Why Days of Supply Targets Don’t Work when Computing Safety Stocks

Why Days of Supply Targets Don’t Work when Computing Safety Stocks

CFOs tell us they need to spend less on inventory without impacting sales.  One way to do that is to move away from using targeted day of supply to determine reorder points and safety stock buffers.   Here is how a days of supply model works:

  1. Compute average demand per day and multiply the demand per day by supplier lead time in days to get lead time demand
  2. Pick a days of supply buffer (i.e., 15, 30, 45 days, etc.). Use larger buffers being used for more important items and smaller buffers for less important items.
  3. Add the desired days of supply buffer to demand over the lead time to get the reorder point. Order more when on hand inventory falls below the reorder point

Here is what is wrong with this approach:

  1. The average doesn’t account for seasonality and trend – you’ll miss obvious patterns unless you spend lots of time manually adjusting for it.
  2. The average doesn’t consider how predictable an item is – you’ll overstock predictable items and understock less predictable ones. This is because the same days of supply for different items yields a very different stock out risk.
  3. The average doesn’t tell a planner how stock out risk is impacted by the level of inventory – you’ll have no idea whether you are understocked, overstocked, or have just enough. You are essentially planning with blinders on.

There are many other “rule of thumb” approaches that are equally problematic.  You can learn more about them in this post

A better way to plan the right amount of safety stock is to leverage probability models that identify exactly how much stock is needed given the risk of stock-out you are willing to accept.   Below is a screenshot of Smart Inventory Optimization that does exactly that.  First, it details the predicted service levels (probability of not stocking out) associated with the current days of supply logic.  The planner can now see the parts where predicted service level is too low or too costly.  They can then make immediate corrections by targeting the desired service levels and level of inventory investment. Without this information, a planner isn’t going to know whether the targeted days of safety stock is too much, too little, or just right resulting in overstocks and shortages that cost market share and revenue. 

Computing Safety Stocks 2

 

How to Tell You Don’t Really Have an Inventory Planning and Forecasting Policy

The Smart Forecaster

 Pursuing best practices in demand planning,

forecasting and inventory optimization

You can’t properly manage your inventory levels, let alone optimize them, if you don’t have a handle on exactly how demand forecasts and stocking parameters (such as Min/Max, safety stocks, and reorder points, and order quantities) are determined.

Many organizations cannot specify how policy inputs are calculated or identify situations calling for management overrides to the policy.   For example, many people can say they rely on a particular planning method such as Min/Max, reorder point, or forecast with safety stock, but they can’t say exactly how these planning inputs are calculated.  More fundamentally, they may not understand what would happen to their KPI’s if they were to change Min,Max, or Safety Stock. They may know that the forecast relies on “averages” or “history” or “sales input”, but specific details about how the final forecast is arrived at are unclear.

Often enough, a company’s inventory planning and forecasting logic was developed by a former employee or vanished consultant and entombed in a spreadsheet.  It otherwise may rely on outdated ERP functionality or ERP customization by an IT organization that incorrectly assumed that ERP software can and should do everything. (Read this great and, as they say, “funny because it’s true,” blog by Shaun Snapp about ERP Centric Strategies.)  The policy may not have been properly documented, and no one currently on the job can improve it or use it to best advantage.

This unhappy situation leads to another, in which buyers and inventory planners flat out ignore the output from the ERP system, forcing reliance on Microsoft Excel to determine order schedules.  Ad hoc methods are developed that impede cohesive responses to operational issues and aren’t visible to the rest of the organization (unless you want your CFO to learn the complex and finicky spreadsheet).  These methods often rely on rules of thumb, averaging techniques, or textbook statistics without a full understanding of their shortcomings or applicability.  And even when documented, most companies often discover that actual ordering strays from the documented policy.  One company we consulted for had on hand inventory levels that were routinely 2 x’s the Max quantity!  In other words, there isn’t really a policy at all.

In summary, many current inventory and demand forecast “systems” were developed out of distrust for the previous system’s suggestions but don’t actually improve KPI’s.  They also force the organization to rely on a few employees to manage demand forecasting, daily ordering, and inventory replenishment.

And when there is a problem, it is impossible for the executive team to unwind how you got there, because there are too many moving parts.  For example, was the excess stock the fault of an inaccurate demand forecast that relied on an averaging method that didn’t account for a declining demand?  Or was it due to an outdated lead time setting that was higher than it should’ve been?  Or was it due to a forecast override a planner made to account for an order that just never happened?  And who gave the feedback to make that override?  A customer? Salesperson?

Do you have any of these problems?  If so, you are wasting hundreds of thousands to millions of dollars each year in unnecessary shortage costs, holding costs, and ordering costs.  What would you be able to do with that extra cash?  Imagine the impact that this would have on your business.

This blog details the top 10 questions that you can ask in order to uncover what’s really happening at your company.  We detail the typical answers provided when a forecasting/inventory planning policy doesn’t really exist, explain how to interpret these answers, and offer some clear advice on what to do about it.

 

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