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Read this paper for an overview and examples of how big data is used in specific areas, such as supply chain management, risk management, and logistics of business in industry. One of the biggest issues for analysts with big data is knowing how to separate the valuable data from that which does not help answer their requirements. Consider the times, even in school, when you cannot find the right information. Sometimes, narrowing search terms can be difficult if you are unfamiliar with the topic. Sometimes, people describe intelligence as 'connecting the dots', but it is rarely simple, like a 'paint-by-numbers' art project. The dots are not just lying around waiting to be connected. More appropriately, it has been described as filtering out the right radio signals from the fray in a huge city. You have to be carefully tuned to your requirements, as these guidelines keep you on track to finding the right data to answer the questions you need to focus on rather than following rabbit holes and finding yourself in the weeds, awash in signals. What are some examples where you have had to make decisions and were concerned about the quality of the data you used to make those decisions? What did you do to 'connect the dots'?
Big data in supply chain management (other than manufacturing and logistics)
A supply chain is a sequence or network of suppliers, manufacturers, transporters, warehouses, retailers, and customers. Supply chain management is trying to manage the flows of funds, information, and products in a supply chain to ensure a high level of product availability and service to the customer with the lowest possible cost. These days, there are so many records generated by transactions between the suppliers and the purchasers. Choi et al. discussed the application of big data techniques and strategies in various supply chain management topics such as forecasting, revenue management, risk analysis, etc. and provided examples from top branded firms. Even with all the increased usage of big data in supply chain management, there are still several managers that don't apply big data analytics in their decision-making processes.
It is an important issue to consider the availability of data when developing decision models in a system. There are three major flows in each supply chain: information, material, and money. Using data analysis, supply chain managers are able to monitor these flows and apply the results in order to better accomplish their jobs. Researchers believe that information assumes the role of an invisible string between the supply chain members in order to achieve the most efficient cooperation and make the right decisions at the right time, apply the resources at an optimized level, and direct all of the supply chain activities in the right direction.
The convergence of certain factors has recently increased the desire to use data analysis in supply chains: 1- the increased volume of available data in supply chains; 2- the lower cost of data storage compared to past years'; 3- powerful hardware which can speed up data analysis; 4- continuous access from mobile data; 5- powerful tools which simplify working with data; and 6- methods which can graphically show a large amount of data (advanced visualization). Available information in a supply chain is mostly regarding customers, sales, markets, service level requirements, demand forecasts, inventory, capacity deployment, quality control, human resources, skills levels, logistics, resources, warehouse planning, and pricing.
Big data enables companies to better evaluate their suppliers and control the procurement process. Using big data, companies are also capable of simulating their supply chains. Simulation allows for the possibility of finding bottlenecks, virtually running the production process in different locations, and examining prototypes.
Big data can improve the supply chain throughput by increasing the visibility, resilience, robustness, and organizational performance of the supply chain. Big data also improves the knowledge management in supply chains, which can increase the supply chain throughput by improving product development. Moreover, big data can positively affect demand predictions, inventory management, production and service scheduling, and product development in a supply chain.
Supply chains benefit from big data because of the cycle time reduction, cross-functional views, decision-making process improvement, and supply chain performance optimization. For example, big data can reduce the bullwhip effect in a supply chain by reducing the uncertainties of the future demand. Using big data analytics has been shown to be useful in improving the logistics and supply chain management processes. The Thomson Reuters Web of Science is used to study various contributions in the supply chain management and logistics fields by using big data analytics. Figure 5 shows the relative frequency of the published works in big data analytics applications with regards to logistics and supply chain management. Studies are categorized based on their research focus in Figure 5; most categories are explained in more detail afterwards. It is worth nothing, however, that review papers are not discussed further, since we are studying research contributions in the literature; the "review" category is mentioned in Figure 5 just to help give more insight. Moreover, the "Knowledge Management", "Agility", and "Algorithm Development" categories are not further discussed, since each includes less than 5% of the publications.
Figure 5 Publication frequencies with "big data" in title and "supply
chain management" or "logistics" in topic (Thomson Reuters Web of
Science).
Strategy development
Management's decision to use big data analytics in a company would be a strategic one. Management commitment positively affects the level of accepting big data analytics in a company. Using big data analytics lets the manager have access to analysis which based on dynamic data, and can make the supply chain more competitive. Applying big data analytics to planning, decision making, and supply chain coordination and control can improve the preparedness, alertness, and agility of the supply chains in question. The aspect of value creation by big data has not been studied often in supply chain management literature. Therefore, Brinch studied the value discovery, creation, and capture that can be achieved using big data analytics in a business supply chain.
