Top 4 Use Cases of Manufacturing Supply Chain Analytics

A study by IDC estimates that the global revenue for Big data analytics will be $274B by 2022, and manufacturing will be among the top 3 industries in which analytics will show maximum growth.

Supply Chain involves a range of departments, from manufacturing to delivery. 

Let us see how Supply chain analytics is used in its various branches.

“Supply Chain Analytics helps companies optimize distribution networks, increase operational efficiency, and accurately predict inventory levels, among other uses.”

1. Product Analysis:

Supply Chain Analytics can help companies understand the mechanics related to product deliveries. It can offer better visibility across suppliers, distributors, sales channels, and customers across the product chain. Analytics can compare similar data from different periods to identify patterns and potential causes of changes. Learn the Competition, Conduct Research, Analyze Product Information, Create a Competitive Strategy, Begin with a Flagship Product, Analyze Product Pages.

2. Supply Chain Analytics:

Supply chains usually generate millions of data points. They help companies to uncover patterns and gain deeper insights into this data. 

Supply Chain Analytics can help reduce costs, improve margins, reduce risks, and create a better plan for the future. It also helps organizations monitor their warehouses and partner responses and attend to customer needs more efficiently. Supply Chain Analytics, Capacity planning, Advanced sales and operations planning, Simulation and scenario analysis, Optimization, Demand shaping.

“The above graph shows the increasing organization size of Supply Chain Analytics in large enterprises and SMEs from 2016 to 2027.”

3. Demand Analytics:

Demand determines numerous factors in the supply chain like supply levels, material costs, customer behavior and purchase patterns. Demand Analytics can help the manufacturing industry-

• keep track of the types of products to be manufactured.
• make a note of stock products
• calculate the number of products that needs manufacturing
• predict sales opportunities and purchase trends
• Predict customer behavior and areas of higher profits

Identify the market. The first thing you should do is identify the market you aim to target with your new goods, Assess the business cycle, Create a product that meets a particular niche, Define your advantage, Determine your competitors.

4. Overhead and Labor Costs:

Supply chain analytics can assist organizations in gaining deeper insights into overhead and labor costs and reduce them as far as possible. It can surf through millions of data and create the appropriate context and an accurate format to consider all cost factors.

Investment decisions are critical in this case to help create long-term smart and cost-effective strategies of when, why, and how to invest in new facilities such as factories, warehouses, and the resources and equipment needed. Thus, a well-analyzed investment decision backed by analytics and machine learning can go long.  

List the Expenses, Add the Overhead Costs, Calculate the Overhead Rate, Compare to Sales, Compare to Labor Cost, Percentage on Direct Material Method, Direct Labor Cost Method, Prime Cost Percentage Method.

Conclusion

The best thing about technology is that it is constantly improving. Data science and AI techniques are finding better applications in every industry and helping companies create a more innovative working environment.

Manufacturers today are adopting supply chain analytics faster than they ever had before. As research continues, it is expected to see even more of these adoptions as the technology becomes more mainstream.