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.
Capacity investment decisions have traditionally been guesswork. Educated guesswork, to be sure — but still guesswork. Fickle consumers, competitive threats, economic factors and a host of unforeseeable factors often combine to create highly uncertain levels of demand for new products, components and capacity. A new approach to capacity decision-making uses financial engineering techniques, similar to those used on Wall Street, to help organizations reduce their tooling investments, direct material spends, inventory write-offs and total sourcing costs.
Both Ends of the Bullwhip
Capacity decisions are high-stakes decisions, particularly in volatile industries such as semiconductors. Here, competitive advantage is won or lost on unique feature sets and within extremely compressed time-to-market windows. Chip manufacturers typically make billion-dollar investments in foundries, employing the newest technologies, and they must optimize capacity to remain competitive — despite downturns, thin margins and fierce competition. Yet, while these capital investments approach 15 percent of sales, only rudimentary processes and solutions currently exist to aid in capacity investment decisions, with costly consequences.
During upturns, demand spikes quickly deplete safety stocks, resulting in ballooning component lead times, product shortages and lost revenue. With pressure to maintain positive forecasts and service levels, purchase orders often continue to flow and inventories accumulate even after demand softens. Downturns can be just as costly when companies are often left with millions of dollars of custom inventories and orders that can't be returned or cancelled. Ultimately they must be written off.
For example, a large Silicon Valley semiconductor testing equipment manufacturer was chronically caught short when the market would begin to heat up. Without forward-looking capabilities, they picked up the demand indicators late, began ordering components and when demand began to fall off, they could not shut the pipeline down quickly enough to avoid being caught with excess inventory. They're not alone ¬— for the U.S. semiconductor industry as a whole, these kinds of capacity decisions amounts to a $10 billion gamble.
What Will Consumers Want Next?
Similarly, the auto industry represents a hyper-competitive market with many new models and even more options introduced each year. Unlike semiconductor time-to-market measured in weeks or months, bringing new auto models and features to market requires enormous tooling and capacity investments with long lead and ramp-up times. Attempting to discern fickle consumer tastes two to three years in advance exposes automakers to considerable risk — diminished market share if they cannot meet demand for a hot vehicle or evaporating profits if they must deep-discount vehicles.
Rising prices on key commodities such as steel and alloys further squeeze margins. Buy now at a high price to guarantee a quantity and forfeit possible future savings. Or buy only the minimum quantities now, and risk higher prices or shortages. Either way, risk plays a leading role in the equation.
Consumer packaged goods companies also face uncertain consumer adoption rates. Growth in this industry relies on a healthy stream of new products, and leading firms introduce hundreds of new products per year. Forecasts — while notoriously inaccurate — form the basis for key planning and sourcing decisions. With many new products in the pipeline, companies often adopt a rule-of-thumb approach for determining finished goods and raw materials inventory levels. To hedge the possibility that demand will exceed available supply, companies often stockpile, so as not to be caught short. Compounded by significant forecast error for new products, this approach frequently results in substantial inventory write-offs.