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4 Stages of Oracle Global Order Promising (GOP) in Make-to-Order Production Systems

Guest Author

Manmeet Walia, Principal Consultant at Trinamix

In times of growing competitiveness, the significance 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 atmosphere, companies are trying to organize their supply chains with the objective to minimize costs, maximize profits and service levels. Manufacturers have to think in advance much before the arrival of actual customer orders, especially in 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 toughest decisions. Available-to-Promise (ATP) is a business function, which has the capability to respond to customer requests by matching them with the available enterprise resources and at the same time provides acceptable scheduled ship dates.

Oracle Global Order Promising (GOP) provides an internet-based fast ATP tool that calculates accurate and reliable delivery promises. This solution 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

Fig: GOP process flow for order promising in MTO environment

In Stage 1: Customer information, nature of orders (received in the past), customer type etc. is evaluated to define customer/demand class. A demand class represents a grouping of customers, such as government and commercial customers, or it may represent sales channels or regions. Focus here is on the profit which each customer gives type of the customer and the degree of the ordered product complexity.

For Stage 2: Oracle GOP allocates supply for the customer/demand classes defined in the previous stage. This phase also needs information of the forecast from Planning Central/Demand Management for each customer class and supply planning information from Planning Central/Supply Planning. Depending on the situation, the allocation can be equal to the forecast of the customer or to a portion of supply when supply is constrained.

Under Stage 3: Based on configured ATP Rules, GOP engine generates expected scheduled delivery dates if the order is accepted and the material and resource capacity is available. ATP rules define the Order Promising behavior by allowing to specify the promising mode, supplies and demands to be considered during promising, capable to promise and profitable to promise.

Un-satisfied orders due to any constraints in material or resources in Stage 3 are passed through Stage 4 of Forward Scheduling which calculates about what could be possible future dates when the product can be delivered. This phase looks beyond the request date to find a future supply that can meet the shortage.

One of the biggest challenge in demand fulfilment in MTO environment is to implement the supply chain model in practise 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, Oracle Global Order Promising (GOP) can promise very accurate deliveries and enhance the customer service levels.  

 

The author is working with Trinamix as a Principal Consultant. Trinamix, an Oracle Platinum Partner (Cloud Select NA) is the global service provider and the leading implementation specialists for Oracle Cloud Applications. Trinamix has its signature PaaS based solutions on Oracle Cloud. Click here to read Trinamix Success Stories.

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