The term “supply chain analytics” refers to the procedures businesses employ to glean knowledge and derive value from the vast volumes of data about obtaining, processing, and distributing commodities. Supply chain management must include supply chain analytics (SCM). Although supply chain analytics as a field has been around for more than a century, the mathematical models, data infrastructure, and applications that support these analytics have advanced dramatically. Better statistical methods, predictive modeling, and machine learning have enhanced statistical formulas. Data architecture has transformed with the advent of complex event processing (CEP), the internet of things, and cloud computing. Applications like ERP, warehouse management, logistics, and corporate asset management have developed to the point where they can now offer information across conventional application silos.
Types of supply chain analytics:
The primary categories of supply chain analytics may be distinguished based on Gartner’s concept of the four analytical capabilities—descriptive, diagnostic, predictive, and prescriptive.
- Dashboards and reports are used in descriptive supply chain analytics to aid in interpreting what has occurred. Searching through, condensing, and organizing data on supply chain activities frequently includes using various statistical techniques. How inventory levels have changed over the past month or the return on invested capital are two issues that this might help with.
- Diagnostic supply chain analytics are employed to determine why something occurred or is not functioning as it should. What causes missing or delayed shipments, for instance? or “Why isn’t our business turning through inventory at the same rate as a rival?”
- With current data and predictive supply chain analytics, it is possible to predict what is most likely to occur in the future. How, for instance, could altered trade laws or a pandemic lockdown impact the pricing and accessibility of commodities or raw materials?
- With embedded decision logic or optimization, prescriptive supply chain analytics helps prescribe or automate the optimum course of action. This can facilitate decisions on whether to introduce a product, establish a factory, and the most effective shipping method for each retail location.
Uses of supply chain analytics
- Sales and operations planning uses supply chain analytics to create plans that link daily operations to corporate strategy to balance a manufacturer’s supply with demand. Supply chain analytics is also used for the following purposes:
- improve planning precision by identifying factors that influence demand by analyzing consumer data; Improve risk management by identifying current risks and predicting future risks based on supply chain patterns and trends;
- By creating models to determine the inventory levels required to satisfy service goals with the least capital expenditure, working capital may increase. Combining data sources to evaluate inventory levels, forecast demand, and spot fulfillment concerns can improve order management. Streamlining procurement involves compiling and examining departmental spending to enhance contract negotiations and identify opportunities for discounts or alternative sources.
In conclusion, supply chain analytics are very crucial for organizations.