The Merging of MPS and Order Acceptance in a Semi-Order-Driven Industry : A Case Study of the Parasol Industry

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1 The Merging of MPS and Order Acceptance in a Semi-Order-Driven Industry : A Case Study of the Parasol Industry Watcharee Wattanapornprom 1,2 and Tieke Li 1,2 1 Dongling School of Economics and Management, University of Science and Technology Beijing, Beijing, China 2 Engineering Research Center of MES Technology for Iron and Steel Production, Ministry of Education, Beijing, China (wwatcharee@gmail.com) Abstract - The main purpose of this research is to demonstrate a newly created paradigm for improving production planning in semi-order-driven industries. The research results provide a clear explanation of the associated planning tasks, the order acceptance and the master production scheduling because their dynamic interactions are essential. Based on an analysis of decision situations among a number of semi-mtodriven planning settings, we provide a framework consisting of separate interlinked quantitative models for order acceptance and master production scheduling using ATP and CTP while considering the contribution of the modeling and evaluation of both situations in a dynamic setting. Our approach is evaluated through a simulative analysis using empirical data from the parasol industry on web-based ERP using the Linux operation system. Keywords - Web-based ERP, Master Production Schedule (MPS), Order Acceptance (OA), Available to Promise (ATP), Capable to Promise (CTP), Parasol Industry. INTRODUCTION In adopting an advanced planning system, the master production schedules (MPSs) found in many research studies can be categorized into four basic production models, including make-to-stock (MTS), make-to-order (MTO), assemble-to-order (ATO) and engineer-to-order (ETO). Differentiated from the rest, if an enterprise adopting the MTO strategy has no finished product inventory, then the main production schedule cannot effectively support the order acceptance decision because the horizon of the main production schedule has a long time span. Hence, the planned objects are generally product variety, and the demand of the customer order usually requires an appointed product and delivery time [1-3]. Consequently, more than one production strategy is used in a company, an important issue faced by companies using MPS arises when some of the logic of the MPS does not fit with their strategy.. Many companies believe that they employ a MTO strategy, but in practice they do not, because their customers cannot wait for the full manufacturing lead time. The worst case arises for an urgent order, which the production will have a shorter lead time and a higher cost, so some parts of the product must be changed due to the bottleneck of the ATO model, while the main production line still uses the MTO model. In these semi-order-driven industries, companies strive to better synchronize their production output with market demand. This current trend of mass customization gradually affects the DP of the customer order, causing it to shift upstream as a result [1-3]. Fig. 1. MTS, ATO, MTO and semi-order-driven industries decoupling point /13/$ IEEE

2 As the figure 1, The decoupling point (DP) is the interface between the forecast-driven and order-driven planning processes; an upstream DP, such as that for ATO or MTO strategies, involves rather long order lead times. Different customers generally have different specifications, therefore the each specifications also has different DP [4-7]. A client,, has to wait for the delivery of the ordered products, but at the very least, he can receive the promised quantity at the promised date. Therefore, the order acceptance (OA) process is very important supply-chain environment to build core competency through fast and reliable order promises to retain customers and increase market share [4-8]. Once orders have been promised, The objective is to keep the promised due dates while running the processes at the lowest cost [9-11]. In MTO, meeting this objective is the major task, where, in general, only capacity restrictions are relevant. For ATO, this task is called demand-supply matching because the given demand (the accepted orders) must be matched with the given supply, and the assembly orders have to be released and sequenced with a short horizon. As opposed to the classical scheduling problem with due date restrictions, additional restrictions on the availability of various materials should be considered, which may be common for various orders [17]. OA in the MTO environment literature can be divided into three categories. The first category [18-20] uses a stochastic model of the production system and an estimate of the flow time through the system to promise due dates for new orders by calculating the flow times with respect to the current utilization of the multistage production system and a single station. The second category [21] is comprised of deterministic approaches for job shop scheduling, first presenting a comprehensive survey of the scheduling research on due date setting decisions, later [22-23] dealing with inserting a new job into a given schedule of old jobs and finally also allowing for rescheduling of the old jobs. More precisely, a previous paper [24] has analyzed the computational complexity of calculating due dates for a batch of new jobs by updating the schedule of the old jobs on a single machine. The third category MIP linear programming was applied to plan (aggregate) capacities and to determine due dates for a multistage production system [25], which is better suited to a midterm demand-supply matching than a short-term due date setting. Then, in 2001, a MIP model was applied to determine the acceptance of orders in a hybrid MTO and MTS environment [26] Based on past studies, the position of the DP has a significant influence on the various planning tasks of demand fulfillment. OA effectively makes a number of