Toplu üretim planlama ve bilgisayar destekli bir uygulama

Abstract

Günümüzde isletmeler yoğun bir rekabet ortamında -Faaliyet göstermektedir. İşletmeler rekabet güçlerini arttırmak ve daha ileri hedeflere ulaşabilmek için maliyetlerini en aza indirgemelidir. Aynı zamanda işletmenin kar maksimizasyonu, Fonksiyonun bir diğer boyutunu oluşturmaktadır. Dalgalanan talebi tümüyle karşılayamamak, stok bulundurmanın getirdiği ağır maliyetler ve gereksinmesindeki değişikliklerin yarattığı maliyetler, -Fonksiyonu baskı altında tutan başlıca kısıtlar arasında sayılmaktadır. Bu durumda, işletmelerin üretim planlama -Faaliyetleri giderek artan bir önem kazanmaktadır. üretim planlamanın etkinleştirilmesi için, destek girdilerinin sağlam bir veri tabanına dayandırılması ve en üstteki planlardan, akış sürecinde, iş emirlerine kadar aynı biline ve dikkatle uygulanması gereklidir. En önemli girdiler arasında sayılan talep tahminlerinin mümkün olduğunca isabetli sonuçlar sağlayan tekniklerle yapılması önemlidir. Bu nedenle, uzun dönemi kapsayan stratejik hedeflerin orta dönemli taktik kararlarla hayata geçirilmesi aşamasında Toplu üretim planlama, en iyi kaynak ve zaman kombinasyonu yaratma yöntemi olarak kullanılmaktadır.

