Creating a calculation engine to plan for the unique needs of the semiconductor supply chain often requires factoring for both static and dynamic components. While standard, off-the-shelf, planning tools can save internal resources in developing a calculation engine, it is the dynamic components in many cases which preclude the use of them in favor of a custom in-house engine. Here’s a look at best practices and when to invest in an in-house engine.
As the complexity of supply chain planning increases, standard planning tools face challenges in meeting all its dynamic requirements. It is often in these instances where it is best to use an in-house engine. These dynamic requirements are especially pronounced in industries where business models and products are rapidly evolving:
- In the semiconductor environment, binning is a general concept to differentiate the grade of a semi-finished product. The multiple-to-multiple relationship between a semi-finished product and final customer order is another challenge that creates flexible variables which traditional planning tools are unable to meet.
- In the manufacturing process, raising the number of required machines dynamically scales the demand for those machines’ required materials. In this scenario, the unique way of combining different resources to meet the changing manufacturing requirement are rarely supported by a standard planning tool.
- For the LED industry where economies of scale rapidly change, standard tools are unable to evolve with shifting market forces. For example, the average LED selling price reduction year on year requires better and better inventory control and optimized planning, which raises the bar for planning quality. Balancing this with planning solutions for new products and new flows becomes imperative, and thus requires a custom in-house engine.
At a semiconductor company working with several business units, each unit often requires different products from different industries to share the same manufacturing resources. However, the demand stability, product volume and mix are quite different, which result in quite different planning strategies from supply chain point of view. Segmented business unit planners are frequently in charge of a small group of products to enable them to focus on the detailed planning activities such as material requirements planning, order confirmations or loading plans. Due to these factors, a centralized planning mechanism/process is essential to achieve global optimization since the resources are shared by different products crossing the business unit.
Due to the complexities in the manufacturing process, variation in product portfolio, growing complexity of product specifications, and balancing these with the nuances of semiconductor supply chain for our own business, we’ve implemented in-house calculation engines for supply chain planning. From our own experience, deploying these engines has evolved our planning process to the next level:
- The optimization engines are able to predict inventory which give early warning to the organization to adjust demand or specification to avoid slow moving inventory.
- Flexibility and scalability improved. Our technology roadmap is easily supported without the need for major change to the planning tool, which was one of the big difficulties before the in-house engine was implemented. Trouble shooting with a dynamic engine also became much easier.
- As the optimization engines are used in all layer of the supply chain from S&OP to shipment this is driving consistency and stabilized the planning.
- Achieved an adaptive support platform for multiple business requirements across customer and industry groups using different business models.
- Allowed planners to spend more time on business objectives thanks to the efficient planning tool, rather than on routine planning calculations.
Although dynamic variables may create a demand for an in-house calculation engine, the implementation and sustaining of this tool additionally creates new challenges:
- A highly technical skilled team is required, with necessary hardware/software. This can be quite expensive to some company if economies of scale cannot be achieved.
- The role of the planner evolves from just planning centric to include system savviness as well.
- Such system culture is not easy to build. The system won't be simple since the problem solved by it is complicated; which means the system is not easy to be understood by planner, trust and adoption to the system is part of the culture.
Whether customized in-house calculation engines are needed, there is no absolute answer. It works for some companies while it doesn’t for some others. Are you facing similar problems within your supply chain? Feel free to give us your comments in the comments section below.
Additional authors include:
Elle Dings, senior director worldwide supply chain planning LED at Lumileds. Elle is a seasoned professtional with 15 years' working experience in Global Supply Chain operations, Optimizing/Re-engineering Business Process and Program Management in primarily high-tech manufacturing environments. Elle has held leadership roles managing diverse, global, high-performance teams. She has strong analytical skills and is a certified Black Belt in Lean Six Sigma methodologies. Elle has a BS in Applied Science from Fontys University and an MS in Industrial Engineering from Einhoven University of Technology in the Netherlands.
Himanshu Singhal, supply chain excellence director at Lumileds. Himanshu has been with Lumileds since 2016 managing the supply chain systems and tools for the company. Prior to Lumileds, he was with Intel Corporation for 12 years in various supply chain and procurement roles. Himanshu holds a Bachelor’s in Mechanical Engineering from University of Pune, India and an MBA from Thunderbird –The Global School of International Management, Arizona.