دانلود مقاله ISI انگلیسی شماره 3597
عنوان فارسی مقاله

اثرات استراتژی های کنترل تولید بر چرخه تبدیل وجه نقد در سیستم های تولید

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
3597 2006 16 صفحه PDF سفارش دهید محاسبه نشده
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عنوان انگلیسی
The effects of manufacturing control strategies on the cash conversion cycle in manufacturing systems
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : International Journal of Production Economics, Volume 103, Issue 2, October 2006, Pages 535–550

کلمات کلیدی
- چرخه تبدیل نقدی - فشار - کشش - چرخه موجودی
پیش نمایش مقاله
پیش نمایش مقاله اثرات استراتژی های کنترل تولید بر چرخه تبدیل وجه نقد در سیستم های تولید

چکیده انگلیسی

It is a common practice to measure the performance of a manufacturing system using common production management criteria such as cell performance metrics or general operations management metrics among engineering management/business administration practitioners. However, most of the time, these performance measures do not truly reflect company's financial performance. It is not unusual to see a well performing operational strategy in terms of one or more cell performance metrics fail to produce the same level of financial performance. The aim of this study is to investigate the effects of the two most common manufacturing planning and control strategies, namely push and pull, on the cash conversion cycle (CCC) in a manufacturing system. The CCC is an important measure of the length of time between cash payment for the purchase of resalable goods or an investment made for production and the collection of accounts receivable generated by the sale of those purchased/produced goods. We have simulated a hypothetical multi-stage manufacturing system that is run under either push or pull control systems to measure the effects of these systems on the financial performance of the company. We used deterministic master production scheduling for the simulated production period to eliminate the variation generated by randomness so that a one-to-one comparison between manufacturing control strategies is made possible. We analyse the results generated by the two control strategies to understand their effects on the CCC and draw conclusions.

