CASE STUDY

Predictive Analytics in Supply Chain Management Helped a German OEM Manufacturer Increase Accuracy of their Demand Forecast by 37%

May 10, 2021

Engagement Summary

Understanding demand and forecasting accurately remains a significant challenge for OEM manufacturers across the globe till date. The demand in this industry has never been linear and it is affected by uncountable variables, some of which are beyond control. Predictive analytics allows businesses to improve demand forecasting and supply planning, resulting in a high return on investment. Additionally, it also helps in controlling inventory management costs. Usually, OEM manufacturers and suppliers are always ready to invest in predictive analytics to better understand operations and drive innovation in business performance and efficiency in their supply chain organization. This success story revolves around a German OEM manufacturing company that wanted to leverage Quantzig’s predictive analytics solutions to shift from a monolithic architecture of logistics and supply chain operating network.

About the client

The client is an OEM manufacturer based in Germany with active operations across the globe and production hubs in Berlin. This OEM supplier and manufacturer operates through a complex network of over 100 subsidiaries and has regional entities in several countries worldwide.

Most of your logistics and supply chain disruptions are due to inventory mismanagement. Speak with our experts today to learn more about leveraging predictive analytics in supply chain management for OEM manufacturers.

Business Challenge

This client is an OEM supplier of car accessories worldwide with numerous warehouses and factories spread across Europe and Asia. Their business process includes raw material procurement, warehouse management, supply chain operations, and transportation of finished products. With the outbreak of the COVID-19 virus, the manufacturing plants have slowed down significantly in Europe due to a shortage of workforce. But the demand for OEM products has not shown any sign of decrease. This OEM manufacturer was looking forward to leveraging predictive analytics in supply chain operations to improve overall performance. The primary business challenges faced by the client were –

  1. Improve logistics and warehouse management- Improve logistics facilities among the various warehouses and factories were becoming a challenge for the client due to the reduced workforce and spread of COVID-19, resulting in a delay in delivering products, thus indirectly affecting the brand value.
  2. Monolithic architecture of logistics and supply chain operations- The previous logistics and supply chain operating network was based on a monolithic architecture. This monolithic architecture of supply chain operations led to frequent system failures. Every time a new user was added to their supply chain operating network, it crashed, leading to severe disruptions in the complete logistics and supply chain network.

Apart from these primary challenges, this OEM manufacturer and supplier was facing other challenges which were-

  1. Unable to identify upcoming challenges
  2. Ineffectual attempts of improving workflow
  3. The high cost of production

A simple act of editing your monolithic supply chain operating network can be cumbersome. Book a free proposal to understand how we can help you build a microservice-based supply chain operating network.

Solution Offered

This OEM manufacturer collaborated with Quantzig to leverage its cutting-edge technology of predictive analytics in supply chain management solutions. The industry specialists first helped in building a scalable logistics and supply chain operations architecture which helped the client improve their warehouse efficiency across Europe, thus reducing the delivery time.

In the second phase of this success story, Quantzig’s experts derived a demand and planning optimization model for this OEM manufacturer powered by predictive analytics that helped the client improve their existing supply chain operations and mitigate the risk factors that led to massive disruption.

In the third and last phase of collaboration, our experts helped this client validate and deploy a predictive analytics in supply chain model. This predictive analytics in supply chain model runs on ML and AI against real-time production data that is collected from various warehouses and factories to identify upcoming and potential threats. It helped the client to avoid unplanned downtime.

Business Impact

The key business outcomes of this demand and planning optimization were –

  1. Increased accuracy of demand forecast by 37% over a period of six months
  2. Automated manual work, and reduced paperwork for warehouse workforce, thus improving productivity level by 8.7%
  3. Deployed a microservice logistics and supply chain operating network
  4. Streamlining of inventory management for 75 warehouses across Europe

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