A new planning system at DOW Chemical Company

With Amazon gaining more and more importance outside of retail, typical B2B organizations might get threatened. Amazon’s robust infrastructure in logistics is a solid foundation for expansion in areas not obvious in advance, such as the chemical sector. Therefore, conventional B2B organizations are shifting to more agile, scalable and responsive B2C like practices by means of digitalization.  Latu Adiweno explains how delivery performance is improved and unnecessary costs minimized by the new planning system at Dow Chemical Company.

Where it started

At Dow, there is full commitment towards the digitalization of commerce platforms, and the possibility for customers to order online. However, costs and service level are two contradicting parameters in this change. Dow is building its own supporting planning system, called OMP, to help with a hybrid production strategy and thereby create a seamless online customer experience. This must improve service level and simultaneously reduce unnecessary costs.

However, results of OMP are suboptimal, due to unreliable input in the form of production strategy and parameter settings. Eventually, this results in disappointed customers, as the delivery time is inaccurate. Research by Adiweno showed that previous on time delivery (OTD) was 64 percent, while the aim was 90. Consequently, the possibility that customers shift to competitors rises extra costs from inefficient planning must be endured. 

The research by Adiweno focused on determining three crucial inputs for the OMP: production strategy, product identification, and inventory parameter settings. The overall goal was to achieve cost efficiency and improve delivery performance.


Adiweno created a framework to utilize the fill rate and total cost, to represent the delivery service and cost efficiency. Through product identification in the form of data completeness, demand type (lumpy demand) and perishability, 93 initial items were cut down to 28. Further, an inventory simulation model was developed to evaluate the production strategy and helped with determining the appropriate reorder level and batch size. The production strategy had to satisfy a >95% fill rate and at the same time provide the lowest costs, leading to a recommendation for a Make-to-Stock policy. This results in a 24% cost reduction and a 46% increase of the fill rate compared to the current condition.

Further advice

The new production strategy brings better production/machine utilization. But it also requires a 5.8 times bigger inventory. This is currently not a problem because the capacity exists and the holding costs are low, still resulting in a cost reduction. However, this might change in the future and is thus up for consideration. Further, the research shows that important advancements can be made if scientific contributions are considered for the OMP. However, more and cohesive research is needed to address a multitude of inefficiencies, such as using multi-period lot-sizing, including profit calculations or adding make-to-order policies. Nonetheless, the research by Adiweno is a big step in the right direction.