مقدمه انگلیسی

The optimum level of working capital will vary depending on the industry in which an organization operates and the nature of its transactions (Walker, 1964; Outram, 1997). It is the responsibility of the operations manager to maintain inventory (material, work in process (WIP), finished goods, and consumables) at optimum levels to meet due dates, minimize production leadtime, and maximize throughput while producing high-quality products with minimal or zero defective or rework, so as to avoid the unnecessary transfer of funds from working capital into rework, subcontracting, wasted capacity, and/or to extra capacity, such as overtime and extra shifts needed to allow for timely work completion. However, operations managers often think they are responsible for a specialized portion of the business only and may regard the cash position as being completely beyond their control and something for the financial managers to worry about (Mehta, 1974). It is unfortunate that the top management of many manufacturing organizations gives far more attention to pure accounting and budgetary control than to cash management and thus working capital control. Operations management, therefore, is denied any responsibility for self-financing even though the profit is earned on the shop floor (van Horne, 1984; McKosker, 2000). Operations managers often make reasonable subjective forecasts of the operating variables they are responsible for. However, it is difficult for them to translate this knowledge into a cash flow forecast, since most operations planning activities are not guided by the tools of finance, and operational and financial analyses are not reconciled. Moreover, much of the literature on operations planning and control seems extremely näive from a financial point of view. On the other hand, financial analysis diverts attention from, and sometimes actively undermines, real shop floor operations strategies. Some even argue that finance theory determinedly ignores operations planning and control system implemented on the shop floor (Grass, 1972). Numerous papers have been published on manufacturing planning and control (MP&C) implementation. MRP-based push and JIT-based pull systems are the two best-known planning and control strategies worldwide (Huang and Kusiak, 1998; Baykoc and Erol, 1998; Thesen, 1999). A review of MP&C implementation studies reveals that little or no recognition has been given to the adaptation of accounting systems to meet the information needs generated by the MP&C strategies. Cooper and Kaplan (1988) argue that management accountants must develop accounting systems that support the changing manufacturing environment and manufacturing control strategies. Moreover, performance measurement is a critical aspect of management accounting systems within an MP&C environment, since inappropriate performance measures not only misrepresent, but also undermine, MP&C efforts (Cooley, 1996). In addition, the relevance of traditional standard costing systems for the purpose of performance measurement in an MP&C environment is questionable. For example, the use of efficiency variances may encourage production for inventory rather than meeting demand. A possible consequence of the use of this type of measure is that inventories of WIP and finished goods will accumulate, contrary to the goal of inventory elimination, which is a central theme in all MP&C strategies. Manufacturing performance measures that relate to the financial needs and goals of the organization should also be introduced. As production processes become more tightly linked through the elimination of non-value-adding activities, timely financial feedback to the shop floor becomes essential. Cash inflows and outflows are usually the consequences of operating decisions. Although the finance manager has direct responsibility for managing “cash”, the operating activities that generate the cash flows are frequently controlled by others in the organization. Research in the area of financial performance of manufacturing organizations is limited and mostly focuses on product cost modelling (Malik and Sullivan, 1995). In the 1990s, the direction of this research moved towards activity-based cost modelling, focusing especially on cost modelling in automated manufacturing systems (Spedding and Sun, 1999; Takakuwa, 1997; Koltai et al., 2000), even though available information on how cost items are apportioned is limited, as is the information reported at the production line. Moreover, this research is overwhelmingly from the financial point of view and deals mostly with designing cost systems for manufacturing organizations to enable them to identify the cost sources better, so that improved cost control mechanisms can be established. Such research assumes that manufacturing lines work under perfect conditions: there are no machine breakdowns, no delays occur, no bottleneck constraints exist, materials are always available, WIP and inventory levels are always negligible. It is also assumed that production environments are deterministic and that part entry and processing times are always predictable. However, reality is very different and the harsh facts of life on the factory floor render all these assumptions unjustifiable. The authors have found only a few publications that consider the effects of manufacturing variability on system's financial performance (Fry et al., 1998; Taylor III, 1999; Leitch, 2001; Boyd et al., 2002; Farris II and Hutchinson, 2002). To the best to our knowledge, this is the first full-scale study that considers, through cost and financial models, shop floor strategies and realities under different operational conditions in relation to financial performance; particularly in relation to inventory turnover and the cash conversion cycle (CCC). The objective of this work is to investigate the effects of MP&C strategies, namely push and pull, on the CCC in manufacturing systems. The CCC is an important metric to measure the length of time between cash payment for purchase of resalable goods or an investment made for production and collection of account receivable generated by sale of these purchased/produced goods. We have simulated a hypothetical multi-stage manufacturing system that is run under either a push or a pull control system to measure the effects of these systems on the financial performance of the company. We used a deterministic master production schedule (MPS) for the simulated production period to eliminate the variation generated by randomness so that a one-to-one comparison between manufacturing control strategies is made possible. Three shop floor control strategies, namely push, pure pull, and a variation of the pull system, which is CONWIP, are implemented to control production. We use both instant and cumulative data to measure the CCC for a certain production period. If the production demand is known (deterministic) and the estimation of product cost, average accounts receivable, average accounts payable, sales revenues, raw material cost, WIP cost and average inventory level for raw material, WIP, and finished goods are typical the budgeting problems we study herein. The aim in budgeting is to determine what resources are needed to produce/sell planned products in the next planning period. Therefore, based on the MPS, we determine which activities were needed to produce the planned items, the cost of these activities, and then we estimate the required budget to manufacture the products ordered in a given planning period through MPS. This paper uses the same manufacturing environment used in Özbayrak et al. (2004) (Fig. 1). The paper then introduces the modelling environment and the operational strategies implemented in Section 2. Activity-based budgeting (ABB) is introduced in Section 3. In this section, we calculated activity usage rates, resource, and product costs. The CCC and the inventory conversion cycle, consisting of the raw material conversion cycle, the WIP conversion cycle and the finished product conversion cycle, are calculated under the three operational strategies in Section 4. This section includes the calculation of the accounts payable and receivable cycle times as well with the discussion of the results. The concluding discussions and conclusions drawn are presented in Section 5.

