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***Editor’s Note: *This article first appeared in the September 2024 issue of Supply Chain Management Review. For more content like this, click here to subscribe to either the print or digital edition of the magazine.

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My Ph.D. from Wharton is in operations research, which I received a long time ago. My graduate studies included learning about constraint-based optimization algorithms (such as linear programming) and decision analysis theory. There were a few of the more theoretical learnings that I had not seen widely used during my working career.

However, during retirement, I watched the COVID-19 pandemic unfold and noted that traditional business planning processes—such as demand forecasting and the sales and operations planning (S&OP) process—were rendered largely useless. Companies struggled to perform supply demand planning for uncertain futures. They also struggled with severe supply shortages (such as semiconductor chips) and lockdowns that caused labor shortages. Prior S&OP planning assumed supply was plentiful, and that forecasting could be done using historical demand. Thus, I realized that at least two special planning teams would have to be assembled to support forecasting and planning under uncertainty, and during severe supply shortages.

Two special planning teams proposed

In the May/June 2024 Insights column, “Special planning teams for unusual times,” I proposed adding two special ad hoc planning teams to the “hierarchy demand-supply planning framework” presented in it: “Quick Response under uncertainty” and “Supply-in-mind demand optimization under risk.” This approach would keep intact the traditional roles of the S&OP process as the lynchpin tactical-planning process that ties corporate strategic plans to execution-based operational plans. Also, it would not disrupt the activities of an S&OP team, which is not chartered nor experienced in handling uncertainties and severe supply constraints.

Rationale for not disrupting S&OP

S&OP is an important rudder for navigating a company toward financial objectives. The team develops detailed planning numbers that drive operations over a 12 month to 18 month planning horizon. Expecting it to incorporate short-term uncertainties and supply shortages would severely hamper its job of updating detailed plans. S&OP is a tactical planning process under risk, and not under uncertainty. Decision-making under risk is predicated on being able to develop random probability distributions from historical data. Under uncertainty there is negligible useful historical data from which to draw.

In addition, the S&OP process was largely predicated on supply always being available to meet unconstrained demand. Both the short-term “demand control” and SOE operational planning processes also assume decision-making under risk, and that supply is plentiful.

Because a strategic planning process has a planning horizon of three or more years, forecasts are not useful, and uncertainties abound. Thus, strategic plans are traditionally developed using a scenario-planning approach and often need a special project team that might require external third-party consulting help as well.

The Quick Response planning process

In my Insights column, “Under uncertainties: Quick Response, not only S&OP” (March/April 2022), I argued that the S&OP process should not be used for planning under uncertainty.

I recommended a Quick Response team be assembled with responsibility for operational planning during extreme uncertainties in demand and supply. I termed it Quick Response because managers must make fast decisions with little to no historical information. Decisions made are usually practical and involve common sense. For example, during the early days of the COVID-19 pandemic, policymakers could only advise the public to stay away from each other, wear masks, wash their hands, and don’t go to work. Later, policymakers had sufficient information to advise getting vaccinations, as well.

A Quick Response process would be temporarily put in place whenever uncertainties arise in a significant portion of a demand-supply chain. An executive-led team of specialists would be assembled to do short-term planning. It would exist until enough information and data had been assembled to return the planning back to the S&OP team.

To demonstrate how scenario planning might be used in planning decisions under uncertainty for the pandemic, I wrote an Insights column titled: “Decision making under uncertainty: A primer” (May/June 2022). I discussed a hypothetical $1 billion profit-sized company, looking at three types of strategies to deploy throughout the pandemic.

The supply-in-mind demand optimization process

In my March/April 2024 Insights column, “Supply constraints? Demand-shaping revisited,” I discussed another special team that might be required from time to time. It was needed during the COVID-19 pandemic to do short-term operational planning for products with severe supply shortages. S&OP teams are typically not experienced with supply constrained demand matching. Rather they are used to matching future supply with demand forecasts, and then buffering inventory, capacity, and lead times to ensure demand is met.

Generally, a supply-in-mind demand-shaping process involves identifying supply issues and developing opportunities to enhance demand. By aligning supply with marketing & sales activities. It might, for example, be aimed at optimizing demand-side objectives such as revenues, operating margins, and market share.

Additionally, when there is an excess of materials and components, underutilized plant capacities, and a surplus of finished goods inventory, supply managers might collaborate with sales & marketing managers to develop and promote programs aimed at taking advantage of these excess supply opportunities.