All benefits considered, there still is not enough empirical research that applies big data analytics to supply chain management, so there is a lack of ability to adopt an informed strategy just by comparing different methods. Investing money in the required hardware and software to apply big data analytics may affect the strategies of a supply chain. As for training, our educational system can train a good number of data scientists, but may have ignored the managerial abilities of these data scientists in many cases. As a consequence, converting the available data into applicable knowledge which can mitigate supply chain risks is still an obstacle for many supply chains.
Operations improvement
Dealing with varying numbers of suppliers, manufacturers, logistic providers, etc. creates big data sets that can be used for optimization projects in a supply chain. Big data analytics improves demand forecasts, reduces the safety stock, and improves a supplier's management practices. It has been shown that big data predictive analytics can be combined with other methods such as enterprise resource planning to improve the performance of supply chains. Oncioiu et al. studied the role of big data analytics applications in improving Romanian supply chain companies' performance and implementing assessment processes. Big data analytics has also been used by Boone et al. to improve the practices of service parts management.
In another study, Hofmann shows that the velocity of big data can be used to reduce the bullwhip effect (increasing the safety stock levels in upward echelons) in supply chains. Working with omni-channel supply chains generates a huge amount of data from different sources. Big data analytics can make more accurate sales predictions in different channels and develop optimal delivery plans to minimize transportation costs. In another application, sharing the data through a big data framework can reduce the uncertainty cost in a supply chain.
Sustainability
Another application of big data analytics in supply chains can be the optimization and adjustment of the operations based on sustainable objectives. Big data can improve the environmental, financial, and operational management of the supply chain in order to help combat climate change. Badiezadeh et al. developed a network data envelopment analysis with big data in order to help assess the performance of sustainable supply chain management. Open access to big data can facilitate innovation, create resilient supply chains, and improve the performance of the distribution network. Liu demonstrates how applying big data analytics to the targeted advertising of products can reduce the carbon emissions in a supply chain.
It is worth noting that using big data analytics is not beneficial in all supply chains. There are several barriers that may interact together and prevent big data analytics from establishing a sustainable system. Cheng et al. considers a sustainable supply chain with a manufacturer and a retailer and shows that the proficiency in big data analytics depends on the service level adopted by the retailer. Available big data from transportation and logistic provider companies can be used to satisfy delivery requirements while also keeping in mind carbon emission constraints. In another study, the application of Big data analytics in enabling the resilience of supply chains after disasters was studied by Papadopoulos et al., where they used the example of Nepal to prove their analysis.
Food supply chains
The lack of information in food supply chains can bring about huge costs in the forms of deterioration and waste. Accordingly, using the information excluded from big data analytics is gaining more attention from the decision makers in food supply chains. Big data analytics has applications in agricultural supply chain management, farm management, food sustainability assurance, consumer demand management, new product development, and food safety. Ji & Tan considers five major benefits of using big data in food supply chain management: 1- data sharing over supply chain echelons; 2- doing experiments to find frauds and anomalies in the supply chain; 3- accurate clustering of customers in order to target the marketing of each cluster; 4- developing automated algorithms to support the decision-making processes; and 5- developing new products, services, and business models.
Big data analytics was used by Liu to develop new e-commerce methods for marketing fresh products with a short shelf life by keeping in mind the critical aspects of humidity and temperature. In another research paper, Mishra et al. used social media big data to determine factors that influence customers' beef purchasing decisions. They believe that the available unstructured big data in social media can help businesses to design their supply chain to be more consumer centric. Big data applications have also been developed for cold chains (temperature-controlled supply chains). However, there is an important lack of understanding regarding what data in a cold chain should be collected, and what is the appropriate method to collect and analyze that data.
Risk management
Any supply chain that is dealing with uncertainties in its decision-making processes is using risk management methods at some level. Risk is one of the consequences of a lack of information, and big data can be applied to reduce this lack of information. In the context of logistics processes, transportation risk can be defined as the deviation from the estimated delivery time. Big data analytics can be applied to predict these delivery time deviations, as well as prevent transportation risks such as missing cargo flights. Engelseth & Wang used big data analytics to manage the risks in long-linked supply chains. They used an analytical framework to mitigate the risks of a case study that looked at machine parts imported from China to Norway.
The establishment of big data analytics could resolve bargain issues between a supplier and a retailer. Tsao used big data analytics and game theory to show the way that a supplier and a retailer could determine the period in which to use their credit in order to minimize their risk of defaulting. A type of common risk is that of hazardous materials and waste in closed supply chains (supply chains with remanufacturers and recyclers). Big data analytics has also proven to be useful in recognizing powerful demand signals and minimizing the negative environmental impacts of remanufacturing.
