In times of increasing competitiveness, the importance of operations planning has become very important in a wide range of industries. Customers today expect quick responses to their orders. They are demanding more and more customized products which they are able to find from different suppliers easily in terms of price and service levels. In order to survive in such a competitive environment, companies are trying to structure their supply chains in order to minimize costs, maximize profits and service levels. Manufacturers have to think in-advance much before the arrival of actual customer orders, especially in a Make-to-Order (MTO) production systems.
MTO is a complex supply chain where raw material and resource availability, assembly and production capacities and other supply chain attributes should be considered in order to promise customer orders. In such an environment, determining which orders to accept and which to reject is one of the most important decisions. Available-to-Promise (ATP) is a business function which has the capability to respond to customer requests by matching them with enterprise resources and at the same time provides acceptable scheduled ship dates.
Available-to-Promise (ATP) enabled solutions usually follow below 4 step process methodology for promising delivery dates.
Stage 1: Customer Segmentation
Stage 2: ATP Allocation
Stage 3: Order Promising
Stage 4: Forward Scheduling
In Stage 1 Customer information, nature of orders we have received in the past and customer type is evaluated in order to define customer classes. The focus is on the profit which each customer gives, type of the customer and the degree of the ordered product complexity.
For Stage 2 Available to promise engine runs and allocation is executed for the customer classes defined above. This stage also needs information of the consensus forecast from Demand Planning for each customer class and supply planning information from Master Production Schedule.
Under Stage 3 Available to promise engine generates expected scheduled delivery dates if the order is accepted and the material and resource capacity is available.
All the orders which could not be fulfilled due to any constraints in material or resources in Stage 3 are passed through the Stage 4 of forward scheduling which calculates about what could be future dates when we can deliver the product.
One of the biggest challenges in demand fulfilment in MTO environment is to implement the model in practice and to get a good balance between solution quality and short response time. Hence there is a strong need to have accurate and clean master data available in the ERP system. If business rules and framework is created which could overcome the limitations, order promising can be made very accurate and enhance the customer service levels.
About the Author
Manmeet Walia has more than 10 years of experience managing and implementing Oracle (VCP/Fusion Cloud) and non-Oracle suite of products in Supply Chain Planning. His focus areas are advisory and solutions consulting for manufacturing organizations in Supply Chain Management, Customer Experience Management and Manufacturing Execution