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Finding Insights to Purchase Less

Inventory is one of the 8 wastes in Lean. Lean was founded in Japan after the destruction caused by World War 2. The Japanese wanted to compete with the big economies of the US and Europe, but were strapped for cash. Reducing inventory is a great way to improve cash availability.

Inventory in the physical sense is easy to identify, just assess the quantity of parts and the work in progress. Excessive inventory is considered wasteful because of the money spent in purchasing and making parts with no customer cash return, yet.

One lever to reduce inventory is by purchasing fewer parts. Determining how many parts to buy and how often can be a challenge, especially if customer demand is variable. Data analytics can be useful in this determination. This data includes:

  • Time parts sit on shelf

  • Lead time and actual delivery time

  • Safety stock levels

  • Customer demand

There are several complicated solutions and services to conduct data analysis, such as predictive analytics; however, there are simple options, too. For example, comparing the lead time vs the actual delivery time on a scatter plot can help identify areas to reduce the lead time. See the figure below:

Supplier C has points closest to the diagonal line. In other words, they deliver the parts as the lead times are planned. Supplier B tends to deliver earlier than the lead time, based on the points below the diagonal. Supplier A tends to take a longer to deliver than the lead time. Supplier A may be able to reduce lead times, which improves order quantities, thus reducing order quantities. Supplier B may be causing other issues, such as premium transportation or quality issues. We will delve into Supplier B's wastes another time.

Another data analysis that can be conducted is a comparison of customer demand vs. safety stock. If the demand for a product line is decreasing, review the safety stock and reduce it accordingly. This analysis may require more advanced data stitching if there is a complex Bill of Material. A person with Excel skills can use V-Lookups and pivot tables to complete the analysis.

Finally, data analysis for evaluating how long parts sit on the shelf. There are a few formulas available to calculate days in inventory for evaluation. This wiki provides 3 options: Once the parts inventories are calculated, create a Pareto chart. The parts with the longest time in inventory can be selected for evaluation. Consider: What is the commercial strategy for these parts? Are they part of the company strategy? Is it worth holding onto parts that don't sell? Is is possible to work with the supplier to reduce the lead time on those parts, so that if sales pick back up, the parts can be readily available.

Remember that cash is king. Investing time in data analytics on the purchased parts can reduce inventory and save cash.

Note: In a future blog, we'll look at how data analytics can reduce Work in Progress (WIP).

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