Marketing and sales
Big data analytics can help inform marketing managers of current trends in product sales beyond simply the demand forecasts. For example, product reviews can be approved more easily to help influence sales performance in many studies. The impact of big data analytics on improving sales forecasting was studied in an analytical review by Boone et al.. Sagaert et al. shows that using big data analytics can improve the transparency of market dynamics to sales managers. Using big data analytics in the case study of a tire company could improve forecasting accuracy by 16.1% over the traditional method. Moreover, Li et al. showed that managing a demand chain with big data and electronic commerce works much better than traditional methods of supply chain management.
Using product-in-use data has been proven to reduce the uncertainty for aftermarket (spare parts) demand planning. Gawankar et al. studied the impact of new technologies - such as the Internet of Things and big data analytics - on the retail environment in India. They found that the retailing industry in India is eager to use new technologies in the retailing environment that they call "Retail 4.0" in their study. Big data analytics was also used by Liu & Yi to show the correlation between the price and the products' environment friendliness degree. It shows that the available data can used for targeted advertisements in a supply chain's green environment. Another study in big data pricing application was done byvLiu, in which he considered the data company to be an echelon in the supply chain, and determined its benefits using the Stackelberg game.
Analyzing social media data can help supply chains increase their number of customers in the system through personalized services. Companies can analyze social networks, mobile, and web data to track the way that a customer wants to use the product. On the other hand, Aloysius et al. survey of a group of retail store customers showed that many people have concerns about how much of their personal information is collected, which can negatively affect the store's image.
Some of the selected journal articles regarding big data applications in supply chain management are summarized in Table 2.
Table 2 High-quality articles using big data in supply chain management practices (Citation count is as of March 2020).
Author / Journal | Contribution | Study approach | Case study (NA stands for Not Applicable) | Citation# | Future research topic(s) in the article |
---|---|---|---|---|---|
Hazen et al. (2014)/International Journal of Production Economics | Studying the importance of data quality in supply chain management decisions | Statistical process control / Field study | Remanufacturing company for jet engines and related components for military aircraft | 527 | -Developing new methods for controlling data |
Chen et al. (2015)/Journal of Management Information Systems | Studying the role of big data analytics in value creation and competitive advantage | Technological, organizational, and environmental (TOE) framework | Collected data from supply chain executives through a questionnaire | 192 | -Examining the influence of firm-level employment of big data analytics on organizational performance |
-Examining the intervening variables between organizational IT practices and performance outcomes | |||||
Tan et al. (2015)/International Journal of Production Economics | Providing firms an analytic infrastructure to combine their competence sets | Deduction graph technique | SPEC company, a leading eyeglasses manufacturer in China | 252 | -Testing the contributed approach on other supply chains to determine its general applicability |
-Simplifying the contributed mathematical approach | |||||
Giannakis & Louis (2016)/Journal of Enterprise Information Management | Developing a big data analytics system that exerts autonomous corrective control actions in a supply chain | Analytical study / Supply chain agility theories | NA | 77 | -Studying the application of an agent-based technology in supply chain sustainability |
-Studying the influence of the attributes of supply chain managers on the implementation of agent-based technology in decision making | |||||
Prasad et al. (2018)/Annals of Operations Research | Developing a model to connect big data analytics to superior humanitarian outcomes | Resource dependence theory | Three focal non-governmental organizations' supply network in India | 42 | -Doing research to clearly identify stages regarding big data attributes |
-Examining the scenarios of non-linear patterns emanating from distributed supply chain networks | |||||
Richey Junior et al. (2016)/International Journal of Physical Distribution & Logistics Management | Developing a framework in which supply chain managers can use big data | Native category approach | Interviewing 27 supply chain experts in 6 countries | 68 | -Developing unbiased managerial guidance for using big data analytics in supply chain management |
Gunasekaran et al. (2017)/Journal of Business Research | Studying the impact of big data and predictive analytics on supply chain performance | Statistical analysis / Field study | E-mail survey of a sample of companies in India | 279 | -Investigating top managers' commitment towards developing big data predictive analytics capabilities |
Kache & Seuring (2017)/International Journal of Operations & Production Management | Investigating the impacts of big data analytics on information usage in a supply chain | Delphi survey / Statistical analysis | Collect data from 15 experts by questionnaire | 195 | -Studying the constituents of a big data ecosystem as keys for optimal supply chain productivity |
Roßmann et al. (2018)/Technological Forecasting and Social Change | Studying expert assessments of big data analytics applications in supply chain management | Delphi survey / Fuzzy c-means clustering | Interview with 73 experts | 38 | -Interviewing other fields' experts |
