A comparison of production policies in remanufacturing systems

A comparison of production policies in remanufacturing systems

M. GALLO, R. GRISI, G. GUIZZI, E. ROMANO Department of Materials Engineering and Operations Management

University of Naples “Federico II” Piazzale Tecchio – 80125 Napoli

ITALY [email protected] http://www.impianti.unina.it

Abstract: In remanufacturing systems material management and production planning, unlike what happens in traditional production settings, are made difficult by a wider uncertainty affecting the quantity, the quality and the timing of recovered products/components. This instability makes the problem of defining an optimal inventory control policy more complicated as well as not unique within the product life cycle. In this article, after having reviewed the existing literature on inventory management in remanufacturing systems, a particular policy for material management and production planning in a hybrid remanufacturing system has been proposed. The considered model a multi stage inventory model. The comparison of the proposed policy with some others already present in literature, in different scenarios related to the product life cycle and with the change of several cost parameters, is carried out using the discrete event simulation approach. This allows us to identify in what circumstances such a policy is preferable to the others. Key-Words: Remanufacturing systems, Production Planning, Inventory Control, Computer Simulation 1 Introduction The increasing technological innovation rate of products is pushing toward new profit models, based on an integrated product life cycle management. In fact the innovative policies oriented to recover products on the one hand improve the efficiency in natural resources consumption, but on the other hand show new business opportunities to original equipment manufacturers. Among the different recovery options, remanufacturing is an important and interesting one. Remanufacturing plants show a high degree of uncertainty and complexity compared to the traditional production processes. Planning and control systems have a particularly critical role because of the management of these issues. Remanufacturing firms, in fact, have to tackle a number of problems that limit the efficiency of the production process and that can’t be addressed with the traditional tools of planning and control, being typical of this particular sector. The present work addresses the tactical decision-making problem concerning the selection of the optimal manufacturing/remanufacturing policy, adequately coordinating the various activities and the various inventory levels in the production system. This choice is not univocal within the product life cycle but is strongly influenced both by external factors (recovery rate of products and parts, demand rate) and by the product’s cost structure. The comparison of different policies will be made considering some cost factors (holding, shortage, disposal, manufacturing, and purchase cost). The analysis of the system’s performance, as the considered factors change, will be carried out through the discrete event

simulation approach and the optimization of the model parameters will be done through a meta-heuristic technique. The remaining part of the article is organized as follows. After a review of the existing literature on inventory management models in remanufacturing systems, the logical model of the system under consideration is presented, its assumptions and the management policies considered. After the system’s performance analysis, conclusions and possible developments are outlined. 2 Literature review To address the problems of inventory management in remanufacturing systems, it is possible to consider models that provide a system’s schematic representation which is usually simplified. According to the underlying assumptions, it is possible to distinguish between deterministic and stochastic models. The inventory models can be divided into three categories:

• cash flow balancing models; • periodic review models; • continuous review models.

In the following we will only focus on the second and third category of models, while a thorough examination of the first category can be found in [3]. 2.1 Periodic review models Kiesmuller and van der Laan in [5] analyzed a remanufacturing system for a single product, considering that the products demand and return rate are random variables. The product recovery depends on the demand flow, there is only one warehouse for finished products,

Proceedings of the 8th WSEAS International Conference on SYSTEM SCIENCE and SIMULATION in ENGINEERING

