هزینه یابی بر مبنای فعالیت و مدیریت اعمال شده در یک سیستم پشتیبانی تصمیم گیری ترکیبی برای مدیریت دستوری

This article introduces a new Cost Management and Decision Support System (DSS) applicable to Order Management. This model is better fit and compatible with today's competitive, and constantly changing, business environment. The presented Profitable-To-Promise (PTP) approach is a novel modeling approach which integrates System Dynamics (SD) simulation with Mixed-Integer Programming (MIP). This Order Management model incorporates Activity-Based Costing and Management (ABC/M) as a link to merge the two models, MIP and SD. This combination is introduced as a hybrid Decision Support System. Such a system can evaluate the profitability of each Order Fulfillment policy and generate valuable cost information. Unlike existing optimization-based DSS models, the presented hybrid modeling approach can perform on-time cost analysis. This will lead to better business decisions based on the updated information.

Decision Support Systems (DSSs) play a crucial role in today's rigorous global competition business environment. By providing on-time and reliable information, DSS assist management decision making in rendering the company more profitable, leaner, more responsive, and agile. In the area of Supply Chain Order Management, DSSs can be formulated through three different theoretical modeling approaches: Available-To-Promise (ATP), Capable-To-Promise (CTP), and Profitable-To Promise (PTP). The first two modeling approaches emphasize the capacity availability in order to decide whether to accept or reject an order, whereas the PTP approach considers the opportunity cost of accepting or rejecting an order as a main decision evaluation factor. In fact, PTP examine decisions based on the possibility of assigning the available capacity by not accepting an order today to another order with higher profit margin which has been predicted to take place in the future. Regardless of the modeling approach used, management requires a complementary tool that can assist them to analyze the impact of the decision implemented on the business status changes. For instance, with respect to ATP and CTP, management needs to know the availability of their production resources after fulfilling each order. However, PTP management needs to monitor and control the costs and profit changes after taking any decision dynamically. The traditional optimization-based models cannot fulfill this requirement. They are only able to provide relevant information based on the business static status. Moreover, the PTP model needs to be developed based on an accounting approach which can accurately estimate the value of consumed resources. Generally, a production process requires three inputs: Direct Labor, Direct Material, and Manufacturing Overhead (MOH). The first two are categorized as direct costs, which are traceable to a specific cost object (e.g. service, product, order). The latter represents a mixture of both direct and indirect costs (e.g. maintenance, security, safety) which represents a challenge to assign them to the cost objects. The Traditional Cost Accounting (TCA) allocates, as opposed to assigning, MOH costs either by using a plant-wide rate or departmental rates; either case may distort the final production cost amount. Especially in a case where there is a highly customized and low volume production process. Unrealistic cost estimation, may lead to mispricing and compromise the firm's growth and profitability. Activity-Based Costing and Management (ABC/M) is a relatively new cost accounting and management approach that enhances the level of understanding about business operation costs; especially MOH costs. ABC/M is an accounting approach which assigns, instead of allocating, MOH costs to the activities. Although the application of ABC/M does not eliminate MOH costs allocation, it can reduce it to some facility-level costs (e.g. facility utility costs, facility managing costs). The importance of hybrid Supply Chain DSSs has been shown comprehensively in a recent study presented by Martinez-Olvera [24]. “As real-life business environments have become really complex, supply chains members have been forced to use hybrid business models (that is, the integration of features of two different business models).” The other three studies which have paid attention to this subject are: [19], [20] and [26]. Martinez-Olvera [24] also discussed the optimization-based simulation models as a potential future work. This article explains how ABC/M can be utilized as a powerful approach to develop a hybrid Mixed-Integer Programming (MIP) and System Dynamics (SD) Decision Support System for Order Management problems. It also introduces a new approach in integrating ABC/M information with SD simulation modeling technique, which results in a more reliable and precise cost monitoring tool. The rest of this article is organized as follows: Section 2 provides a brief literature review, Section 3 elaborates the discussed general Order Management problem, Section 4 incorporates the ABC/M-based Mixed-Integer programming (MIP) decision support model, and Section 5 provides an illustrative numerical example for the developed MIP model. The System Dynamics (SD) cost monitoring model and the hybrid DSS all are explained in Section 6. In Section 7 the relevant conclusion and future work are explained. Finally, the SD model variables' information is given in Appendix.

This study introduces a novel modeling approach by integrating System Dynamics and MIP programming in order to develop a powerful hybrid PTP Decision Support System for the Supply Chain Order Management problem. The developed DSS system assists management in monitoring, analyzing and foreseeing the consequences and outcomes of each decision and monitors their business competitiveness factors. In the first step, we developed a mathematical Decision Support model based on the ABC/M cost structure. In the MIP model a more detailed and activity-oriented cost structure is used to enhance the model accuracy. As a second step, the ABC/M-based SD model presented a complementary tool to the MIP model. This model identifies the interconnections and correlations between the Order Management decision making variables. The SD model helps management to investigate and examine the further consequences of executing the different Order Fulfillment decision scenarios expansively. The model adjusts the pool rates based on the actual costs, defines the on-time selling price based on the Order Management fulfillment policy, and can also serve as a cost monitoring tool with the purpose of checking the costs behavior at different levels and for different products. ABC/M, as a common modeling approach, makes two models work together as a hybrid Decision Support System. The hybrid DSS output indicates that fulfilling more orders actually decreases the company's profit (MIP part output), and requires adjusting the product selling price (SD part output). Depending on the product type and applied Order Fulfillment scenario, the selling price could be decreased or increased compared to the initial selling price used in the MIP model. Reducing the selling price can give more satisfaction to the customer if the level of order fulfillment remains the same. However, increasing the selling price may result in a lower or higher customer satisfaction level. This depends on the customer's understanding and the value given to a better order fulfillment service. Thus, it should be considered that the result of fulfilling more orders completely actually may conflict with the original intention of increasing customer satisfaction. This study can be further expanded by integrating the cost of backlog among the decision making factors, which improves the model accuracy level. Illustrating the role of raw material suppliers and raw material inventory into the hybrid system through the integration of the supplier selection decision based on the supplier costs analysis and raw material holding costs could enhance the model legitimacy level. Another potential extension of this research is by using a similar approach to evaluate the inventory cost. Moreover, a comprehensive design of experiments can be used to analyze the model output and price variations.