Demand modeling "sits at the crux of what companies are trying to understand - demand variability," says Smith. It's critical to achieve an understanding at the lowest-possible level of detail. The ability to understand orders, and translate the intelligence into reliable forecasting, is central to realizing revenue and cost savings.
Traditional demand-planning tools fall short when trying to deal with order intermittency, said Smith. In the world of consumer packaged goods, going from weekly or monthly to daily cycles has the tendency to fracture demand signals. Demand modeling can help planners to cope with the problem.
SKU proliferation, coupled with shrinking product lifecycles, has planners crying out for more reliable methods of assessing customer demand. They are struggling to reduce latency in the planning process, by improving the quality of demand modeling in finer, daily buckets. Their goal is achieving "the flexibility to adjust to various demand signals as they come in from the marketplace," says Smith.
Forecasting becomes a special challenge when planners address demand for products outside the realm of fast-moving goods. Their job requires a greater understanding of variability and order frequency. "How often can we receive an order of this particular SKU?" asks Smith. "Demand modeling answers that."
Time horizons for assessing demand can range from the next day to 18 months or more in the future. Regardless of the scope of the exercise, data should feed directly into a company's sales and operations planning (S&OP) process. Having all that information in one place helps companies better to determine the probability of future events, such as extreme weather and product promotions.
