I just finished watching videotapes of a Public Broadcasting Service (PBS) series called the Ascent of Money. Much of it was focused on telling the story of the rise of the global financial industry and how important the concept of money was in creating a global economy. That was the good news in the story.
The bad news was that money’s ascent has also resulted in increasingly bigger economic bubbles as investors fund bigger investments leveraging a tightly connected worldwide money network. These investments can yield untold
rewards. On the other hand, they are very risky because they can also yield catastrophic losses and distress. I covered a perfect example of this in my last column, when I referred to the idea of investors relying too much on other people’s money and resources to succeed—the Other People’s Bubble (OPB) that recently burst causing a worldwide recession.
Risk and Uncertainties Abound
The real story behind the PBS series was that since risk and uncertainty are all around us, in our everyday lives as well as in our business lives, a variety of financial instruments and activities—such as insurance programs and trading markets—have evolved over hundreds of years to help mitigate all types of risk. Over centuries, this has included funding various ill-advised world trade transactions, as well as too many wars. The financial industry has truly been leading all other industries in evolving the science behind risk management. (Note: Just because the industry failed to mitigate the risk of the recent OPB bust, a catastrophic, one-time event, does not imply that they have not mastered mitigating more routine everyday risks and uncertainties, such as we experience in our supply chains).
Trying to ascertain the exact science behind risk mitigation, I commissioned two MIT graduate students to research risk management and see how it could be applied in the Sales and Operations Planning (S&OP) processes that companies use for tactical planning. The research question posed was: “What risk management techniques are financial managers successfully leveraging, and how can they be adapted into a company’s S&OP process?” While managers recognize there is some uncertainty in customer demand, most still assume (for planning purposes) that customer demand is certain, as reflected in their single-point forecasts. They also assume that supply is 100 percent reliable, despite the fact that disruptions might occur in the supply chain and suppliers are not always perfect. The managers’ S&OP processes fail to plan while recognizing that uncertainties exist, hence, they don’t leverage risk management to mitigate the associated risks.
Risk Management in S&OP
The Master’s thesis that resulted from the graduate students’ research was published in June 2008. It was based on extensive literature review, an industry survey, and interviews with various industry managers and luminaries. The students argued that planning for one possible scenario and in only one way is often too dangerous, since it does not account for the riskier, more important segments of a business.
The segmented planning approach for coping with demand-side uncertainties is outlined below and the tactical recommendations are summarized in Exhibits 1 and 2. The exhibits depict the tactics that might be applied to mitigate the demand risk in each product and customer segment, respectively. (The approach for mitigating supply-side risk can be found in the students’ thesis).
1. Future demand should be recognized and represented via range forecasts or scenarios, and not just by a single- point forecast. A point-forecast accounts for only one possible future of demand, which is unrealistic. A range forecast can be expressed as a probability distribution, such as normal distribution with a mean and standard deviation, or as a confidence interval, such as a mid-point and a plus and minus confidence range. Scenarios are often expressed as three values: the most likely, optimistic, and pessimistic.
2. Rate and then rank customers and products on their “Importance” to the business. The criteria can be as simple as the sales volume they represent. Yet it might also include the profitability, as well as the strategic importance of a customer and a product. Create several categories of “Importance,” for example, A represents high, B medium, and C low in importance for products, and Tier 1, Tier 2 and Tier 3 similarly for customers.
This ranking might mimic standard ABC inventory analysis, in which the A products represent the highest-selling 5 percent of the products that represent 60 percent of total sales, and in which the C products represent the lowest-selling 80 percent of items representing only 20 percent of sales.
3. Rate, rank, and categorize each customer on its order lead time. This can be as simple as a customer’s order lead time being categorized as either relatively “Low” or “High” in comparison to other customers.
4. Also rate, rank, and categorize each product on its forecastability. This can be as simple as categorizing a product as either relatively “Low” or “High” in comparison to other products.
5. Implement risk tactics for each segment as shown in Exhibits 1 and 2. Risk tactics are of three types: capacity, inventory and time buffering. These are discussed in my April 2008 Insights column.
The above approach should go a long way toward mitigating the routine demand risks in your supply chain. The thing about uncertainty is that it renders most of your decisions wrong in retrospect. While risk management can never guarantee you’ll make the correct (retrospective) decision each time, it can lead to the “best” decision you can make given the future uncertainties you face.
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