customers. This effectively steers demand towards items that are available in stock.

B. Sourcing

In many companies, data on procurement volumes and suppliers are only gathered for few activities in the sourcing process. However, supply data goes beyond the classic spend analysis and annual supplier performance review. On a transactional basis, supply processes can be sensed in real time to identify deviations from normal delivery patterns. Firms are also finding opportunities for predictive risk management. By mapping its supply chains and using "Google trend"-style information and social data about strikes, fires, or bankruptcies, a firm can monitor supply disruptions in transportation, or at 2nd or 3rd tier suppliers, and take decisive actions before its competitors.

Data analysis can also drive strategic decisions. In recent years, one pharmaceutical company has created a database with all bids submitted for packaging. The data has been evaluated to fully understand the cost structure of those suppliers and to create detailed cost models for different types of packaging. Using updated information on commodity prices, factor costs, and plant utilization, these models can be used to aid the selection of the most appropriate suppliers for new packaging projects. Similarly, Caterpillar has initiated a contest on the crowd-data science website Kaggle to model quoted prices for industrial tube assemblies.

These "clean sheet costing" bottom-up calculations can also be applied in the purchase of transportation and warehousing. By exploiting data on the cost breakdown of operations of trucks and warehouses across the globe, companies do create a powerful fact base to challenge carriers and Logistics Solution Providers (LSPs), and provide real insight into "should cost" during negotiations.

C. Manufacturing

Big data and analytics can already help improve manufacturing. For example, energy-intensive production runs can be scheduled to take advantage of fluctuating electricity prices. Data on manufacturing parameters, like the forces used in assembly operations or dimensional differences between parts, can be archived and analyzed to support the root-cause analysis of defects, even if they occur years later. Agricultural seed processors and manufacturers analyze the quality of their products with different types of cameras in real-time to get the quality assessments for each individual seed.

The Internet of Things, with its networks of cameras and sensors on millions of devices, may enable other manufacturing opportunities in the future. Ultimately, live information on a machine's condition could trigger production of a 3D-printed spare part that is then shipped by a drone to the plant to meet an engineer, who may use augmented reality glasses for guidance while replacing the part.

D. Warehousing

Logistics has traditionally been very cost-focused, and companies have happily invested in technologies that provide competitive advantage. Warehousing in particular has seen many advances using available ERP data. One example are "chaotic" storage approaches that enable the efficient use of warehouse space and minimize travel distances for personnel. Another are high-rack bay warehouses that can automatically reshuffle pallets at night to optimize schedules for the next day. Companies can track the performance of pickers in different picking areas to optimize future staff allocation.

New technologies, data sources and analytical techniques are also creating new opportunities in warehousing. A leading forklift provider is looking into how the forklift truck can act as a big data hub that collects all sorts of data in real time, which can then be blended with ERP and Warehouse Management System (WMS) data to identify additional waste in the warehouse process. For example, the analysis of video images collected by automated guided vehicles, along with sensor inputs including temperature, shelf weight, and the weight on the forklift, can be used to monitor picking accuracy, warehouse productivity and inventory accuracy in real time. Similarly forklift driving behavior and route choices can be assessed and dynamically optimized to drive picking productivity. The data can also be used to conduct root-cause analysis of picking errors by shape, color, or weight, to help to make processes more robust.

New 3D modelling technologies can also help to optimize warehouse design and simulate new configurations of existing warehouse space to further improve storage efficiency and picking productivity. German company Logivations, for example, offers a cloud-based 3D warehouse layout planning and optimization tool.

E. Transportation

Truck companies already make use of analytics to improve their operations. For example, they use fuel consumption analytics to improve driving efficiency; and they use GPS technologies to reduce waiting times by allocating warehouse bays in real time.

Courier companies have started real-time routing of deliveries to customers based on their truck's geo-location and traffic data. UPS, for example has spent ten years developing its On-Road Integrated Optimization and Navigation system (Orion) to optimize the 55,000 routes in the network. The company's CEO David Abney says the new system will save the company $300 million to $400 million a year.

Big analytics will also enable logistics providers to deliver parcels with fewer delivery attempts, by allowing them to mine their data to predict when a particular customer is more likely to be at home. On a more strategic basis, companies can cut costs and carbon emissions by selecting the right transport modes. A major CPG player is investing in analytics that will help it to understand when goods need be shipped rapidly by truck or when there is time for slower barge or train delivery.

F. Point of Sale

Brick and mortar retailers—often under heavy pressure from online competitors that have mastered analytics—have understood how datadriven optimization can provide them with competitive advantages. These techniques are being used today for activities like shelf-space optimization and mark-down pricing. Advanced analytics can also help retailers decide which products to put in high value locations, like aisle ends, and how long to keep them there. It can also enable them to explore the sales benefits achieved by clustering related products together.

Search engine giant Google has acquired Skybox, a provider of highresolution satellite imagery, that can be used to track cars in the car park in order to anticipate in-store demand. Others have explored the use of drones equipped with cameras to monitor on-shelf inventory levels.

