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<p>In the competitive environment, many manufacturers are increasingly focusing on designing the systems that help them to manage variable demand and supply situations. Dynamic allocation of demands is very important in case of customer order allocations. Order promising and allocation can be based on the simple sequence that enables a manufacturing company to receive orders unless there are some other priority orders. Manufacturing company can also manage allocations of supply to key customers and channels, thereby ensuring that they can meet contractual agreements and service levels in the priority that yields better profit. This paper will focus on a Maketo- Stock order fulfillment system facing random demand with random orders from different classes of customers. Available-to-promise (ATP) calculating from master production schedule (MPS) exhibits availability of finished goods that can be used to support customer order allocation. This order allocation system is adapted in MTS (make-to-stock) production model and all orders are treated according to maximization of customer service policy. It allows incoming purchase orders as well as existing inventory on hand to be selected and allocated to customer sale orders and back orders. The system then automatically allocates the available stock to the selected sales orders. We developed an integrated system for allocation of inventory in anticipation of customer service of high priority customers and for order promising in real-time. Our research exhibits three distinct features:<br />(1) We explicitly classified customers in groups based on target customer service level;<br />(2) We defined higher level of customer selection directly defined according to company strategy to develop small and medium customers;<br />(3) We considered backorders that manufacturing company has to fulfill in order to maximize overall customer service for certain customers.</p>

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Abstract:This paper will focus on a make-to-stock multi-period order fulfilment system with random orders from different classes of customers under limited production circumstances. For this purpose a heuristic algorithm has been developed aimed at maximizing the customer service level in any cycle and in the entire multi-period. In this paper, in order to validate the results obtained with this algorithm, a mixed integer programming model was developed that is based on the same assumptions as the algorithm. The model takes into account the priorities of customer groups and the balanced customer service level within the same group. The presented approaches are applied to a real example of Fast Moving Consumer Goods. Their comparison was carried out in several scenarios.

Enterprise resource planning (ERP) systems are often seen as viable sources of data for process mining analysis. To perform most of the existing process mining techniques, it is necessary to obtain a valid event log that is fully compliant with the eXtensible Event Stream (XES) standard. In ERP systems, such event logs are not available as the concept of business activity is missing. Extracting event data from an ERP database is not a trivial task and requires in-depth knowledge of the business processes and underlying data structure. Therefore, domain experts require proper techniques and tools for extracting event data from ERP databases. In this paper, we present the full specification of a domain-specific modeling language for facilitating the extraction of appropriate event data from transactional databases by domain experts. The modeling language has been developed to support complex ambiguous cases when using ERP systems. We demonstrate its applicability using a case study with real data and show that the language includes constructs that enable a domain expert to easily model data of interest in the log extraction step. The language provides sufficient information to extract and transform data from transactional ERP databases to the XES format.