ISSN: 1790-2769 334 ISBN: 978-960-474-131-1

lead-times of the product are taken in account and a finite planning horizon is considered. A product is disposed, with a certain probability, only if it doesn’t satisfy the quality requirements for remanufacturing (unplanned disposal). The study shows that not taking into account the dependence between demand and recovery of products may lead to an increase in the average total costs of the system. B. Mahadevan et al. in [7] propose a PUSH policy based on a periodic review for the management of a remanufacturing hybrid system in which both the demand and the recovery of the products are random variables. The aim is to identify the lot size for remanufacturing and manufacturing. Since the optimal policy parameters can’t be determined analytically, the authors develop heuristic procedures based on classical models of inventory management, evaluating the system performances as the return rate, the backorder costs and the manufacturing and remanufacturing lead-times change. 2.2 Continuous review models An early study by van der Laan et al. [11] concerned a remanufacturing system for a single product, with a single warehouse for the serviceable inventory. The recovered products in excess are disposed and it’s assumed that all products meet the quality requirements for remanufacturing. The demand and the return are stationary independent Poisson random variables. If the demand is not met a shortage cost occurs because of the backordered sales. The comparison of different inventory control strategies shows that to minimize the total cost of the system, the disposal must occur when the available cores exceed a threshold value or when the number of products waiting to be remanufactured reaches a specific value, depending on the remanufacturing process capacity. Starting from the same model, van der Laan et al. in [12] proposed a strategy with a planned remanufacturing and disposal of the products and a heuristic optimization procedure to control the inventory. Later, van der Laan and Salomon in [13] considered a hybrid system with an additional warehouse for remanufacturable inventory. The product demand and return are dependent random variables and the manufacturing and remanufacturing lead-times are constant. Two alternative strategies are compared: Push-disposal and Pull-disposal. It is showed that systems with product disposal have lower costs because of the reduction in the system’s stock variability. Some studies testing the effects of the duration and variability of the lead-time on the system total expected cost, using a Push or Pull management strategy without disposal, show that in some cases an increase in remanufacturing lead time can lead to a decrease of the total costs of the system [14]. However a more effective control of the finished products warehouse is possible using two variables whose evolution

is linked to manufacturing and remanufacturing lead-times. Using this approach, the total cost of the system may decrease especially for large differences between these lead-times [6]. In case of stochastic lead times, the use of a dual sourcing ordering policy, where each order is split between the production and remanufacturing processes, could lead to a significant reduction in system costs [9]. Fleischmann et al. in [1] analyze several control policies and the influence of the return flow on the system, considering the demand and the return of products as independent random variables and with a single warehouse to check. For the same system is then proposed an optimization algorithm to compute the control parameters for a order up to level policy [2]. Teunter and Vlachos in [10] considered a hybrid system, with a finite planning horizon and controlled by a Pull order-point policy. They showed that if the demand rate is on average higher than the return rate and if the remanufacturing process is cost effective compared to the production process, the planned disposal option results only in an increased complexity of the system, with significant cost reductions occurring only when the return rate is higher than the demand rate. Closed-form expressions to optimize the control parameters of a Push and a Pull inventory management policy in hybrid remanufacturing systems are proposed by van der Laan and Teunter in [15]. Takashi et al in [8] have modeled a hybrid remanufacturing system, where the warehouse of remanufacturable products is analyzed at a higher level of detail. The recovered products are disassembled, according to a stochastic process, and classified in: waste to be disposed, raw materials to be sent to the production of new parts and parts for the manufacturing of new products. To manage the system, the Authors propose two Push control policies, that are compared using calculation procedures based on Markov chains. In the first policy, the production of new parts and their recovery are independent processes. This could lead to an excessive disposal of the parts. The second policy aims at improving the control of the parts in the system by introducing a second control limit to the part production process. As several cost factors change, it is shown that controlling the system with the Push 2 policy produces total cost always lower. 3 Logical and analytical model with underlying assumptions We have considered a logical model of the hybrid remanufacturing system with multiple levels of stocks similar to that proposed by Takashi et al in [8]. This model, in fact, considers various warehouses corresponding to different types of stocks that normally occur in the systems


ISSN: 1790-2769 335 ISBN: 978-960-474-131-1

for remanufacturing. For this system we want to identify in different scenarios related to the various stages of a product's life cycle, the most effective control policy as several cost parameters change. In particular, the performance of a particular Pull policy will be compared with that of the Push policies proposed by Takashi et al in [8]. 3.1 Logical model For the considered model (Figure 1), we assume that there is no difference between newly manufactured products and remanufactured products. The demand, the recovery, the assembly and the production process are modeled as Poisson process with parameters, respectively: λd, λr Mp Mpa. From the disassembly process we can obtain a unit of raw material, a part, or a part and a unit of raw materials, with a probability, respectively: λm, λp, λpam. Product Disposal occur with a rate equal to λdis. We don’t consider any lead-time for raw material.

