Traditional supply chains primarily consists of Procurement, In-bound Logistics, Parts Inventory, Manufacturing, Finished Goods Inventory, Fulfillment and Outbound Logistics. As of late, data and data based analytics have become an important piece of the puzzle, especially in strategic Supply Chain design, gaining a competitive advantage in the market.
However, a majority of companies are still lacking in implementing data analytics into supply chain management. There is an urgent need to understand the difference between analytics and reporting. As of now, data science, in supply chain, has mostly been limited to reporting. It has also been driven by statistics and quantifiable performance indicators. Companies need to think of using data beyond demand planning and forecasting, to create a competitive edge in the marketplace.
Data usage and analytics, in supply chain, has matured over time. Primarily, supply chain data has been stored in spreadsheets and physical sheets, coming from different stakeholders of the end to end supply chain. It has transitioned slowly, to EDI and ERP systems. ERP systems, in their initial phase, helped connect and exchange information from one end to another of the supply chain. In the past few decades, ERP systems have not only helped manage end to end supply chain data, but also help in designing, planning, and forecasting, which were not available as a functionality in initial versions of ERP. In the past decade, usage of data has matured to Business Intelligence and Advanced Predictive Analytics. Industries need to assess their supply chain maturity in terms of data usage, and data based decision making. In a nutshell, data analytics plays a significant role in redesigning Supply Chain networks.
Historically, data has been used to determine various elements of lead time, and used to lay out statistical parameters for procurement planning. Similarly, insights from historical demand data are extrapolated into the future, to forge out production plans, and extend it to procurement and logistics plans. In the current scenario, leading organizations are deeply analyzing unstructured data to get valuable insights. For example; in modern warehouses, digital cameras are being used to monitor stock levels and unstructured data. Generated from the video feed, it is used to create alerts for restocking requirements. Forecasting is not only based on structured data, but it also accounts for unstructured data and uses advanced machine learning algorithms to impart higher accuracy. Companies are supplementing their traditional supply chain data with unstructured data coming from various sources e.g. social media.
Organizations need to develop a resource pool to perform advanced analytics among their regular supply chain professionals. Training supply chain professionals to become “citizen data scientists” would be the first step towards having analytics-matured supply chain. Citizen Data Scientist, as defined by Gartner, is a resource who creates models and data based insights, but whose primary job function is outside the field of analytics and statistics. Capabilities of analytics resources include:
As supply chains become more entangled, with the increasing number of entities, companies face risks of losing out on market edge, if they do not look to innovate and make informed decisions based on data analysis. Recognizing this as a thing of the future, many companies are increasingly integrating “analytics” as a core function of their supply chain organizations.
At CGN Global, we utilize our mix of subject matter expertise, in Supply Chain and Data Analytics, to help global organizations capture maximum value from enterprise-wide available information, and leverage it for end to end design and decision-making in supply chain.