Five Ways Effective Use Of Data Can Simplify Supply Chain Design Process

Any organization in the supply chain field will always let you know that effectively using data is one of the most frequent challenges faced. What makes this a more intimidating and overwhelming factor is that supply chain professionals are receiving more data from more sources than ever before.

However, no matter how daunting data ever becomes, the benefits still manage to outweigh the negatives. These advantages range from identifying ROI-building business intelligence, to streamlining employee workflows and so on. They enforce the notion and effort of understanding how to navigate through data in order to make the most of insights provided to optimize the supply chain.

Toby Brzoznowski, Co-Founder and Executive Vice President of LLamasoft, explored the five ways needed to effectively utilize data so to simplify your supply chain design process. Let’s see below what they are.

Data cleansing

Data cleansing is often used to automatically sift through, identify and remove data outliers. It is also used to recognize and point out trends and patterns that take place over time. Lastly, data cleansing also models the variability seen in key input parameters such as lead time, yield demand and all other factors. All this is to assist in making certain that inputs into supply chain organization and simulation models are valid.

Data aggregation

This approach makes use of clustering methods of analysis where objects in the same group are more similar to each other than objects in the other groups. Data aggregation is utilized to identify groups of customers and products that have the same behaviour; making certain that even when data is collective, important factors still remain visible. Aggregation allows models to automatically adapt to new realities when data changes.

Cost modeling

With cost modeling, supply chain organizations are able to conclude how certain variables such as market conditions or expected merchandise volume will influence costs. This analysis provides guidance in terms of cost optimization decisions.

Demand modeling

One of the most important key inputs when it comes to supply chain models is the demand that needs to be met and using only historical data will not suffice. Demand modeling gives solution to this by allowing ground to accurately project demand into the future; test alternate demand scenarios and sensitivities; comprehend internal and external drivers of demand; and incorporate external weather and economic time series data that might affect and impact demand. Doing this enables for a broader, more accurate perspective that will assist in being able to predict future demand for more informed supply chain decisions.

Output analysis

Output analysis helps point out key factors like volumes shipped, remaining inventory levels and costs that equate to decisions that are important for the business across the entire supply chain. An example that is always provided to get the point of output analysis across is one of it possibly helping you understand the key drivers that lead to a SKU being stocked or not.


Brzoznowski concludes by saying “Next time you are building data workflows to generate your supply chain optimization process, try to think about how you can apply these techniques to simplify things and consider data management tools to speed the process and add automation to simplify repeated use. If you do, you’ll certainly begin to reap the rewards of a smarter supply chain design.”


Source: inboundlogistics