decisions about the acceptance of orders and sets due dates for new orders, while the demand-supply matching addresses the execution of the processes downstream from the DP by allocating those incomplete orders to the respective unassigned stock on hand and projected supply. According to the shortage planning, OA is particularly relevant for ATO and MTO supply chains because of the rather long customer order lead times that are predominant in these cases. The type and relevance of the stocks to be considered also vary with respect to the position of the DP. ATO supply chains must address component availability, while additional assembly capacity can be restrictive. On the other hand, for MTO supply chains, production capacities of the many resources downstream from the DP must be taken into account. As we can see, the operational performance is determined by semi-mto-driven production planning, therefore, a clear understanding of the associated planning tasks, OA and MPS, as well as ATP and CTP, including their dynamic interactions, is very important, According to this approach, we propose a new paradigm in production planning using linear and mixed integer programming models that match semi-mto supply chains and use them to help improve the performance of the supply chain in the advance production system. II. METHODOLOGY Figure 2 shows a semi-mto-driven planning system, where the real time OA model is separated from the MPS model. Here, we determined the ATP based on the CTP policies to accept the customers orders in real time. Fig. 2. Merging of the MPS and the Order Accecptance with the ATP/CTP model. According to the model, OA is the first task in the process chain of the fulfillment of a customer order. The result is a decision based on either the acceptance date of the order or, if the order is accepted, on the first promised due date [9-11]. If the customer can configure the ordered products, then the desired configuration must be verified as technically feasible. In the case of ATO, the major step involves checking the availability of the stock of finished

3 products or components [11], which is called the ATP (available to promise) check. ATP is an enhancement functionality provided by recent modern planning and scheduling solutions for placing new production orders or for increasing already scheduled production orders [13]. For example, when sale representatives process their customers orders, they inquire whether the finished product inventory can fulfill the demands of the order, and if so, they accept the order; if not, they inquire whether the main production schedule can fulfill the demands of order, and if so, they accept the order. If the order cannot be accepted at this point, the representatives compute the CTP (capable-to-promise) status in cooperation with the production planning department, and if the capacity can fulfill the demands, they accept the order; otherwise, they reject this order requirement [14]. It is obvious that the OA process is primarily based on a finished product inventory inquiry, a main production schedule inquiry and CTP, and the process of the former two activities mainly occurs through a database inquiry, which is comparatively simple. Therefore, the order fulfillment and ATP/CTP module aim to match customer orders against available quantities on stock and scheduled receipts. Next, customer requests for quantity and delivery time and location can be addressed [9,13-15]. As be presented in the model, all incoming customer requests are processed individually upon their arrival within the OA procedure (1). The OA result gives a production period for the specific configuration of the order, which is determined by taking into account the lead time, the current available resources and the capacity (2) and sales quotas (3). The quoted offer and delivery date that result from the production period are returned to the requester (4). If the offer is accepted, a preliminary production order is generated and passed on to the MPS (5). The objective of the MPS is to coordinate production, procurement and sales in the short term to facilitate an efficient mode of production, and the decision is made by taking into account the set of accepted orders with specific configurations and the quoted due date as well as the aggregate capacity constraints, which originate from the subordinate master production and sales. The MPS produces instructions for downstream planning tasks, such as material requirements (6), as well as updated data regarding unused capacities, which is again transferred to the ATP/CTP (2) and then to the OA. Thus, the MPS defines the actual production period of the preliminary production orders. Because the decision situation of the MPS differs from that of the OA, the actual period does not necessarily coincide with the period derived from the promised due date. In this case, the order is either produced earlier than requested or it is delayed. In figure 3, the upper half of the MPS presents the ATP/CTP recalculation workflow, and the bottom half displays the OA workflow. The ATP/CTP calculation workflow computes both the ATP and CTP quantities that can be consumed by new (customer or production) orders arriving at the DP. This workflow is used when the ATP and CTP quantities are calculated for the first time or when they must be recalculated because the input data have been updated or unforeseen events in the supply or demand processes have occurred, e.g., delays of the projected supply or cancellations of previously committed orders by customers. Generally, the main input data for the workflow include (1) already committed (accepted), but not yet fulfilled orders on the demand side, (2) inventory on hand and projected supply of products (for ATP) and (3) the projected capacity of resources (CTP). The projected supply may consist of past supply orders that are still "in the pipeline" as well as the quantities forecasted in the MPS. Meanwhile, an inventory/capacity netting