How do planners build risk assessment into our S&OP processes? Some tools might include a risk register, SWOT analysis, probability and impact matrix, a tornado diagram, modeling and simulation, probabilistic analysis, or expert judgement. Here are results from the survey we took in January 2023.
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Risk Planning for 2023
The sun just set on another year, a new year is dawning, and there are clouds on the horizon. Will these clouds turn into a storm? If so, how bad will it be?
Demand planners are used to forecast volatility, but there are also many elements of external risk that could significantly impact our businesses. Will the market continue toward a larger downturn? Will Covid surge again, or some other disease? Are the recent attacks on utilities in the Carolinas and in the Pacific Northwest the beginning of some larger threat to our infrastructure? What major cyber-attacks might we see in 2023? How will the price of oil and natural gas be impacted by the ongoing war in Ukraine?
We live in a world full of risk. If the COVID-19 pandemic taught us anything it should be that we cannot take current stability for granted.
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Phugoid Oscillations and Congested Ports
When I was studying for my Private Pilot certification, I learned about phugoid oscillations. Most airplanes that are disturbed from level flight will go through a series of diminishing oscillations until they reach level flight again (if no controls are moved and the trim isn’t changed).
An aircraft that will eventually stabilize itself from minor deviations has aerodynamic stability. Most aircraft are built to be aerodynamically stable. If the pilot stays hands off, the plane will right itself from minor deviations.
Supply Chain Oscillations
We may be seeing something akin to phugoid oscillations in the congestion at ports. Port congestion is peaking again in California and is also building on the east coast.[1] California port congestion last peaked in the first quarter. The congestion was eventually capped by vessel supply. As those vessels were delayed in their normal rounds, other temporary vessels have been added. Now there is another round of congestion building.
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What If We Forgot to Ask What If?
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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Make-to-order or Make-to-stock? Part 2
My last blog introduced the factors to consider when determining whether an item should be Make-to-Order (MTO) or Make-to-Stock (MTS). This blog proposes a step-by-step process for evaluating those factors.
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Planning with Uncertainty
“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
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
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
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 
.
Production Quantity and Obsolescence with Demand Variability
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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