نتیجه گیری انگلیسی

This study has provided insights into how MP&C systems such as push and pull systems affect the financial performance of a manufacturing organization. The financial performance metrics used are the CCC and its components, the stock conversion cycle (ICD), accounts payable (APD) and accounts receivable cycle (ARD) times. For this purpose a number of shop floor operation strategies were designed and the system was run under push and two modes of pull control systems for each one of the operational design configurations to measure the organization's financial performance. It is common practice among the scientists and practitioners to measure the operational performance mainly based on shop floor metrics, such as mean flow time, mean tardiness, WIP, and hardware utilization. To the best of our knowledge, this is the first time a study has been conducted to gain an insight into how the performance of operations management tools such as push and pull systems on the shop floor affect the company's financial performance. The financial metrics we used are the typical indicators of the system's financial performance and they are mainly driven by the operations that take place on the shop floor. Therefore, these are open to the effects of success or failure of the planning and control regime adopted on the shop floor. This paper further presents a methodology to calculate the CCC in a manufacturing system that is run under either push or pull control policy. The parameters involved in this comparison study are too many and most of them interacts each other. Modelling this highly complicated system in detail makes simulation approach an ideal modelling tool to analyse the relationships between the shop floor and financial performances. The model simulates the manufacturing activities under different control policies and each activity that affects operational shop floor performance is linked to a cost model based on ABB to find out the financial implications of these activities, which in turn generates the financial performance of the manufacturing system. Therefore, this methodology produces the true reflections of the shop floor activities on the financial measures. The push system traditionally relies on queues in front of machines and tries to maximize equipment utilization and production throughput through continuously feeding the machines. Further, push systems rely on traditional batching policies in both material acquisitions from suppliers and releasing jobs to the shop floor. This policy is the main reason why push systems have put up with excessive inventory before and during the manufacturing period. It is always desirable for a manufacturing system to create a pipeline kind of material acquisition, converting them into final product as soon as possible and delivering the goods to customers as soon as the production system converts them into finished goods. JIT-based production strategies are well known for their success in reducing inventory though implementing effective control policies at every stage of the inventory cycle. However, many operations managers treat inventory as a warranty for uninterrupted raw material supply to keep the expensive work centres busy all the time. However, inventory costs should be a concern for them also, in the effort to achieve cost effective and competitive production. Therefore, there is a trade-off between having costly inventory of both raw material and WIP and having uninterrupted production and lower costs of production. It is difficult to find the balance between cost and uninterrupted production, especially in highly complex automated systems. Financial performance metrics help managers to build this balance by considering the financial consequence of these operations management performance measures. If we assume that the periods for accounts payable and account receivables are the same no matter what shop floor strategy is implemented in the system, the performance metrics that create a difference for operating a manufacturing system are both raw materials and WIP inventories, production throughput, and production leadtime. JIT-based control systems are traditionally quicker than push systems in converting raw materials into finished goods. This implicitly covers the quick inventory turnover where pull-based systems cycle the inventory more than push system. This implies a shorter inventory cycle period than with push systems. Two other important operations management performance metrics are production throughput and production leadtime. These are the major indicators of how quickly a manufacturing system converts raw materials into final products to meet the timely demands of customers. However, this conversion cycle is heavily influenced by the MP&C strategy implemented. The queuing policy adopted by the control strategy for part dispatching is the main driving factor for these performance measures. These have been tested for the two pull-based control policies namely pure pull and CONWIP and the push system with different buffer sizes and four different initial part dispatching rules. The clear message of this research is that when we increase the input and output buffer sizes for work centres both operational and financial performance metrics deteriorate. This indicates that there is a strong relationship between the WIP and both operations and financial performance of the system. This has been proven by both the control policy implemented and the different buffer sizes. The pure pull system, which works without work buffer outperforms the other two control policies, which work with work buffers. Since an important portion of the CCC is the WIP element of the overall inventory conversion cycle, reducing or eliminating WIP on the shop floor gives a strong advantage to the production planning and control strategy that adopts this as a control policy. Therefore, primarily pure pull control and CONWIP have a considerable advantage on push control in creating a shorter inventory conversion cycle, which leads to a shorter CCC. When we increased the buffer size for CONWIP experimentation, this became so clear that every additional part in the WIP inventory contributes to a longer CCC. Therefore, a five-unit buffer size has a longer CCC than a three-unit buffer size and a seven-unit buffer size has a longer CCC than a five-unit buffer size. This is because the production leadtime is shorter with those policies, which work with small or zero buffer sizes and they are quicker than the other policies in converting the raw materials into finished goods by creating a more balanced supply processing flow. This virtually eliminates WIP. Another important observation is the effects of part dispatching rules on the financial performance of the manufacturing organization. It is observed that SPT is more successful than the other three dispatching rules tested in each one of the parameters that form the CCC. The explanation for this result would be that the SPT gives a better performance by creating shorter mean flow times and shorter production leadtimes, which may be translated into a shorter CCC. The next best dispatching rule is the SLACK rule and understandably it has a better performance in reducing tardiness in comparison to other dispatching rules and this may be translated into higher production throughput and eventually shorter CCC. It can be concluded that the JIT-based pull control strategies are quicker than push systems in converting the raw materials into finished goods as well as creating shorter CCCs. Also, as a result of this study, it has been shown that buffer size has a considerable effect on both operational and financial performance metrics and there is a strong correlation between the WIP size and the CCC in a manufacturing system.

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