A topic that is still a challenge for many retailers is out-of-stock detection and prevention. In developed markets, manual inspections are expensive, while RFID tags still cost too much to be applied to individual grocery items. Instead, retailers are now monitoring sales activity for out of stock indicators. If an item that usually sold every few minutes does not appear at the tills, an alert is triggered to have person check if the item is out of stock at the shelf. Other innovative technologies are also being tested, including the installation of light or weight sensors on shelves as well as the use of in-store cameras to monitor on-shelf stock levels.

Similar technologies can be applied directly at the point of use. Amazon's Dash service, for example provides consumers with wireless buttons that can be used to reorder domestic products with a single push, like washing powder or razor blades. Ultimately, stores may be able to link to data gathered from consumer's Internet-connected refrigerators to forecast demand in real time.

As the examples in this article show, big data is already helping leading organizations transform the performance of their supply chains. Today, such approaches are the exception rather than the norm, however. Lack of capabilities and the lack of a structured approach to supply chain big data is holding many companies back. For big data and advanced analytical tools to deliver greater benefits for more companies, those organizations need a more systematic approach to their adoption. Part 2 of this series will address that topic in detail.

About the authors: Knut Alicke is a master expert in the Stuttgart office, Christoph Glatzel is a director in the Cologne office, and Per-Magnus Karlsson is a consultant in the Stockholm office. Kai Hoberg is an associate professor of supply chain and operations strategy at Kühne Logistics University, Germany.

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World Journal of Science, Technology and Sustainable Development

ISSN : 2042-5945

Article publication date: 6 January 2020

Issue publication date: 10 January 2020

The purpose of this paper is to develop a theoretical model to explain the impact of big data analytics capabilities (BDAC) on company’s supply chain sustainability (CSCS). The secondary objective of the study is to assess the relationship between different dimensions of supply chain sustainability and companies’ BDAC.

Design/methodology/approach

This research was carried out by conducting a survey among 234 pharmaceutical companies in Iran (a case study of Iran), using a standard questionnaire of BDAC and United Nations (UN) online self-assessment on supply chain sustainability. However, the respond of managers of 188 companies were usable in this research. Smart PLS3 was used to employ partial least squares method to examine the validity and reliability of the measurement and structural model.

The results of this study demonstrate that BDAC have a strong impact on both pharmaceutical supply chain sustainability, and the dimensions including vision, engage and internal. It is found that the relationships between BDAC and the other dimensions of supply chain sustainability including expect, scope and goals are not significant but positive.

Research limitations/implications

Research on the relationship between BDAC and CSCS, especially in the pharmaceutical supply chain, is scanty, and this gap is highlighted in developing countries and the pharmaceutical supply chain that plays a prominent role in public health. This paper discusses several important barriers to forming a sustainable supply chain and strong BDA capabilities.

Practical implications

This paper could be a guide to managers and consultants who are involved in big data analytics and sustainable development. Since UN urges companies do the online self-assessment, the results of this paper would be attractive and useful for UN global compact specialists.

Originality/value

No study has directly measured the relation between BDAC and CSCS and different dimensions of CSCS, using a comprehensive survey throughout all pharmaceutical companies in Iran. Moreover, this research assesses the different dimensions of BDA capabilities and supply chain sustainability. This paper represents the facts about situation of sustainability of pharmaceutical supply chain and BDAC in these companies, and discloses several related issues that are serious barriers to forming a sustainable supply chain and strong BDAC. In addition, this paper provided academic support for UN questionnaire about CSCS and used it in the survey.

• Sustainable development
• Pharmaceutical supply chain
• Big data analytics
• Global compact
• Supply chain issues

Shokouhyar, S. , Seddigh, M.R. and Panahifar, F. (2020), "Impact of big data analytics capabilities on supply chain sustainability: A case study of Iran", World Journal of Science, Technology and Sustainable Development , Vol. 17 No. 1, pp. 33-57. https://doi.org/10.1108/WJSTSD-06-2019-0031

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Mapping Out How Geospatial Technology is Transforming Supply Chains

Shravan Kumar

• September 13, 2024

In the past few years, we’ve seen how unprecedented events can turn supply chains upside down—most notably, the Suez Canal blockage and more recent conflicts that added over 10 days to transportation times .

Needless to say, data-driven decisions are pivotal to maintaining processes that are as seamless as possible. However, coordinating and managing these data points can be a massive burden for supply chain managers.

The “where” in supply chain data is just as vital as the what, how, and why. That’s where geospatial technology and location intelligence come into play. Let’s examine how geospatial technology is revolutionizing supply chain logistics to be more robustly interconnected and how organizations can integrate this solution.

Bolstering Resilience

There are four broad areas in which location intelligence is really making a dent in supply chain resilience: load planning, smart mobility, risk monitoring, sustainability, and strategic site selection.

Location intelligence enhances the efficiency of network analysis by visualizing routes, identifying bottlenecks, and optimizing network configurations. When it comes to planning loads and routes, this can be a gamechanger for cutting down on emissions and costs.