Figure 1 – Hybrid Remanufacturing System logical model

The Pull policy proposed in this paper will be compared with the Push ones proposed by Takashi et al in [8]. According to these policies the procurement, the production and the assembly processes are regulated by the levels of the corresponding warehouses in the system that represent, also, the control parameters of the system. The second Push policy presents an additional control parameter that tries to coordinate the production of parts with their recovery from recovered products. The Pull policy proposed here aims to further extend the coordination level between the various activities within the production system and to link these activities to the presence of an external demand. In particular, while for the assembly process we adopt the same activation logic used in the previous policies, the parts production process is activated only in presence of an external demand and if remanufactured parts are not available; the purchase process is activated only when the warehouse of raw material is empty (their is no recovered material) and it is required to activate the production of a part (there are no recovered parts and a demand for a finished product occurs). The PULL policy under

consideration has the obvious advantage of being able to control the part production process without introducing an additional control variable as the PUSH 2 policy does. 4 Analysis of the simulation model The simulation models, one for each policy considered, will be compared in different scenarios and for different values of the significant system costs using the total cost function in optimal conditions. 4.1 Definition of the scenarios and choice of the values for model parameters The various policies have been compared in three scenarios related to different stages of the life cycle of a remanufacturable product and characterized by different values for the ratio between the demand rate and the return rate. In particular in the first scenario the demand rate is greater than the return rate (launching a new product), in the second scenario these rates are equal (maturity stage of the product) and in the third scenario the demand rate is lower than the return rate (Table 1). Table 1 – Demand and return rate for the three scenarios

λd λr Scenario 1 8 6 Scenario 2 6 6 Scenario 3 4 6

The values of the model parameters are based on data grounded in real remanufacturing environments from the existing literature in particular considering electric and electronic products. As regards the features of the return flow: the materials recovery rate, the part recovery rate and the disposal rate, remain constant in the three scenarios (Table 2). Table 2 – Fixed parameter of the system

Mp=9 Mpa=8 λpa=2 λm=2 λpam=1 λdis=1 Cp=10 Cb=5

The cost factors considered here are the holding cost for the three stores, the shortage cost, the disposal cost and the production cost of parts. The purchase cost and the assembly cost are considered constant (Table 2). We used, for an initial exploratory study, an OFAT experiment strategy (one factor at a time): starting from a basic setting and varying one by one the various factors considered. The cost parameters values for the basic setting are shown in Table 3.

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Material

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Recovery

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Demand


ISSN: 1790-2769 336 ISBN: 978-960-474-131-1

https://www.researchgate.net/publication/223123292_Inventory_control_for_a_MARKOVIAN_remanufacturing_system_with_stochastic_decomposition_process?el=1_x_8&enrichId=rgreq-a9d79671-5b50-46a6-8dc8-cdbe7f831309&enrichSource=Y292ZXJQYWdlOzIyOTA0ODAwNDtBUzo5NzMzNzYwMzAwMjM3OEAxNDAwMjE4NTAxODk5

Table 3 – Cost parameters values for the basic setting Holding Cost (material, parts, products) hx=0.5 hy=0.7 hz=1

Disposal Cost Cd=20 Production Cost Cpa=5

Shortage Cost Cr=20

We used an experimental design with 16 (5+4+4+3) experiments being the four cost parameters considered respectively a 5, 4, 4 and 3 level factor (Table 4). Table 4 – Experimental design (basic setting in bold)

