Five Ways to Simplify Your Supply Chain Design with Analytics

Tags: Logistics I.T., Logistics, Technology , Supply Chain

Toby Brzoznowski, is Co-Founder and Executive Vice President, LLamasoft

Ask a supply chain pro to name the biggest problem their organization faces and chances are “effectively using data” will be at the top of the list.

While no one understates the importance of data, supply chain professionals now have access to more data from more sources than ever before, making the effective application of this data a daunting task.

However, the benefits of data far outweigh the negatives, from identifying ROI-building business intelligence, to streamlining employee workflows and much more.

That’s why it’s essential to understand how to navigate your data and make the most of the insights provided to optimize your supply chain.

Here are five ways to effectively utilize data to simplify your supply chain design process:

  1. Data Cleansing: Data cleansing can be used to automatically identify and remove data outliers, identify trends and patterns over time, and model the variability seen in key input parameters, such as lead time, yield, demand, etc. This ensures that inputs into supply chain optimization and simulation models are valid.
  2. Data Aggregation: Data aggregation uses clustering methods of analysis in which objects in the same group are more similar to each other than to those in other groups. This analysis is used to identify groups of customers and products with similar behavior, ensuring that even when aggregating data important factors are kept visible. When the data changes, the groupings change, allowing models to automatically adapt to the new realities.
  3. Cost Modeling: Cost modeling allows supply chain organizations to determine how certain variables like market conditions or expected merchandise volume will influence costs. Seeing this analysis helps guide cost optimization decisions.
  4. Demand Modeling: A key input to supply chain models is the demand that needs to be satisfied, and using only historical data is not sufficient. Demand modeling enables you to accurately project demand into the future, test alternate demand scenarios and sensitivities, understand internal and external drivers of your demand, and incorporate external weather and economic time series data that might impact demand. This gives you a broader, more accurate perspective in order to accurately predict future demand for more informed supply chain decisions.
  5. Output Analysis: Output analysis can help identify key factors like remaining inventory levels, volumes shipped, and costs that lead to business-critical decisions across the entire supply chain. For example, it can help you understand the key drivers that lead to a SKU being stocked or not.

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.






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