For instance, a large-scale refrigerated goods logistics company covers hundreds of thousands of miles on a monthly basis. Every empty mile, where drivers are traveling without transporting any goods, such as on return trips from deliveries, wreaks havoc on the company’s carbon footprint and finances. Conversely, geospatial technology can help supply chain managers allocate drivers according to where they can derive the most value from their routes to deliver more coverage for clients—while keeping mileage and time top of mind.

Additionally, geospatial technology can yield real-time insights into fleet, fuel, driver, and vehicle performance through GPS and telematic data. This helps end users keep abreast of smart mobility and risk monitoring by knowing where vehicles are currently located and also ensuring drivers are adhering to speed and safety protocols. These insights can help organizations gauge fuel consumption and areas of improvement to strengthen the case for sustainability in each mile.

Mitigating Risks

When it comes to climate concerns, organizations typically face two types of risks: physical and transitional. Geospatial technology’s real-time insights can help mitigate such risks. This includes flagging threats like disruptive weather, geopolitical issues, or infrastructure problems. This happens through geospatial data—satellite imagery, GPS data, and geographic information system (GIS) data—which is integrated into location intelligence systems.

Moreover, sustainability officers are mandated to measure, monitor, and declare their operational carbon and embodied carbon footprints. Failing to do so can put companies at reputational risk. Geospatial AI can help sustainability officers accurately report on carbon emissions to fulfill regulatory requirements.

Globally, companies are required to declare operational and supply chain risks due to climate change. This technology can make suppliers more proactive in their risk management, as real-time insights strengthen their ability to adapt and respond competently to hazards—in fact, geospatial AI can help simulate climate change hazards by combining climate and satellite data. Moreover, it can be integrated into wider ecosystems so that when disruptions do occur, companies are equipped to manage their extended supplier networks.

An added bonus of leveraging this technology is that suppliers can stay on top of physical safety. The first instance is preemptive, where alerts can notify teams to avoid certain routes and avoid possible accidents. Secondly, when an incident takes place, central teams can be alerted when a vehicle is down and a driver isn’t responding. With the satellite tracking system, pinpointing drivers’ exact locations can be achieved speedily to ensure rapid response.

Supercharging Analytics with the AWS Sagemaker Geospatial Platform

For many organizations, location intelligence and geospatial data yield huge amounts of analytics and insights that drain manpower, cost, and time resources. The AWS Sagemaker Geospatial platform is transforming data processing. Its AI-powered capabilities make the analysis of large volumes of geospatial data much more manageable.

Moreover, this tool yields customizable analytics and visualization tools with personalized dashboards, which is pivotal to enhancing decision-making processes as it provides organizations with clear insights from complex data. The platform also possesses advanced machine learning capabilities, integrating ML models to analyze geospatial data and providing predictive analytics like demand forecasting and route optimization.

For supply chain managers concerned about integration processes, this technology supports real-time data streaming and integration, enabling dynamic adjustments in supply chain operations. The platform integrates seamlessly with other AWS services, providing organizations with a comprehensive solution for data storage, processing, and analytics.

Best Practices for Adopting Geospatial Tools 

Implementing geospatial tools doesn’t happen overnight—it takes adapting to the technology and building clear internal strategies. The best way to start is for managers to define clear objectives and use cases for utilizing geospatial technology and location intelligence. For example, you can map out gaps in existing processes across your supply chain.

Once you’ve established your company’s specific needs, invest in the right technology. Importantly, choose platforms and tools that can handle large datasets and integrate them with existing systems. By nature, geospatial technology is resource-intensive—it needs people, stringent processes, and sufficient data to function.

Whenever data enters the equation, it’s crucial to ensure its accuracy. Frequently update and validate geospatial data to maintain accuracy while making sure your machinery and workforce are enabling geospatial technology to capture data regularly. This can be scheduled on a regular basis to guarantee the continuity of supply chain management processes.

Additionally, training programs should be provided to ensure that employees can effectively use new tools and correctly interpret the insights—teams have to be upskilled on specialized geospatial technology, its processes, and how solutions can be applied to resolve business problems. It’s also critical that they have ample opportunity to understand how these tools work in real scenarios. For instance, simulations and in-depth workshops are great training techniques so that employees gain and grow the necessary skills to work with these tools.

Moreover, management buy-in is key to properly integrating this solution and ensuring projects are successful when utilizing it. That means geospatial technology has to be ground-truthed to establish trust with stakeholders and nurture widespread adoption through training programs.

Finally, continuously monitor the impact of geospatial technology and location technology on supply chain performance and make necessary adjustments on a regular basis. Leveraging insights directly from the platform and benchmarking these against strategic and operational goals is a great way to gauge performance.

Geospatial technology is the present and future of supply chain management as the world continues to face geopolitical uncertainties, climate change issues, and shortages. Making the most of the solution requires a holistic and interconnected approach that is powered by the right data. Identifying where opportunities lie to strengthen supply chain resilience and mitigate risks will help organizations assess their current needs and how this technology can fit into the bigger picture to future-proof their supply chain strategies.

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