Category: Forecasting and Planning / IBP / S&OP

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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How Much is Enough -Without Being Too Much?

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How much should I make when demand is uncertain? How much should I order?  How much is too much when inventory has a shelf life after which it is no longer usable or sellable? 

These are uncertain times.  For many industries, past sales are no longer valid in predicting future demand. At least not until the economy gets back to a new normal after the pandemic.  Demand is way down, or even completely gone in some industries (such as foodservice and travel).  Demand is way up or at record levels in other industries (such as PPE and cleaning products).

This is the first in a series of blogs on the topic of lot sizing to determine optimal batch quantity for production or ordering in uncertain times.  I’ll explore some of the historical methodologies and some of the newer philosophies.  My focus is on adapting these methodologies and philosophies for products with finite shelf life when demand is uncertain.

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The Great Sequestration

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Yesterday the President of the United States extended nationwide social distancing guidelines for another 30 days.  Our lives and our supply chains are disrupted not only by the pandemic, but also the resulting Great Sequestration.  How should our supply chains adapt to these times and prepare for what comes next?

As most everyone else is, I am sequestered at home, washing my hands frequently, and just going out for essentials. While I’m sequestered, I’m also thinking about how this is going to impact the companies and industries I regularly work with. Not only how to manage the current disruption, but also how to prepare for the future.

After being in touch with multiple clients and industry organizations, I see three essential components to success when coming out the sequestration.  It doesn’t matter whether you are in Senior Management, Demand Planning, Supply Planning, Procurement, or anywhere else in the supply chain, these three things will be essential in how well you weather this crisis.

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SAP IBP – Back to the User

To everything, turn, turn, turn, there is a season…  

When I started out in planning and forecasting, there were no integrated systems.  We built production schedules for the next week or two based on recent sales and open orders.  We knew there were certain products that sold well at certain times of the year (ice cream cones in summer, saltines in the winter).  We built inventory for these products based on last year’s sales and this years’ projections.  And we did it all in spreadsheets.  As we moved to more sophisticated systems, users had less flexibility to quickly adapt to changing requirements.  Now the pendulum is swinging back to user flexibility. Continue reading →