Demand Forecasting: The Unfair Competitive Advantage
Heres a common “what’s wrong with this picture?” scenario: in many supply chain organizations, sub-SKU forecasting (the task of translating high-level forecasts into specific quantities by size, color, configuration, region, etc.) falls on the shoulders of the sourcing and supply functions, rather than the demand planners. Why? The simple answer, it has always been this way. Does that make it right?
Supply planners and sourcing groups have less exposure to the market demand yet are expected to fully understand what is going on. Taking the burden of ad hoc SKU-level forecasting off their shoulders would help them focus on improved procurement decisions and meeting vendor minimums more cost-efficiently.
Demand Planning: A High-Level Activity
Aggregating provides accuracy. For example, it is easier to forecast how many SUVs will be sold in North America this year versus how many white LX models with a sunroof will be sold in Miami over Memorial Day weekend. Product attributes such as finish, style, color, size, speed, trim level, and configuration complicate forecasting at granular levels.
Without doubt, product family-level forecasts that are accurate enough for monthly sales and operations planning (S&OP) meetings can be significantly skewed at the SKU/location level. However, if one disaggregates through proportional profiling (or as Supply Chain Digest’s Dan Gilmore called it Sub-SKU forecasting in this video), demand planners can use historical sales figures to deepen their forecasts to the sub-SKU level, three, four, or more attribute levels (e.g. model / capability level / feature configuration). Now forecasters can work at a level of aggregation that matches their business requirements, while allocating the forecast accurately across product attributes and options before submitting to the supply side.
This accurate fine-grained demand plan provides the supply-side team vital insights that lead to smarter decisions.
So we officially have a new “unfair” competitive advantage: granular forecasting early in the supply cycle as part of up-front demand planning, the early bird gets the worm! Translating demand plans into sourcing, supply, and production plans sooner sets important aspects in place before all product decisions have been made, vendors have been selected, or the forecast is complete. Production schedules and capacity planning can get underway with enough lead time to create smoother and more efficient production runs.
It is time to take the sub-SKU forecast burden off of the supply team.
So, the promise of using statistical algorithms, forecasting and predictive analytics is now added to the list of a company’s number one priorities. T
In the world of commerce, every business ecosystem has a type of supply chain that is critical to corporate operations.These supply chains rely on a n