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Advanced Techniques of Theory of Constraints and Activity Based

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Advanced Techniques of Theory of Constraints and Activity Based Advanced Techniques of Theory of Constraints and Activity Based Costing for Scheduling of High Technology Production Lines Stefano Apolloni*, Marcello Lando*, Matteo M. Savino** * Department of Design and Industrial Engineering, University Federico II of Naples, Italy ** Department of Engineering, Sannium University, Benevento, Italy Abstract One of the most important decisions that a manager should make is to determine a product mix to be scheduled able to maximise profits. To make right decisions he needs more accurate information about the optimal product mix and the restrictive bottlenecks of its company. The objective of this paper is to demonstrate as using the Activity Based Costing (ABC) approach together with the Theory of Constraints (TOC) philosophy we are able to locate an optimal solution to the product mix problem and the bottlenecks machines on the shop floor. A case study is provided to show the applicability of the proposed approach to a real case. Specifically we have analysed an aeronautical firm where, applying our methodology we have located a new layout able to reduce the total cost per unit during the amortization time. 1. Introduction The objective of this paper is to demonstrate as, using the Activity Based Costing (ABC) approach together with the Theory of Constraints (TOC) philosophy we are able to find an optimal solution to the product mix problem and, at the same time, to the optimisation of the production bottlenecks. Our contribution is a product mix decision model that uses activity based cost an theory of constraints information to improve the financial performance of a company. A case study is also given to show the applicability of the proposed approach to a real case. Specifically we have analysed an aeronautical firm where applying our methodology we have located a new layout able to reduce the total cost per unit by 34% during the amortization time. The proposed methodology will give managers more accurate information regarding the optimum product mix and critical bottlenecks of their companies. Infact By applying the TOC philosophy based on this information managers will be able to take the right actions that will improve the profitability of their companies. Specifically they will be able to observe the effects of several alternatives on the throughput of the whole system. In addition the proposed methodology should help managers to prevent making decisions that sub optimise the system. Th is may occur, for example, when the managers fix the productive capacity of a plant independently by its bottlenecks capacities. 2. Theory of Contraints and the effects of a bottleneck As succinctly put by Eliyahu Goldratt1 the primary goal of an enterprise is to make profit in the present and in the future but, in a manufacturing enterprise, there are many obstacles that prevents management from accomplishing this goal (the so called constraints/ bottlenecks). The Theory Of Constraints (TOC) is the management philosophy proposed by Goldratt that deals with managing these constraints; it is based on five steps focus on identifying the system constraints, exploiting it, subordinating the rest of the system to the needs of the bottleneck, improving the constraint and repeating the process continually. In a factory the bottlenecks are usually those machines or processes which control the throughput of the system; managing them – effectively and efficiently – yields higher system throughput. Many production control systems have been proposed to improve throughput in the past. Among them are the Materials Requirement Planning (MRP), Just-in-Time (JIT), Kanban, Constant Work in Process (CONWIP), and Drum-Buffer – Rope (DBR) systems. Successful manufacturing control procedures are required to identify and manage the system’s throughput, WIP, and cycle time where throughput is the number of final products produced per unit time by the system, WIP is the material within the system undergoing transformation into a final product, and cycle time is the average amount of time required for raw material to be transformed into a final product. An insufficient throughput leads, in fact, to unmet demand; an excessive WIP requires tying up excessive capital and an excessive cycle time leads to the loss of customer orders. In short, if any of these parameters are not managed properly, then the manufacturing centre loses money. These parameters are influenced by process variability, process time, process reliability, system bottlenecks, and the production control system used. The current manufacturing control systems may be classified into three categories. The first is MRP and its successor Manufacturing Resource Planning (MRPII). These control systems push materials into the production facility based on forecasted demand, and are thus known as push systems. In the second category of control systems, known as pull systems, the 1 Goldratt E.: “The Goal: