Effective production control in an automotive industry: MRP vs. demand-driven MRP

Transcription

1 Effective production control in an automotive industry: MRP vs. demand-driven MRP Mohamad Jihan Shofa, and Wahyu Oktri Widyarto Citation: AIP Conference Proceedings 1855, (2017); View online: View Table of Contents: Published by the American Institute of Physics Articles you may be interested in Preface: The 3rd International Conference on Engineering, Technology and Industrial Application (ICETIA) AIP Conference Proceedings 1855, (2017); / Analysis of loss of time value during road maintenance project AIP Conference Proceedings 1855, (2017); / Performance of bulk oil circuit breaker (BOCB) influenced by its parameters (Case study at the substation of Bogor Baru) AIP Conference Proceedings 1855, (2017); / Optimizing cutting conditions on sustainable machining of aluminum alloy to minimize power consumption AIP Conference Proceedings 1855, (2017); / Holding time effect of pack carburizing on fatigue characteristic of v-notch shaft steel specimens AIP Conference Proceedings 1855, (2017); / Replacement model of city bus: A dynamic programming approach AIP Conference Proceedings 1855, (2017); /

2 Effective Production Control in an Automotive Industry: MRP vs. Demand-Driven MRP Mohamad Jihan Shofa 1, a) and Wahyu Oktri Widyarto 2 1, 2 Universitas Serang Raya, Jl. Serang-Cilegon, Banten, Indonesia a) Corresponding author: m.j.shofa@gmail.com Abstract. Material Requirements Planning (MRP) has deficiencies when dealing with current business environments, marked by a more complex network, a huge variety of products with longer lead time, and uncertain demands. This drives Demand-Driven MRP (DDMRP) approach to deal with those challenges. DDMRP is designed to connect the availability of materials and supplies directly from the actual condition using bills of materials (BOMs). Nevertheless, only few studies have scientifically proved the performance of DDMRP over MRP for controlling production and inventory control. Therefore, this research fills this gap by evaluating and comparing the performance of DDMRP and MRP in terms of level of effective inventory in the system. The evaluation was conducted through a simulation using data from an automotive company in Indonesia. The input parameters of scenarios were given for running the simulation. Based on the simulation, for the observed critical parts, DDMRP gave better results than MRP in terms of lead time and inventory level. DDMRP compressed the lead time part from 52 to 3 days (94% reduced) and, overall, the inventory level was in an effective condition. This suggests that DDMRP is more effective for controlling the production-inventory than MRP. INTRODUCTION The behaviour of business environment is very different from the previous years, shifting from deterministic to stochastic environment. In addition, fluctuating demand, long lead time, inaccurate forecast, huge variety of product, and complex network impact on many areas such as production planning and inventory control. Consequently, the current production planning and inventory control method has deficiencies [1]. As indicated by the results of a survey conducted by the Aberdeen Group (Fig. 1), supply chain complexity is rising due, in large part, to globalization. For the supply chain professionals, this means working with more global suppliers, reaching out more global customers, and dealing with the truly global competitors [2]. Such conditions become a challenge for companies especially with regard to production planning system. MRP as a production control system is appropriate for the deterministic environment [3]. So that, in a stochastic condition, some research proposed modified MRP to anticipate demands and lead time in a new normal environment to produce a good service level [4] [5] [6]. To cover the shortfall, a demand-driven material requirements planning (DDMRP) is offered to fill the gap [2]. DDMRP is a method which is able to adapt to an uncertain environment and the variablity of demands and supplies. Nevertheless, there are few studies on the benefit of DDMRP. It is indicated that there is an opportunity to compare MRP and DDMRP. This study fills this gap by evaluating and comparing the performance of DDMRP and MRP in terms lead time level and inventory level in the industrial world. This study focuses on part SA-12 that became a top-demand product during February January 2015, which is in common with the SA-22 and SA-02 parts. Green Process, Material, and Energy: A Sustainable Solution for Climate Change AIP Conf. Proc. 1855, ; doi: / Published by AIP Publishing /$

3 FIGURE 1. Top Pressure Forcing Companies to Supply Chain Applications [3] LITERATURE REVIEW MRP was first popularized by Joe Orlicky's first edition book in Figure 2 depicts MRP that has undergone various developments from MRP, closed-loop MRP, manufacturing resources planning (MRP II), advanced planning and scheduling systems (APS), and the enterprise resources planning (ERP). There are 5 inputs in MRP: Master Production Schedule (MPS), Bill of Material (BOM), item master, orders, requirements of items that are needed, and MRP combine them [7][8]. MRP is based on the supposition that the demand and lead times are deterministic. Unfortunately, most production systems are stochastic. Therefore, the deterministic assumptions of MRP are often too restrictive [9]. MRP s approach for uncertainties has been conducted. Under demand uncertainty, MRP combined with lot sizing [10], safety stock and safety lead time [11], while under lead time uncertainty, MRP approached with markov process and newboys model [12], brunch and cut algorithm [13], safety stock and safety lead time [11], periodic order quantity [9]. Under Inaccurate forecast, MRP approached with properly forecasting models [14]. Finally, traditional MRP is not relevant with the current environment. Companies that have experienced in MRP got chronic problems and the risk of high variation, overstock, and shortage in supply planning and customer demand. Such conditions impact on three main factors: inventory performance, service level performance, and high expedite related to expenses and waste [15]. FIGURE 2. Planning Tool Evaluation [2] The solution is to become an agile company, which consists of four distinctive competencies: cost, quality, dependability, and flexibility [1]. Companies are required to produce their products at low cost, high-quality products and services, short lead time and varied volume, and also improve the value of customer through customization. Demand-Driven MRP (DDMRP) is an approach to deal with those challenges. DDMRP can adapt in a stochastic environment and make an agile company. DDMRP was introduced by Carol Ptak and Chad Smith in 2011 in Orlicky's Material Requirement Planning 3rd Edition book. DDMRP was developed from Orlicky's MRP that is still relevant coupled with innovation in compressing lead time in the industrial world that refers to the demand-driven world [16]

