4 Reasons Why You Must Implement Data Analytics in Supply Chain Management for the Manufacturing Industry

Supply chain management focuses mainly on managing costs and improving profitability. However, executives also know supply chains need to balance quality, availability, and cost by being reliable, efficient, flexible, responsive, and resilient.

“The above chart shows the various goals of supply chain management which has now reached new heights of efficiency by using analytics and machine learning.”

• Supply Chain Analytics for Operational Efficiency:

One of supply chain analytics' main goals is to improve operational efficiency and effectiveness by using data-driven decisions in operational, strategic, and tactical situations. Optimisation models and client-identified tools, and a user-friendly, in-house supply chain simulation tool can also be used to find a suitable strategy for complex operational problems with multiple constraints. 

• Supply Chain Analytics for Risk Management:

Supply chain analytics often use predictive analytics to forecast the risks and consequences of a business decision. One of the major reasons for the rising adoption of predictive analytics is the need to minimize the risks and frauds. The sooner a company can see the threats to its ability to deliver as promised, the faster it can take action to mitigate that risk.

• Supply Chain Analytics for Demand Forecasting:

Numerous factors like transportation, organization and even recruitment can depend on the demand for products. Strategic and long-range plans of a business like its budget, financial planning, sales and marketing, risk assessment, and mitigation plans are formulated to predict the demand for products. Demand forecasting can be short-term, medium to long term, external macro level, and internal business level, etc.

• Supply Chain Analytics for Capacity Planning:

Capacity planning is crucial for scheduling production to meet all types of demands. It can also be used for long-term planning at the organizational and strategic levels. Capacity planning nowadays is automated through software, linking it directly to supply chain planning, allowing companies to view capacity at different operational levels. 

Capacity planning acts as the input for aggregate planning, scheduling, demand management, and demand forecasting, etc.

 “The global supply chain management market was valued at $ 12.96 billion in 2017. It is expected to rise with a CAGR of 12.3% from 2018 to 2026.”

Conclusion

Supply chain management involves a long list of factors like organizing the inventory, transportation, scheduling of deliveries, and recruitments, etc. Analytics has proved its worth in this department by speeding up the various processes, reducing errors and providing predictions to make better business decisions.