What If We Forgot to Ask What If?

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With Easter being last weekend, I thought of an old story about what happened after Jesus returned to Heaven.  Some angels asked about the plan to continue his work on earth.  What would happen if the few disciples he had trained failed to continue his work?  Jesus said, “I have no plan B.”

I heard this story used to teach Christians the importance of evangelism.  I’d like to apply it differently.  My point today is if you are the all-knowing, almighty God, then you don’t need a plan B.  The rest of us need to plan for uncertainty. We need Plan B, Plan C, Plan D, etc.

My wife says when you are watching a scary movie, to be alert when things seem resolved and there is nice music, because something bad is about to happen.  Isn’t that kind of like how supply chain is?

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Tracking Error for Significance

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Have you ever heard or made statements like these:

  • “Our forecast error is down for the third month in a row, showing that our new stat models are working.” 
  • “I want to recognize Susan for having the lowest forecast error last month, Congratulations Susan!”
  • “Forecast error went up for two months in a row, we need to retune the stat models.”

If so, you may want to rethink your credentials as a demand planner.

Demand planners specialize in using statistics to generate forecasts.  But we often overlook the application of statistics to differentiate between common variation and assignable cause in the very metrics we use to measure accuracy.  This may be an area where Demand Planners can learn something from Lean Six Sigma practitioners, and start using Process Behavior Charts to identify when a change in forecast accuracy is significant. 

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Make-to-order or Make-to-stock?

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Part 1- Decoupling Points in Food Manufacturing

What are the best practices for determining whether an item should be make-to-order (MTO) or make-to-stock (MTS)?  Is it better to hold finished goods inventory to react to customer demand or is it better to hold packaging and raw materials with a flexible production schedule to keep working capital down?

It my experience, food manufacturers typically produce in large batches to keep production costs down and optimize plant utilization.  Most items are MTS to assure there is adequate inventory to meet customer service delivery requirements.  The only items that are typically MTO are specialized items for export or military customers, due to shelf life requirements or intermittent demand. But perhaps more than just these difficult items should be MTO.

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Planning with Uncertainty

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“Guests, like fish, begin to smell after three days” is an adage attributed to Benjamin Franklin.  Today we might adapt that old adage to say something about demand disruption.  We are still dealing with so much demand uncertainty even several months into the pandemic, and that stinks like old fish! And there is no end in sight.

No matter what industry you are in, demand uncertainty has moved in and isn’t moving out any time soon. Many industries are seeing lower demand and huge uncertainty, including anything to do with away from home eating and entertainment.  For example, the continued daily uncertainty about restaurants being opened or closed complicates planning for foodservice suppliers, suppliers of suppliers, and growers. 

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Estimating Demand Without History

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My recent blogs discussed how to determine an Economic Lot Size to balance obsolescence, inventory, and changeover costs for perishable products when demand is uncertain.  I recommended using a Gamma distribution to model demand during the shippable life.  A Gamma distribution might also be used as basis for Statistical Safety stock calculations.  

Using a Gamma distribution to project demand variability requires a reliable basis for estimating the distribution. This would usually be based on several years of historical data. However, in times like this current pandemic, history is unreliable as a predictor of future demand. This blog will propose methods for estimating the Gamma distribution function when history is not valid.

I would like to borrow a concept from the Project Management Body of Knowledge (PMBOK), from the Project Management Institute. There are a few different ways it might be applied.

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Economic Lot Sizing with Variable Demand

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Traditional Economic Lot Size (ELS) calculations determine the optimal lot size to balance the costs of holding inventory with the costs of setting up or changing over production lines (see How Much is Enough without being Too Much).  One significant shortfall of ELS is that it assumes demand is constant and the cost of inventory is directly proportional to the number of units produced.  However, that is rarely true in practice.

Many consumer items (such as food) have a fixed shelf life and variable demand. In this situation, obsolescence costs are not directly proportional to production lot size.  At lower lot sizes, the risk of obsolescence may be negligible.  As lot size increases beyond a certain point, expected obsolescence costs increase faster.  With a little more math, ELS can be adapted for this situation.

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Expected Obsolescence for a Given Starting Inventory

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For a product with a set shelf life, obsolescence will occur whenever units sold during the shippable life are less than the starting inventory at the beginning of that period. Varying demand can usually be modelled as a random variable following a probability distribution such as the gamma distribution (see my prior blog: A Probability Distribution for Demand Variability).       

The gamma probability density function is:

where is the gamma function. The mean of the gamma probability density function is ab and the standard deviation is .

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Production Quantity and Obsolescence with Demand Variability

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How much should I make when product has a fixed shelf life?  I should be OK if production does not exceed what I can sell on average before it goes out of code, right?  As Johnny Carson might have said, “You are wrong, bell curve breath!”

We live in a world that talks about averages all the time.  We assume if the expected average result meets our requirements, things will turn out fine. That is not necessarily true.  If it takes an average of 30 minutes to get to work, you should be fine leaving the house with 5 minutes to spare, right?  What if the average includes a one in five chance of being stopped by a train for ten minutes? Four out of five days you can get to work in 28 minutes and one out of five days it takes 38 minutes.   What time would you leave?

In real life, demand for your product fluctuates.  Because of the demand variability, there can be significant risk of obsolescence even when the production run is much less than average demand.   The best way to model this variability may be a gamma distribution (see my prior blog: A Probability Distribution for Demand Variability).  If so, the gamma probability density function can be used to estimate the expected number of units that will become obsolete for a given starting inventory. See how to calculate the expected obsolescence here.

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A Probability Distribution for Demand Variability

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What is a good probability distribution to model fluctuating demand?  Is the Normal distribution applicable?  How can you estimate a probability distribution when history is unreliable?

Understanding demand variability is key to setting an inventory strategy.  Demand variability directly affects the safety stock calculation.  Demand variability and shelf life interact to affect production frequency, thus affecting cycle stock.  An accurate model of demand variability is essential, especially if you have products with limited shelf life that will lose value if demand is less than expected.

This is the second in a series of blogs on the topic of lot sizing to determine optimal batch quantity for production or ordering in uncertain times.  The first blog in this series covered the traditional methodology for Economic Lot Size (ELS).   Upcoming blogs will show how to integrate traditional ELS with the demand distribution covered in this blog and how to estimate a demand distribution when history is unreliable.

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