-Studying the impact of potential technological applications on social dynamics in supply chain management | |||||
Choi (2018)/Transportation Research Part E | Studying the impact of social media comments on quick response supply chains in fashion | Analytical mathematical modeling / Newsvendor model | NA | 30 | -Incorporate the correlation of consumer voices and a product's demand |
-Studying the impact of a government's role in local sourcing and emissions taxes on a supplier-market relationship | |||||
Coble et al. (2018)/Applied Economic Perspectives and Policy | Studying the challenges and opportunities of using big data analytics in an agricultural value chain | Analytical study | NA | 65 | -Studying data ownership rules in an agriculture supply chain |
-Developing access to technology infrastructure for rural areas | |||||
Dubey et al., 2019)/Management Decision | Studying how to use big data analytics to improve the agility of a supply chain | Statistical analysis / Hypotheses tests | Collected data from 173 experts by questionnaire | 46 | -Using other theoretical perspectives to study the effect of big data analytics on the agility of a supply chain |
-Using case-based methods instead of survey-based research | |||||
Dubey et al. (2018b)/The International Journal of Logistics Management | Studying big data predictive analytics' impact on coordination and visibility in humanitarian supply chains | Least squares regression / Hypothesis tests | Survey responses from 205 International Non-Government Organizations | 26 | -Considering country culture and/or supply base complexity in a predictive model |
-Applying agent-based simulation methods | |||||
Irani et al. (2018)/Computers & Operations Research | Studying organizational factors that impact the amount of waste in a food supply chain | Fuzzy cognitive map / Simulation | Data from surveying 34 stakeholders in food industry in Qatar | 21 | -Use Delphi method to involve a wider set of participants |
-Develop the same approach in countries besides Qatar | |||||
Jeble et al. (2018)/The International Journal of Logistics Management | Studying the impact of big data and predictive analytics on sustainable business development | Resource-based view logic / Contingency theory | Survey data from 205 individuals in auto components industry | 40 | -Studying the actual impact of big data and predictive analytics on a business firm rather than just the perception of the impact |
-Explore data that can be more generalized | |||||
Lai et al. (2018)/The International Journal of Logistics Management | Studying the factors that determine the adoption of big data analytics in supply chains | Technology-organization-environment (TOE) framework | Survey data from 210 Chinese IT managers and business analysts | 28 | -Increase the environmental safety of big data |
-Studying the other factors that may affect the adoption of big data analytics, such as supply chain scale and delivery complexity | |||||
Lau et al. (2018)/Production and Operations Management | Using consumer social media comments for sales forecasting | Parallel sentiment analysis / Machine learning | Consumer comments datasets in English and Chinese | 31 | -Combining parallel topic models with lifelong learning strategies |
-Examining parallel ensemble models for better sales forecasting | |||||
Gupta et al. (2019b)/Technological Forecasting and Social Change | Using big data analytics to support data-driven decision making in circular economical supply chains | Stakeholder perspective on circular economy | Interview data from 10 expert employees | 19 | -Using larger empirical data for this study |
-Studying inter-organizational relationships, intra-organizational dynamics, and informational privacy issues in supply chains | |||||
Lamba & Singh (2019)/Technological Forecasting and Social Change | Using big data analytics to study a supplier's selection and lot-sizing problem under carbon cap-and-trade regulations | Mixed integer non-linear program | Experimental problem sets | 15 | -Developing heuristics that can obtain the solution via a faster method |
-Studying the same model's behavior under various carbon emission regulations | |||||
Lamba et al. (2019)/Computers & Industrial Engineering | Studying a supplier selection and lot-sizing problem in dynamic supply chains | Mixed integer non-linear program | A randomly generated dataset | 23 | -Studying the stochastic demand with the same problem settings |
-Focusing on the veracity and value characteristics of big data | |||||
Shen et al. (2019)/Technological Forecasting and Social Change | Using big data analytics to find if a retailer must sell green or non-green products first, according to shelf space limitations | Bayesian analysis | NA | 19 | -Studying incentive contracts in order to achieve a coordinated supply chain |
-Studying the role of government interventions on selling green products | |||||
-Studying this case with enough shelf space for both green and non-green products | |||||
Singh & El-Kassar (2019)/Journal of Cleaner Production | Studying the impact of the integration of big data with green supply chain management and human resource management on a firms' sustainability | Statistical analysis / Hypotheses testing | Survey data from 215 employees in Saudi Arabia, the United Arab Emirates, Egypt, and Lebanon | 40 | -Using the same research framework of this study with multisource and/or multi-time datasets |
-Using mixed methods instead of quantitative data within the same research framework | |||||
Yu et al. (2019)/International Journal of Forecasting | Using Google trends to forecast the oil consumption in an oil supply chain | Cointegration tests / Granger causality analysis | Data from Google trends | 38 | -Considering the dynamic between Google trends and oil consumption over time |
-Introducing other types of big data, such as social networks, to the proposed model |