4 Figure 3 indicates that DDMRP consists of a core of MRP, Distribution Requirements Planning (DRP) logic, Theory of Constraint (TOC), lean principles and innovations. From those, DDMRP develops more realistic product planning and inventory control method. FIGURE 3. Demand-Driven MRP [3]. DDMRP represents a change from the conventional idea of MRP. In DDMRP, demand is not defined by the statement "what we can and will build" but the statement "what we can and will sell [17]. As shown by figure 4, there are five steps of DDMRP: strategic inventory positioning, buffer profiles and levels, dynamic adjustment, demand-driven planning, visible and collaborative execution. These steps are devided into modelling or remodelling environment, plan, and execution. FIGURE 4. Five Components Demand-Driven MRP [3] Strategic inventory positioning is the first step in DDMRP. It considers where inventory should be placed. The six positioning factors are used to determine the initial positioning strategy, such as customer tolerance time, market potential lead time, demand variability, supply variability, inventory leverage and flexibility, and the critical operation protection. In strategic positioning, a realistic lead time called actively syncrhonized replenishment lead time (ASR lead time) is introduced, which is the core concept behind DDMRP. It is defined by the longest unprotected or unbuffered sequence in the BOM for a particular parent. In DDMRP, buffer profiles and levels provide an appropriate buffer position and strategic replenishment to reduce variability from both demand and supply. It is revised from the conventional inventory management in terms of the seasonal level inventory into three color-coded zones that comprise the total buffer. These zones are green, yellow, and red zone. Green represents an inventory position that requires no action; yellow represents a part which has entered its rebuild or replenishment; and red represents a part that is in jeopardy, which requires special attention [1]. Figure 5a shows the zone stratification in buffer stock, which is segmented in green (G), yellow (Y) and red (R) zones, and Fig. 5b shows the inventory effective-ineffective curve with overlaid color-coded zones and displays the meaning of each colored zone. This color coding system will be used in both planning and execution of priority management

5 (a) (b) FIGURE 5. (a) Moving to zone stratification in a stock buffer (b) Effective/Ineffective with buffer zone [3] To calculate each buffer profiles (G, Y, and R), the average daily usage/adu over the percentage of lead time is taken. The calculation follows some formulations, wherein: Green zone = ADU (Average daily usage) * Lead time * GZ Lead time factor Yellow zone = ADU * Lead time Red zone base = ADU * Lead time * RZ Lead time (1) Red zone safety = Variablity factor * Red zone base And, buffer zone is calculated: Top of red = Red zone Top of yellow = Top of red + Yellow zone (2) Top of green (TOG) = red zone base + red zone safety + yellow green + green zone. Dynamic adjusment is to adjust buffer profile to adapt company production planning to a dynamic environment. There are three types of adjusment: recalculated adjusment, planned adjustment, and manual adjustment. Demand driven planning is supply generation. It is based on what the available stock equation places the part. Open order is needed when parts stocked are available in the yellow zone (rebuild zone), wherein: Available stock = on hand + on order demand (pas due, due today, qualified spike) (3) To be able to define threat of spike to the inventory, order quantity spike threshold (OST) should be defined. OST is measured from the percentage of red zone. For this percentage, Ptak and Chad provide guidance with 50 percent of the red zone and OST is used for all stocked items [3]. As for the time, horizon becomes the criterion of order; spike is time where the environment can react rationally to changes that occur. Horizon spike order is usually defined in the lead time of the relevant part. The last is visible and collaborative execution. It is a challenge priority by due date. Priority by due date gives the real day-to-day inventory and materials priorities. Priorities are dynamic. They change as varibility and volatility occur within the time when they are opened until they are closed. RESEARCH METHODOLOGY Figure 6 displays the steps of the research. In order to compare the effectiveness of MRP and DDMRP, this research simulation was conducted for 4 weeks. There are 2 indicators observed in terms of effective inventory: lead time and level of inventory. An automotive company in Indonesia was taken as the context of the case study. This study focuses on part SA-12 that becomes a top-demand product during February January 2015, which is in common with the component parts of SA-22 and SA-02. Input data were analyzed in the period March-April