procedure will verify whether the overall projected supply is sufficient to satisfy all actually fixed orders on time with respect to their already promised due dates. If the supply is sufficient, the corresponding correct ATP and CTP quantities, which remain for allocation to newly arriving orders, can be calculated in an ATP/CTP calculation procedure. Otherwise, the previous assignment of the orders to the supply is no longer valid and must be reconsidered. Therefore, either some already fixed orders have to be unfrozen (they can be assigned a new and thus later due date) or supply and capacity have to be accelerated and increased, e.g., by means of negotiations with suppliers or through overtime work. These firefighting actions have to be repeated until the projected supply and capacity are sufficient to serve the remaining (still) frozen orders on time. Subsequently, in a further re-acceptance step, the new (the second, the third, etc.) promised due dates have to be determined for the unfrozen orders, and the ATP/CTP quantities have to be reduced accordingly through an additional ATP/CTP reservation procedure. The outputs of the ATP/CTP recalculation workflow are re-promised due dates for some (unfrozen) orders and corrected ATP/CTP quantities, which can be promised for newly arriving orders. Now, the updated ATP/CTP quantities are used as inputs to the order entry workflow. Other input data include newly arriving orders (e.g., customer requests, orders to be released to the shop floor) with the desired quantities, desired completion dates, etc. For additional requests, these configurations must be verified for technical feasibility, i.e., orders that cannot be physically produced must be cancelled. Although this step may sound trivial, the determination of such orders can be a complex task; for example, in a mass customization environment, in which the final products are configured by the customer himself, the choices of product options do not necessarily match each other. For all feasible orders, the due date setting sub-process attempts to assign a planned execution date (e.g., for delivery or release) that is close to the desired date and feasible with respect to the currently available ATP and CTP quantities. This due date may either be a single first promised date for the order as a whole or several first promised dates for partial deliveries. However, for customers requesting a delivery date of a potential order, the promised date may be too late from the customers point of view, such that the requested order is not finally placed, but cancelled. All other orders

4 remain allocated. Thus, the corresponding ATP/CTP quantities for the allocated orders are no longer available for further orders and have to be blocked in a subsequent ATP/CTP reservation procedure. As a result, the allocation of orders to ATP and CTP, which was a further result of the due date setting, can again be used. Altogether, the outputs of the order entry workflow are the accepted orders with the first promised dates assigned. Fig. 3. The ATP/CTP calculation and the Order Acceptance Workflow. The computational aspects of the netting and ATP/CTP calculation process can be explained as follows. First, the inventory netting and calculation for a single item are considered, and the planning horizon is subdivided into integer time period. Let, and denote the input data: : Initial quantity of item k on hand : Projected supply of item k at period t : Aggregate actual demand of item at period If : Net inventory quantity of item for period, then Next, if, then a reassignment is required. In this case, some orders must be unfrozen to decrease the customer demand or the supplier must accelerate production to increase ; otherwise, the quantities can be computed from the concept of cumulative with a look ahead:,,, where : Uncommitted quantities of item that are available at period and can be used at period : Cumulated quantities of item that are available for commitment at period In parallel, the capacity netting and calculation are considered for use with the main bottleneck unit that is downstream from the DP. Let and denote the input data: : Projected capacity of resource at period : Demand of resource to produce item Then,, which is the net capacity of resource in period, can be calculated by If the net capacity is still available to serve new incoming orders in period,, ; otherwise, a reassignment is required by unfreezing the already fixed orders or by increasing the capacity of resource for period, e.g., by means of overtime work. If there are several bottleneck capacities m downstream from the DP, the resulting quantity for a certain item is the minimum of. As shown by the flowchart and calculation method, there are two issues of particular concern. (i) Because the projected supply and the projected capacity, which are used as the input data in the above calculation, are usually the result of the MPS, which itself requires input from the master production plan and demand planning (see Fig. 1), a reasonable MPS, e.g., within an APS, takes all capacity restrictions into account and thus ensures that the ATP quantities are also feasible with regard to the upstream capacities. However, if these capacities are not completely allocated by the forecasted demand and if the actual demand is higher, then the quantities are smaller than necessary. (ii) The re-planning frequency of both workflows may vary. The OA workflow was triggered by each arriving order or it was executed periodically. In the latter case, all orders arriving between two subsequent re-planning events, e.g., during a day, are gathered and processed in a batch. The ATP/CTP calculation workflow was initiated periodically. Initially, a demand analysis based on sales rankings for the last 12 months was performed on product items in both production lines, and the top 36 main products accounting for approximately 80 percent of the total sales were selected for the MPS developed in this article.

