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SCDigest Expert Insight: Supply Chain by Design

About the Author

Dr. Michael Watson, one of the industry’s foremost experts on supply chain network design and advanced analytics, is a columnist and subject matter expert (SME) for Supply Chain Digest.

Dr. Watson, of Northwestern University, was the lead author of the just released book Supply Chain Network Design, co-authored with Sara Lewis, Peter Cacioppi, and Jay Jayaraman, all of IBM. (See Supply Chain Network Design – the Book.)

Prior to his current role at Northwestern, Watson was a key manager in IBM's network optimization group. In addition to his roles at IBM and now at Northwestern, Watson is director of The Optimization and Analytics Group.

By Dr. Michael Watson

November 25, 2014

Calculating Supplier Lead-Time Variability: Not as Easy as It Seems

Knowing Lead-time Variability Helps set the Right Safety Stock Level; Tracking and Calculating the Lead-time Variability is not Always Easy

Dr. Watson Says:

...Part of the problem is that it is not always straightforward to track lead-time and lead-time variability...
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Dan Gilmore, in his quest to better understand Out-of-Stocks (OOS), recently discussed how lead-time variability can drive extra inventory.  Or, if you don’t protect against lead-time variability, you will end up with more OOS situations.   In fact, whenever you have any supply chain variability, you have to buffer it with either more inventory, more capacity (like air freight), or time (making your customers wait or higher OOS).  

To help understand the concepts, he has also released a web-based inventory calculator that shows the impact of lowering lead-time.  The math behind the calculation is a bit tough, but the intuition behind it is clear:  If your shipment is late, you better have enough inventory on hand to meet all your expected demand until the shipment arrives.   This is why reducing lead-time variability have a large impact on inventory or OOS.

Gilmore points out that despite the importance of lead-time variability, almost no companies track it. 

Part of the problem is that it is not always straightforward to track lead-time and lead-time variability.  And, existing systems are not set up to track it.  There are three general buckets you should measure to calculate lead-time and lead-time variability.  Each bucket should be measured separately because they have fundamentally different characteristics.  The three buckets are:

Previous Columns by Dr. Watson

The Three Use Cases for Data Scientists

Learn Python, PuLP, Jupyter Notebooks, and Network Design

EOQ Model and the Hidden Costs of Fixed Costs

CSCMP Edge - Nike Quote: "It is All an Art Project Until you Get it on Someone's Feet"

Supply Chain by Design: Why Business Leaders should think of AI as an Umbrella Term



Transit time.  This is the time from when the product leaves your supplier’s shipping dock until it arrives at your dock (or the time from your plant’s dock to your warehouse’s dock).  This is easiest bucket—most companies have visibility to this data.  You can simply take all the transit times, calculate the average and standard deviation and you are done.  It can get tricky when you ship a variety of modes.  For example, if you mostly ship via ocean, but occasionally air freight, you have to make a decision to use just the ocean time for setting inventory levels or use a blended calculation. 



Order-to-Ship Time.  This is the time from when the order is placed until the order ships.  With suppliers, this is relatively easy:  you can measure when you place the order and if you have visibility to the shipment data, know when they shipped the item.  It can get complicated if you frequently change your order or have an open PO.  Then, it may be tough to decide when you actually ordered the item.  It gets harder when the supplier is your own plant.  With your own plant, this is the time from when you decided to make another batch until the items ships—so this includes all the scheduling time, waiting time, and processing time in the plant.



Replenishment Frequency.  This is the extra time because you only order or produce periodically.  If you place an order with a supplier once a week, you are adding a week of lead time to the process.  Even if you order every day, but the supplier still ships once a week, this is still extra lead-time.  When you are ordering from your own plant, this time is often viewed as the frequency of production:  how often you run a given item.  Unless you have a very fixed production wheel, this can be difficult to calculate:  some weeks you make the item twice, some weeks you don’t make it.  You have some control over the replenishment frequency.  For example, if you order or produce weekly instead of monthly you may not fill up the trucks or you many incur extra set-up costs, but the savings in inventory may be worth it. 


Of course, these buckets are just a guide.  In your business, you may have other buckets or you may be able to combine these buckets. 

Once you set up your IT systems to track the raw data in the above buckets, you still have work to do to analyze the data and determine how you will use to determine the average and standard deviation of lead-time.  You also have to realize that your decisions will impact the amount of safety stock you need.  For example, if you decide to calculate the transit time variability with just the ocean freight data, you will hold more inventory than if you included air freight in the calculations.  But, this should then drive down future air freight bills.

Final Thoughts:

In today’s data rich world and with all the tools you have available, you should start to measure and track lead-time variability.  It is too important to ignore. 

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