Fattore Livelli Holding Cost (%) 10 15 20 25 30

Disposal Cost 20 30 40 50

Production Cost 5 10 15 20 Shortage Cost 20 30 40

Note that the holding cost in Table 2 is expressed as a percentage of the total cost. Each experiment has been repeated for each policy considered (Push 1, Push 2 e Pull) and for the three scenarios, performing an amount of 144 experiments (16x3x3). 4.2 Simulation results From the analysis of simulation results we can confirm the non-existence of a single optimal manufacturing / remanufacturing policy for all stages of a product life cycle. This is the reason for which the results analysis will be conducted separately for the different scenarios. We must consider also that in each scenario the system sensitivity to the different cost parameters is very different, confirming the dependence of the assessments made on the type of product taken into consideration. 4.2.1 Scenario I From figure 2 it can be argued that in the first scenario the Push 1 policy shows cost values higher than those of the two other policies, due to the greater amount of parts kept in stock and disposed products, not being present any control on the parts production process. Moreover there is a limit value for the holding cost (between 15% and 20%) beyond which the Pull policy starts to become more convenient than the Push 2 policy. However, the percentage difference between the two policies is not high as in scenario I the inventory level of material, parts and finished products is low. When the holding cost reaches 30% of the total cost, the optimal solution for the three management policies is to keep only one unit at each of the three stores. In such a case it is preferable to adopt a RATO (ReAssemble To Order) system configuration, keeping in stock only part and material, or a RMTO (ReManufacture To Order) configuration, deleting the system inventory. Choosing a policy rather than another affects the delivery time to the customer: in our model there is no capacity backorder.

Figure 2 - Total cost variation with the holding cost in the first Scenario Figure 3 shows that in the first scenario both the Push 2 and the Pull policy are able to control well the disposal cost as they control the production process in a more efficient way.

Figure 3 - Total cost variation with the disposal cost in the first Scenario

As the production cost increases, the Pull policy is more efficient than the Push 2 policy even if the percentage difference is low (Figure 4).

Figure 4 - Total cost variation with the production cost in the first Scenario

The variation of the total cost with the shortage cost (Figure 5) denotes that in the first scenario the Push 2 policy performs better than the Pull one. Note that the difference between the two policies increases with the shortage cost indicating that the Pull policy is more sensitive to this cost. This situation could be explained considering the low return rate of the first scenario and the long lead times of the Pull policy.

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ISSN: 1790-2769 337 ISBN: 978-960-474-131-1

Figure 5 - Total cost variation with the shortage cost in the first Scenario 4.2.2 Scenario II Figure 6 depicts the total cost variation with the holding cost in the second scenario. Also in this case the Push 1 policy produces operating costs higher if compared to the other policies. However, the threshold value for the holding cost, beyond which the Pull policy becomes more convenient than the Push 2 policy, is lower (between 10% and 15% of the total cost), moreover it becomes more evident the percentage difference between the two latter policies. Such a trend is justified by the fact that the demand rate is equal to the return rate and, therefore, as the holding cost increases it is preferable to adopt policies that reduce the amount of stocks.

Figure 6 - Total cost variation with the holding cost in the second Scenario

Figure 7 reports the influence of the disposal cost on the total cost in the Scenario II. As the disposal cost increases, the pull policy proves to be more efficient compared to the Push 2policy even if the percentage differences are not so high. The slope of the three curves is greater if compared with that of the previous scenario: the increased return flow produces an higher number of products to be disposed of.

Figure 7 - Total cost variation with the disposal cost in the second Scenario

With the production cost increasing, the Pull policy become more efficient than the Push 2 policy. The point of intersection between the two curves has shifted more to the left if compared to the first scenario (Figure 8). Moreover, the percentage difference between the two policies is more apparent, especially because a Pull policy allows to take advantage of the higher return rate in the second scenario.

Figure 8 - Total cost variation with the production cost in the second Scenario

Also in the second scenario the Push 2 policy performs better than the Pull one (Figure 9), but in this case the difference between the two policies is smaller and the Pull policy is less sensitive to the shortage cost increasing. This is essentially due to the higher return rate than in the first scenario.

Figure 9 - Total cost variation with the shortage cost in the second Scenario 4.2.3 Scenario III In the third scenario, whatever the holding cost value is, the Pull policy produces cost values lower than those of the other two policies; moreover the percentage difference between the Pull policy and the Push 2 is higher than in the second scenario. In the this last scenario the demand rate is lower than the return rate, so the Pull policy, reducing the system inventory, allows to better manage the amount of returns in excess. Also when the disposal cost increases, the Pull policy performs better than the Push 2 policy and the percentage differences between the two policies are still greater than in the second scenario. The slope of the three curves still keeps growing compared with the previous scenarios: because of