6 FIGURE 6. The step by steps of the research Input Parameters We simulated two systems, MRP and DDMRP, for 4 weeks. Some input scenarios were given, as shown in Table 1, that consist of forecasts and variations. The forecasts were given by the company and variations were applied to make a stochastic environment. This research investigated 3 parent parts: SA-12, SA-22, and SA-02. Fig. 7 shows the bill of materials and lead time of the 3 parent parts. Differentiation between parent part SA-12 and the 2 other parts is just on the component part BTW. Symbol I is for inhouse process and P is for purchased part. TABLE 1. Input Parameters FIGURE 7. Bill of Material and Lead Time (day) of Part SA22, SA-12, SA-02 Production Planning: MRP Vs DDMRP From the forecasts, BOMs and lead time data, MRP is generated. The lead time for 3 parts using MRP method is the longest leg of the BOM (SA IPC RC PT PB); it is 52 days. That means that safety stock for position SA- 12 will need to account for forecast variability over 52 day time frame

7 In MRP implementation, we investigated the demand and inventory on hand condition. Figure 8 shows that the on-hand conditions were stockout for part SA-12 and SA-02 in several period. (a) (b) (c) FIGURE 8. On-Hand Condition of Parent (a) Part SA-12, (b) SA-22, and (c) SA-02 Using MRP Method After that, DDMRP method is simulated. Firstly, remodelling of strategic inventory positioning, buffer profiles and levels and dynamic adjusment was done before generating the production planning system. Using strategic replenish position, the lead time for parent part SA-12 was decoupled from the supplier lead time since all purchased parts were replenished. So, ASR lead time for parent part SA-12 is 4 days. Additionally, the component rod took a replenished position because it is worth the inventory investment. Finally, ASR lead time of parent SA-12 could be reduced to 3 days. TABLE 2. DDMRP Parameter Table 2 shows that DDMRP Parameter was based on the remodelling of DDMRP method. ADU was calculated from the forcastst. Green Zone (GZ) lead time and Red Zone (RZ) lead time were stated based on data converted in percentage. Variability factor and initial state inventory were based on actual data. From the ADU and ASR lead time, buffer profile can now be investigated as displayed (Table 3)

8 TABLE 3. Buffer Profile and Levels of DDMRP Then, simulation using DDMRP was applicated. Fig. 9 shows DDMRP result of on hand, which is no stock-out for on-hand inventory. (a) (b) (c) FIGURE 9. On-Hand Condition of Parent (a) Part SA-12, (b) SA-22, and (c) SA-02 Using DDMRP Method FINDINGS Based on the simulation, DDMRP gives a better effect than MRP. DDMRP can compress lead time, no stock-out condition and improve the inventory level. DDMRP compresses the lead time part from 52 to 3 days (94% reduced). Figure 10a displays the inventory level of part SA-22 using MRP 25 percent on the red zone (little), 22 percent on the yellow zone (effective), 13 percent on the green zone (effective), and 41 percent on the blue zone (overstock).meanwhile, no inventory using DDMRP is on the red zone, but it shifted to 78 percent on the yellow zone and 22 percent on the green zone. Figure 10b displays the inventory level of part SA-12 using MRP, which is 100 percent on the red zone. However, the inventory level using DDMRP is 16 percent on the red zone, 63 percent on the yellow zone, and 22 percent on the green zone. Figure 10c displays the inventory level of part SA-02 using MRP, which is 97 percent on the red zone and 3 percent on the yellow zone, while using DDMRP method, it is 13 percent on the red zone, 78 percent on the yellow zone, and 9 percent on the green zone

9 (a) (b) (c) FIGURE 10. Comparison MRP-DDMRP in daily available stock (a) part SA-22, (b) part SA-12, and (c) part SA-02 CONCLUSION This study aims to evalute the performance of DDMRP and MRP in terms of the level of effective inventory. DDMRP compresses lead time and improves the inventory level. DDMRP compresses the lead time part from 52 to 3 days (94% reduced) and shifted the inventory level for the three parts to the effective stock. So, DDMRP is a more effective production-inventory control than MRP. This research has proposed a model of how to realize the production planning with DDMRP, which still uses Microsoft Office Excel software, so that they can be developed into a program that is able to be used properly and user friendly. The results of this study need to be followed by various studies, such as measurement of effectiveness in the aspect of the benefit cost of inventories, and analyses of the problems faced and what strategies are needed while implementing DDMRP. ACKNOWLEDGEMENT We are most grateful to anonymous reviewers for their constructive and insightful suggestions and comments at various stages of this manuscript that helped us improve the presentation of the paper considerably. We appreciate the contributions of the company whose participation made this research possible. REFERENCES 1. C. A. Ptak and C. J. Smith, Orlicky's material requirements planning 3rd edition. (Mc Graw Hill, New York, 2011) p Nari Viswanathan, Enabling supply chain visibility and collaboration in the cloud (Aberdeen Publication, 2010) p V. Suresh, R. Sivasubramanium, The implementation of material requirement planning techniques in crane hook assembly, International Journal of Applied Engineering Research, Volume 5, Number 8, pp (2010)

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