The Fundamental Difference
An APS aim to generate a weekly optimized plan for the next 12 to 18 months, relying on forecasted demand and a supply chain cost model. Conversely, DDOM forward schedules actual demand up to proven capacity capability before filling up stock buffers that are based on forward or backwards looking average demand over the lead time.
Major Assumptions
An APS assumes a highly accurate weekly forecast at a SKU level and the availability of quantifiable variables for the costing model. In DDOM, the focus is on making products based on customer orders or confirmed needs. It acknowledges the inherent inaccuracy of weekly forecasts and utilises rough-cut capacity planning to keep the plan feasible.
Made-to-Order (MTO)
MTO items, lacking a forecast, pose challenges in optimized-based models but align seamlessly with DDOM's customer-demand-centric approach.
Visibility of Demand
In APS, customer orders get rolled up into the weekly forecast, leading to a loss of visibility of what the customer wants and when. DDOM, driven by actual demand, enhances decision-making by maintaining visibility throughout the process.
Data Requirements
APSs are data-intensive, demanding vast amounts of additional data for operation. DDOM requires many similar data points like forecasts, production rates, labour rates, routing, minimum order qualities, order multiples as well as line capacities. Additional data, crucial for optimization in APS, includes costs of stockouts, overstocking, understocking, production costs, and demand priorities. These are not data points most companies have as standard.
And let me not mention advanced concepts like Lost Sales Multiplier where the stock out of one product leads to the customer not buying other products you have in stock. I could make boardrooms dance with the opportunity this data point could offer, but left planners scratching their heads as to how to provide this data at the SKU level other than by making more assumptions.
The Black Box Syndrome
APSs are often deemed as black boxes, requiring experts to interpret decisions as a result of extensive customizations made to make the model relevant in a particular environment. In contrast, DDOM is transparent and straightforward, eliminating the need for extensive customization with the result that we use the same model for over 150 customers.
Planner Adoption
APS implementation takes three to four times longer than DDOM to implement and often requires re-implementation 1 year later due to evolving cost models and skill dilution. DDOM models are 98% effective on day one and the availability of global standard certified training provided by the Demand Driven Institute makes the replacement of skilled planners more straightforward.
Conclusion
While APS solutions excel in highly constrained and complex Made-to-Stock environments, DDOM outperforms in environments where prioritizing supply to meet customer orders is the key to success. For those seeking success through adept supply prioritization and simplification, DDOM significantly outperforms APS solutions.
