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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Frozen Forecasts?
Should we have frozen forecasts? I’m not talking predicting cold weather or demand for French fries. I’m talking about the concept of having a horizon for which forecast changes are not allowed.
From time-to-time I hear the suggestion that “we should freeze forecasts and not allow any changes within current month” (or some other period). I advocate against this practice.
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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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Tracking Error for Significance
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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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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A Probability Distribution for Demand Variability
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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