Production planning is the process of thinking through the current mission of the organization and the current environmental conditions -Facing it, then setting -Forth a guide -For -future's decisions and results. Production planning is concerned with the determination o-F production, inventory and work force levels to meet -Fluctuating demand requirements. This process is based on three steps ; - long term strategic planning, - medium term tactic planning» - short term planning. Strategic planning is built on -Fundemantal concepts, that current decisions are based on -Future conditions and results, that strategic planning is a processthat it embodies a philosophy and that it provides a linkage or structure within the organization. In the production or operations -Function strategic planning is the broad, overall planning that preceeds the more detailed operational planning. Executives who head the production and operations function are actvely involved in strategic planning, developing plans that are consistant with the firm's overall strategies as well as such functions as marketing, finance accounting and engineering. Once developed, production and operations strategic plans are the basis -For operational planning of facilities and operational planning for the use of the facilities. There are many approaches to strategic planning. The key point we want to make is that operations strategies must be consistent with the overall strategies of the firm. Our observations lead us to the conclusion that operations typically utilize the overall corporate approach to strategic planning VI with special modificat ions and of course, a focus upon operations issues and opportunities» Production planning models can be classified conceptually into three main categories; aggregate planning models, functional interface models and hiearchical models. i ) Aggregate planning models focus on specifying the aggregate quantty to produce for a forthcoming planing horizon, typically one year. The solution methods used sre either exact or heuristic. ii) Functional interface models recognise the inter dependencies between production and the other functional areas of the firm, especially marketing and finance. iii) Hierarchical production planning models recognize the hierarchical nature of the process. They mostly focus on the interface between strategic and tactical level decisions. Routing production planning activities originate at the aggregate in nature and producers initiate actions based only on the first few periods of the plan. Plans are updated regularly as the demand and inventory situations changes. Usually, the physical resources of the firm are assumed to be fi>;ed during the planning horizon and the problem is to obtain the best plan in terms of resource utilization, given the external demand requirements. However, due to changes in demand, cost components, or capacity availability, the conditions affecting the production process will be unstable in time. Hence, in such cases, production should be planned in an aggregate way to obtain effective resource utilization. Aggregate is a term used to refer to total production reqirements, in contrast to requirements for a particular item or order. Aggregate production planning deals with the smoothing of aggregate production levels in order to prevent under and overload of fixed resources. The problem is determining period production rates and work force levels in order to minimize the firm's operating cost's over a multi- period planning horizon. Moreover, as discussed in Chapter 4. The aggregate must respond to expected demand fluctuations by; and layoff. i ) changing the work force size, hiring VI 2 ii) varying the production rate, allowing idle time, working overtime or relying on outside subcontract i ng, iii) accumulating seasonal inventories, iv) resorting to planned backlogs whenever customers may accept delays in filling their orders, v ) developing complementary product lines with demand patterns which are counter seasonal to the existing products, vi) adopting some combination o-F the above entries. Hax and Candea have stated the advantages o-F the aggregate approach as compared to a detailed one. These can be summarized as belows <12>, i ) Aggregations o-F products can significant ly reduce the costs o-F data collection to support the model, as well as the computational cost of running the model, ii) Since the decisions are based on the total production quantity demanded, this will improve the accuracy of the data, iii) Aggregation leads to more effective managerial understanding of the model's result. In general, the objective production planning is to select that combination of human and material resources which can most efficiently satisfy the anticipated demand for production output requirements while minimizing the related costs associated with the fluctuation of work force, inventories and other relevant decision variables such as overtime hours, subcontracting and capacity uti 1 izat ion. Customer demand enters the production system as units of products, hosever, production has to be planned as hours of machining and worker-hours of work that must be dedicated to the production of that demand. When planning work-force and related activities to service a given demand schedule, it is necessary to balance the cost of building and holding inventory against the cost of adjusting activity levels to fluctuations in demand. First alternative uses a Vlll constant work- Force level (i.e., constant production output rate). Since the production output rate is greater than the expected demand rate in the earlier- production periods, cumulative production will exceed cumulative demand, resulting in a significant inventory carrying cost. Conversely, significant shortage cost may incur when the cumulative demand exceeds the cumulative production. Second alternative is a strategy to produce to demand such that the inventory carrying costs are minimized. This alternative requires constantly adjusting the work- Force levels or paying significant overtime cost during the high demand periods. These are two extreme alternatives: the optimal alternative is the one that minimises the total costs of the inventory and the cost of adjusting the work-force level. In this study, we present approaches for computing aggregate plans that could respond the anticipated demand fluctuations while attempting to incur a minimum overall cost of production. The primary output of units to be produced during each period and the work-force levels required by period. The approach for finding the optimal alternative (master schedule and work-force level) is to develop a total cost function which contains the major cost component of the production facility. This cost function is to be minimized while subject to constraints. The linearity or nonl inearity of the cost function and constraints determine the solution approach to the problem. For example, linear programming can be used for solving aggregate production planning problems with a linear function and constraints. Other approaches are used when the functin or the constraints are nonlinear. Scheduling of aggregate production and work-force is a planning problem of primary importance to many manufacturing concerns. The aggregate plan responds to anticipated demand fluctuations while attempting to incur minimum overall cost of operations. This time- phased plan meets anticipated demand by modifying the work-force size, modifying work-force utilization, allowing inventory levels to var ry, adopt i ng some mixed or combinations of these three alternatives. The aggregate planning problems has received a great deal of attention over last two decades. Models have been developed for many special cases. However, IX all of these models, utilise a constant productivity factor; that is, the expected rate of output capability per employee is unchanging over time. It is known that the productivity rates of many organizations change with additional manufacturing experience. Empirical studies have demonstrated that an increased in productivity can be sistematical ly related to the cumulative output of the firm. This phenomenon can be quantifiably represented as an improvement curve, which incorporates the effects of learning and progress. The term improvement is usually applied to the general relationship between unit cost reduction and the cumulative number of units produced. The term learning is applied strictly to that portion of cost reduction which occurs without major method or design changes, and the term progress to the effect of those changes. The aggregate solution of the approach of this study, has minimized two costs; the production cost and the inventory carrying cost. Given the solution the only remaining costs to be considered as the set up costs incurred each time production is switched between products. With only two products to be considered, a simple approach to minimizing set up cost is to minimize the total number of setups scheduled over the planning horizon. One way to achieve this and thus obtain a disaggregated master schedule is to siulate production runs using the following simple decision rule: set up and produce one product until the other product's inventory runs out. Chapter 1 includes the terms, goals and applications of the production planning. The using frame of production planning varies according to types of production is determined in Chapter 2. Chapter 3 describes the steps and periods of production planning. In Chapter 4, the aggregate production planning is detailed with descriptions, different approaches in literature, inputs and outputs. Finally, the last part of this study, Chapter 5, concerns the computer use of the model which has been explained in the former chapters.