5 A daily rolling horizon MPS was developed for the planning timeframe of April 2012 to March Initially, a MPS was developed for the months of April 2012 to March After implementing the decisions for day-one, the schedule is rolled ahead one period (one day), and two policies were developed for the subsequent day-to-day actual operations the from the months of April 2012 to March This process was repeated, and the final MPS covered the end of March 2013 to the first of April 2013, with March 31th being the day for which decisions are to be implemented. The actual production, inventory levels and manpower utilized for the planning horizon of the experiment starting on April 2012 for the 36 main items considered in this research were made available by the company as well as the actual daily incoming customer data. The experiment was performed under the assumption that there were no problems with the supplier, for example, no late deliveries. The integer goal programming model for the re-acceptance and due date setting problem was solved using Lindo Solver Suite. III. RESULTS In this section, the results obtained by solving the MPS model are presented. Table I provides a summary of the decisions actually made by the company in producing the 36 products for the planning horizon from April 2012 to March Table II provides a summary of the results of the model s application and also cost comparisons between actual company decisions and the model results. TABLE I MONTHLY PERFORMANCE INDICATOR FOR POLICY I: MPS WITHOUT ATP OR CTP (ACTUAL COMPANY DECISION) AND POLICY II: ATP BASED ON CTP WITH MPS (MODEL DECISION) MPS without ATP or CTP MPS with ATP and CTP Month Production Quantity Ending Inventory Production Quantity Ending Inventory January February March April May June July August September October November December Total TABLE II COST COMPARISON BETWEEN TWO PLANNING POLICIES (MILLION BAHT/YEAR) Planning Policy MPS without ATP or CTP (Actual company decision) ATP based on CTP with MPS (Model result) Production Ending Inventory Overtime Total Differences (Row 1 Row 2) IV. CONCLUSION Table II clearly indicates that our model results are superior to the actual company performance in terms of total cost. Although some of the results show that the costs are slightly higher when compared to the actual company s performance, the production quantity was also relatively higher. The outcomes point toward that the model can help us to choose a more appropriate order for the company because, with the same CTP, the factory can produce more with a reduced overtime cost. Besides, MPS with the model can reduce the penalty by half, when we consider the ending inventory cost. Some benefit was also gained by avoiding an unnecessary ATP allocation for early production For a semi-mto-driven industry (MTO and ATO), we provided a framework comprised of separate interlinked quantitative models for OA and MPS by using ATP and CTP, while also focusing on the contribution of the modeling and evaluation of both cases in a dynamic setting. The approach is evaluated by means of a simulative analysis using empirical data from the parasol industry on a webbased ERP using the Linux operation system. We observed that our ATP base using CTP with the MPS model exhibited improved results compared to the OA based on the MPS using the company s decisions; however, there are still two disadvantages regarding our implementation. First, because the arrived orders are only one part of the requirements, allocating demands without the consideration of future orders would not comprehensively optimize the ATP plan. Second, this order acceptance model is mainly designed for allocating order demands according to the strategy of FCFS (First Come First Serve), i.e., all orders undergo the same treatment without considering differences in order profit or customer importance. Although our strategy represents a certain level of optimization, its results may have introduced some decreases in profit margin, customer relationships and other performance levels. REFERENCES [1] H. Jodlbauer, Customer driven production planning, International Journal of Production Economics, vol.56, no.1, pp , [2] L. Ozdamar, T. Yazgac, Capacity driven due date setting in make-to-order production systems, International Journal of production Economics, vol. 49, pp.29-44, [3] M.L. Zhang, Study of production planning management system in order-driven small and medium-sized enterprise, in Proc. of 2012 International Symposium on Computer Science and Technology(ISCST'2012), [4] J. Bermudez, Understanding supply chain optimization: from What if to What s best, APICS The Performance advantage, [5] J. Bermudez, Advanced planning and scheduling: Is it as good as it sounds?, The report on supply chain management, Advanced Manufacturing Research, pp. 1-24, 1998.

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