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ISSN: 1790-2769 338 ISBN: 978-960-474-131-1

the higher return rate, it’s greater the number of products to be disposed of. Also with reference to the production cost, the Pull policy shows a similar behavior. Such a result is due to a better management of the production process: a new part is produced only if it is required and a recovered part is not available. Unlike the previous scenarios, in the last one the Pull policy performs better than the Push 2 policy, but the difference is low and decreases with the shortage cost increasing, highlighting the convenience of considering on order policies. 6 Conclusions In the present paper the problem of inventory management in remanufacturing settings has been analyzed. A Pull policy, alternative to some Push policies existing in literature, has been proposed to better manage the material in stock. In order to compare these policies some simulation experiments were carried out on a model of a hybrid remanufacturing system considering three possible scenarios related to the life cycle stages of a remanufacturable product. The simulation results show that the definition of an optimal inventory management policy in remanufacturing systems is not univocal, in fact different control policies have to be adopted at different stages of the product life cycle. Moreover, the optimal control policy depends on the percentage incidence of the holding cost, the disposal cost, the production cost and the shortage cost. A further development of this work could be to investigate the possible interactions between the significant cost factors and to determine the ranges of convenience of the various policies considering such interactions. Another possible improvement of this research could be the introduction in the system model of a backorder capacity in order to take into account customer service level when considering on order policies. References: [1] Fleischmann M., Kuik R., Dekker R., Controlling

inventories with stochastic item returns: A basic model, European Journal of Operational Research, Vol. 138, No. 1, 2002, pp. 63-75.

[2] Fleischmann M., Kuik R., On optimal inventory control with independent stochastic item returns, European Journal of Operational Research, Vol. 151,No. 1, 2003, pp. 25-37.

[3] Inderfurth K., Optimal policies in manufacturing/remanufacturing systems with product substitution, International Journal of Production Economics, Vol. 90, No. 3, 2004, pp. 325-343.

[4] Kelton W.D., Sadowski R., Sadowski P., Simulation with Arena (2nd ed.), McGraw-Hill, 2001.

[5] Kiesmüller G. P., van der Laan E., An inventory model with dependent product demands and returns, International Journal of Production Economics, Vol. 72, No. 1, 2001, pp. 73-87.

[6] Kiesmuller G. P., A new approach for controlling a hybrid stochastic manufacturing/remanufacturing system with inventories and different leadtimes, European Journal of Operations Research, Vol. 147, No. 1, 2003, pp. 62-71.

[7] Mahadevan B., Pyke D. F., Fleischmann M., Periodic review, push inventory policies for remanufacturing, European Journal of Operations Research Vol. 151, No. 3, 2003, pp. 536-551.

[8] Takahashi, K. Morikawa, K., Takeda, D., Inventory control for a Markovian remanufacturing system with stochastic decomposition process, International Journal of Production Economics, Vol. 108 (1-2), 2007, pp. 416-425.

[9] Tang O., Grubbström R. W., Considering stochastic lead times in a manufacturing/remanufacturing system with deterministic demands and returns. International Journal of Production Economics, Vol. 93, No. 1, 2005, pp. 285-300.

[10] Teunter R. H., Vlachos D., On the necessity of a disposal option for returned items that can be remanufactured, International Journal of Production Economics, Vol. 75, No. 3, 2002, pp. 257-266.

[11] van der Laan R., Dekker E., Salomon M., Ridder A., An (s, Q) inventory model with remanufacturing and disposal, International Journal of Production Economics, Vol. 46-47, No. 1, 1996, pp. 339-350.

[12] van der Laan E., Dekker R., Salomon M., Product remanufacturing and disposal: A numerical comparison of alternative control strategies, International Journal of Production Economics, Vol. 45, No. 1, 1996, pp. 489–498.

[13] van der Laan E., Salomon M., Production planning and inventory control with remanufacturing and disposal, European Journal of Operational Research. 1997, Vol. 102, No. 2, pp. 264-278.

[14] van der Laan E.A., Salomon M., Dekker R., van Wassenhove L., Inventory control in hybrid systems with remanufacturing, Management Science, Vol. 45, No. 5, 1999, pp. 733-747.

[15] van der Laan E.A., Teunter R.H., Simple heuristic for push and pull remanufacturing policies, European Journal of Operational Research, Vol.175, No. 2, 2006, pp. 1084-1102.


ISSN: 1790-2769 339 ISBN: 978-960-474-131-1

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A comparison of production policies in remanufacturing systems