A Process of Ongoing Improvement”, North River Press Inc., 1984 material is released into the production facility only when the demand for the end product tri ggers it. Since the material is released into the system only when it is needed, these control system are also called JIT systems. The two popular implementations of JIT control systems are Kanban (card) control systems and CONWIP control systems. The third category of control systems is mixed control systems. In these systems, the pull and push control systems are used to manage certain segments of the production line. There is a great amount of literature evaluating the performance of these systems. Cook demonstrates that serial production systems using DBR results in greater average throughput and lower levels of WIP variance than when the same system is managed by kanban2. Guide in the analysis of a re – manufacturing facility proves that DBR results in a reduction in WIP and throughput variance compared to MRP3. Bonvik et al. in the analysis of CONWIP, kanban, and pull-push production control systems demonstrates that the pull-push systems carries the lowest WIP at any particular throughput level with the kanban system generally carrying the highest WIP4. 3. Integrating Activity Based Costing (ABC) and Theory Of Constraints (TOC) ABC is a long term oriented analysis because it assumes that almost all of the costs of resources used in production are variable. However, in the short run, lot of them are fixed (for example the cost of labor). Another “weakness” of this theory is that it does not involve the system’s constraints so, especially in the short run, when, for example, the capacity of all the activities are fixed, an ABC analysis could be unreliable. TOC has, instead, a short time horizon because in the short run, being fixed the capacity of a plant, we have the bottleneck. ABC and TOC are, so, based on different time horizons; they have different hypothesis about labor and overhead costs and production capacity. Particularly, in the short run, labor and overhead costs can be assumed as fixed and TOC is able to give the right information, however, in the long run, all costs tend to be variable so ABC can reflect the expected costs in this time frame. In the end, since TOC and ABC are valid in different time horizons, they can complement each other. 2 Cook D.P.: “A Simulation comparison of traditional JIT an TOC manufacturing systems in a flow shop with bottleneck”; Production and Inventory Mangement, 1994 3 Guide V.D.R.: “ Scheduling using drum buffer rope in a remanufacturing environment”, International Journal of Production Research, 1996 4 Bonvik A.M., Couch C.E., Gershwin S.B.; “ A Comparison of production line control mechanism”, International Journal of Production Research, 1997 Before introduce the proposed model able to integrate ABC and TOC to determine the best pro duct mix that maximizes a company profit, we have to introduce a definition of the term capacity. The largest asset that any manufacturer has, which allows it to make products, is its capacity. Although there is no unique way to define capacity; in the following paragraph we categorise it as5 (see figure 1): theoretical capacity defined as the maximum output a plant can produce in a specific period; practical capacity defined as the theoretical one adjusted for lost time due to non working day, plant breakdown, repairs and maintenance; normal capacity defined as the average output of a plant over an extended period; budgeted capacity defined as the estimated one that will be utilised in a specific time period Figure 1: Definitions of capacity 4. Model Formulation One of the most fundamentals decisions that a company should make is the determination of the best product mix that maximises profits. Equation 1 reports the conventional approach to this problem: 5 Hill D, Kevin G, Glad E.; “Managing capacity”, Journal of cost management, 1994; Mc Nair C.J.; “ The hidden costs of capacity”, Journal of cost management, 1994 N Max!ƒ(si − mi − mani − ovr *si )*Xi i=1 subject to À N ƒ miXi ≤∈acq.mat i=1 N ƒ maniXi ≤∈manodopera i=1 + Xi ∈ N Equation 1: Conventional approach to a product mix problem Where: Xi = number of product i that is produced in a specific period N = kinds of products that can be produced in the company mi = material cost per unit of product Xi si = selling price of one product Xi mani = direct labour cost per unit of product Xi acq.mat = capital available for direct labour purchase manodopera = capital available for materials purchase ovr = overhead costs. We have calculated these by dividing the total overhead activity capacity by the total direct labour euros available Since the new technologies and the automation have increased the percentage of the overheads on the new productions, the application of an only cost driver could negatively influence the obtainable solutions; besides a lot of activities are characterised by an increase of their costs not proportional to the number of produced unity but to their uses and, so, theyor costs haven’t to be assigned to each unity of product.
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