However, if there is a shortage of any type of supply, marketing & sales ought to change demand plans to less aggressively promote and sell any products affected because if demand exceeds supply, managers will have to take emergency actions to meet any excess demand, and these will be more costly and less profitable.

Constrained-based optimization needed

During supply shortages, a critical starting question for planners to address would be: “What product mix can be made from readily available finished goods, as well as supplies on hand, on order, and procurable in the future?” A variety of product mixes would be identified to help S&OP teams assess and select which is best. To do this, material requirements planning (MRP) and bills-of-material systems might have to be run in reverse. However, I suspect that most MRP systems might not be easily modified to work in reverse. Nor would it be cost-effective to develop a new system solely dedicated to operational planning purposes. I recommend a more practical approach that would involve a class of well-known constrained-based optimization algorithms. The most used one is the Simplex Method that is used to solve linear programming (LP) problems, such as Microsoft Excel’s free add-in Solver.

LP problems involve only linear equations of decision variables (“Quantitative Analysis for Management 13th Edition” by Barry Render, Ralph M. Stair, Jr., Michael E. Hanna, and Trevor S. Hale. Pearson, 2017). One is the objective function to be optimized, such as maximizing revenue and profit, or minimizing costs and assets. Others involve a set of constraint equations that represent limits upon which the optimal solution is feasible. For example, one equation for selecting an optimal investment portfolio might be as simple as the maximum investment in risky gold stocks, or the minimum investment in safe government bonds.

One of the most used LP formulations is the “product mix problem.” It seeks to determine what product mix will maximize profits subject to various supply and marketing & sales constraints. This LP approach could be used by a demand optimization (DO) special team to develop alternative product mixes that can be made from available supply.

A car manufacturer LP illustration

During the early days of the COVID-19 pandemic, car manufacturers experienced serious semiconductor chip shortages. In addition, as the pandemic ensued, labor shortages arose on assembly lines from lockdowns. As an illustration, a fictitious car manufacturer’s DO planning team has determined that five of its car types were being affected by chip and labor shortages: full-sized sedans; compact sedans; SUVs; electric vehicles; and hybrids.

It wants to provide the S&OP team with how many of these cars to make next month. The DO team decided that the company should make at least a minimal mix of 100,000 cars next month, and then optimize “car operating margins” based on the chips and labor that would be available.

Information on the various parameters including operating margins, minimum production levels, semiconductor chips per car as well as the labor hours needed to make a car are shown in Figure 1. Additionally, the availability of chips for next month’s production is 150 graphics chips and 150 proprietary chips. Also, only 750 hours of unskilled labor and 200 hours of computer tech labor will be available next month.

The LHS of the constraint equations represents the number of cars manufactured and the chip supply utilized. The RHS of the constraint equations represents the minimal car production levels and the available chip supply. The optimal cars to make represent the ideal quantity of vehicles to produce while the optimal car operating margin shows the operating margin for each vehicle type, resulting in a total car operating margin of $2,370. (All numbers are in thousands.)

The DO team also wanted to provide advice to the S&OP team about additional supply that might be added to enhance operating margins, for example, via additional overtime labor hours or spot-market chip purchasing. To do this it looked at the LP spreadsheet and the sensitivity report that “solver” created.

In comparing the LHS to the corresponding RHS, these showed that chips available were not all used in the optimal production mix. This meant that chip supply was not affecting car margin, so increasing the supply of these would not enhance margins. According to the sensitivity report, each hour of computer tech labor would yield $12,000 more in operating margin—minus extra overtime costs. This means that the S&OP team ought to assess whether to add more computer tech overtime hours. The team would need to collaborate with marketing & sales about promoting and selling the optimal mix, as well as collaborate with manufacturing to get a commitment to more hours of overtime by its computer tech workers.

Portending a future with more DO teams

In my November 2023 Insights column, “Global economies also include longevity,” I discussed that the world is getting older because of declining birth rates and people living longer. This is especially true in industrialized economies—wherein significant portions of their adult populations will be retired and significantly smaller portions of young people are working in supply chains. Thus, future economies might find it difficult to sustain an innovative growing workforce to meet the growing longevity demand. While there was a need for DO teams during the pandemic, I suspect few if any companies had them. However, I suspect that these special teams will be required in the future—to plan for the impending labor and supply shortages.

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