Chain Reactions: Quantify Risk and Improve Capacity Planning

Capacity planning is risky business for so many different industries. Supply chain risk management is one way to take some of the uncertainty out of your decision-making process


Suppliers Risk Even More

With original equipment manufacturers (OEMs) as customers, suppliers face perhaps even greater challenges. They are expected to continuously innovate and improve responsiveness at shrinking costs. Because most OEMs rely on point forecasts, suppliers are forced to shoulder a disproportionate share of the risk with no visibility from the OEM to help them manage it. And while the OEMs can use just-in-time (JIT) techniques to wish inventory away, suppliers must build stock levels to meet expected demand, considerably increasing their liabilities.

OEMs cannot simply demand more flexibility from suppliers. Traditional one-size-fits-all flexibility programs can't ensure supply continuity in allocated markets. They can't provide adequate liability protection if demand collapses. Even products in the same business cycle may have very different flexibility requirements based on configurations or target market segments. In addition, how does a company define flexibility? How do they size it? How much should they be willing to pay for it?

These are questions that cannot be answered by standard point forecasts and educated best guesses. Regardless of industry, product, or component, forecasting is a treacherous business. Everyone charged with making capacity decision has a point forecast. Everyone knows that his or her forecast will never be exactly right. But almost nobody knows just how wrong it can be — in either direction. With so much at stake, companies need more precise tools for measuring risk, performing trade-off analyses and improving the profitability of their capacity decisions. Supply chain risk management addresses these very issues.

Supply Chain Risk Management — Financial Engineering for Manufacturers

Supply chain risk management (SCRM) is a new approach that applies standard Wall Street financial engineering techniques — hedging, stochastic modeling and real options theory — to capacity planning. For manufacturers, the ability to hedge, that is, capture and quantify the possible demand scenarios, provides far more flexibility in preparing for changing demand conditions. Stochastic modeling incorporates the effects of variable market factors and reflects the impact of these factors on capacity decisions. Real options theory then combines the range of possible demand scenarios with their impacts on the variable market factors to provide companies with a resilient portfolio of tooling, capacity and sourcing alternatives.

SCRM can help organizations to systematically quantify demand and supply uncertainty and make strategic supply chain decisions based on forward-looking, risk-aware metrics. Companies that have experimented with SCRM have moved from old rule-of-thumb, heuristic or best-guess strategies to quantifying the risks they face and the costs associated with managing a range of demand and supply scenarios. From this perspective, they have been able to position their supply chains and supplier relationships to avoid errors and unprofitable bet-the-business decisions.

Understanding Uncertainty — How Wrong Can A Forecast Be?

The first step in reducing supply chain risk is to understand and quantify uncertainty. People always underestimate uncertainty. We rarely anticipate how bad or how good things can go. In manufacturing, this has meant focusing on a single number — the point forecast — instead of looking at the true range of possible demand.

A SCRM approach quantifies the range of possible demand. With SCRM, a company can analyze demand data from analogous situations to accurately predict demand ranges for a new product, component or capacity decision. For example, using SCRM techniques, one company found that its rule-of-thumb forecast of 20,000 to 30,000 units really could range from 10,000 to 80,000 units. SCRM techniques quantify this range in a range forecast using advanced stochastic modeling algorithms, giving companies a precise picture of how high and how low demand can actually go.

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