Big Data Analytics in Supply Chain Management
3. 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.