Applying AI to Manufacturing: Linear Order Promising and Production Planning

Many vertical industries within Manufacturing have already entered or are about to enter a new era of mass-customization. Customers expect improved level of service, precise price and date quotes for their personalized orders. Internet communications in general and dedicated e-commerce efforts in particular greatly facilitated the process of taking orders and shipping the requested products to anticipating customers. However, precise, scalable and effective Order Promising and Production Planning still constitute serious challenges for manufacturers. Specialists in Manufacturing Modeling have already identified the deficiencies of the existing approaches that traditionally split production models into Bills of Materials (BOMs) and Routings (Goldratt 1990). Whereas Artificial Intelligence (AI) understood long ago the benefit of merging states and actions in a combined planning model, an alternative, constructive solution to the BOM/Routing modeling approach has not been explicitly proposed. Re-configurable products may lead to an exponential explosion of the number of BOMs, if the standard modeling approach of listing all orderable products is followed. Another complication may come from the existence of alternative routings, which are different production processes (actions) that produce the same inventory items (lead to the same states). A selection of a different route may imply substituting already selected group of inventory items by a different group of items, for example, changing a monitor type for PC may require a different video card, which in its turn may need an upgrade of the power supply module. The above feature is called "kitting" in Manufacturing Modeling. On one hand, a complicated nature of Manufacturing Modeling and a need to capture the AND/OR-logic in presenting inventory items and alternative routings makes it hard to efficiently derive precise price and date quote (Order Promising) and to construct the entire schedule (Production Planning). On the other hand, customers’ expectations and a broad spectrum of orderable products state an urgent need for scalable Order Promising and Production Planning functionalities. In this paper we introduce novel modeling approach that applies some AI modeling techniques to Manufacturing Modeling, allows to avoid the exponential blow-up for re-configurable products and captures the AND/OR-logic without additional modeling efforts. Furthermore, we state a simple, realistic resource sharing assumption. For the introduced type of models, we construct Order Promising and resource allocation (scheduling) procedures that are linear under the stated assumption for any homogeneous objective function.