Revised MRP for reducing inventory level and smoothing order releases: a case in manufacturing industry

Transcription

1 Production Planning & Control, Revised MRP for reducing inventory level and smoothing order releases: a case in manufacturing industry Marco D Avino*, Valerio De Simone and Massimiliano M. Schiraldi Department of Enterprise Engineering Operations, Management Research Group, University of Rome Tor Vergata, Roma, Italy 5 (Received 27 July 2012; final version received 15 December 2012) Although material requirement planning (MRP) systems are widely used in manufacturing companies, they exhibit significant drawbacks. Rev MRP algorithm has been developed in order to reduce the system nervousness, generate a more regular pattern of order released and lower inventory levels. The aim of this paper is to test the Rev MRP algorithm on the case of one of the largest Italian manufacturing companies of home appliances. The company provided 10 three sets of real data, referring to three different runs of their MRP. Results showed that the Rev MRP outperformed the company s MRP smoothing the production order releases, eliminating overshoots and drastically reducing the inventory levels along the supply chain, even in case of frequent forecasts updates. Keywords: supply chain integration; MRP; procurement; inventory management; bullwhip effect; lumpy demand Introduction The material requirement planning (MRP) aim is to ensure the fulfillment of the demand by releasing a set of production/supplying orders for each item of the bill of materials (BOM) that allows to synchronize the inter- 20 nal and external logistics flows (Orlicky 1975). For this purpose, the basic calculation compute for each item a future projection of the inventory level by algebraically summing the stock at hand, the planned receipts and the gross requirements coming from the Master Production 25 Schedule (MPS): whenever the projected inventory level gets below a predefined level, a new procurement or production order is released. Although MRP systems are widely used, they exhibit significant drawbacks among which the most significant 30 is named nervousness, a term first coined by Steele (1975) and then analyzed in several papers (Mather 1977, Wemmerlov 1979, Vollman and Whybark 1988). The term refers to significant changes in the order planning, even in case of minor changes in the MPS or in 35 higher MRP levels that entail large inefficiencies such as excessive inventory, risk of a decrease in customer service, risk of lost revenues, misguided capacity plans and risk of missed production schedules (Lee, Padmanabhan, and Whang, 1997). In the last decades several ways have been suggested to reduce the MRP nervousness: knowledge sharing along the supply chain (Lee and Everett 1986) and management of delayed information (Iannone et al. 2006); freezing the schedule within the planning horizon or changing the cost procedure (Blackburn et al. 1986, Vollman and Whybark 2005); increasing forecasting horizon (Carlson et al. 1982); adopting specific lot sizing techniques, e.g. lot for lot (Inderfurth 1994, Vollman and Whybark 2005); exploiting safety stock (SS) (Vollman and Whybark 2005). All the identified solutions to MRP nervousness are not so efficient since they originate excess costs (e.g. inventory costs) and reduce the system flexibility (e.g. freezing). The Revised MRP algorithm (henceforth indicated as Rev MRP) aims to reduce the MRP drawbacks, with particular focus over its nervousness (Bregni, D Avino, & Schiraldi, 2011, D'Avino, Bregni, & Schiraldi, 2012). If compared with the traditional MRP, Rev MRP simulations showed considerable improvements such as a significant reduction of the system nervousness, a more regular pattern of order released and lower inventory levels. Furthermore the benefits of the Rev MRP are even more remarkable when the demand is highly irregular *Corresponding author. marco.d.avino@uniroma2.it Ó 2013 Taylor & Francis

2 2 M. D Avino et al. and when the supply chain is composed of several eche- 5 lons. The aim of this paper is to test the Rev MRP algorithm on the case of one of the largest Italian manufacturing companies of home appliances. Indeed, the effectiveness of the new algorithm in a real context is not obvious: sim- 10 ulations environments require many unrealistic assumptions, in particular those concerning the demand trend and the lot sizing rules. On top of the theoretical interest of evaluating the actual behavior of the Rev MRP on real industrial data, the company s management has already 15 expressed its interest in an eventual implementation of the algorithm on its information systems. This paper is structured as follows: a preliminary section introducing the specific case of analysis and the input data; a second section in which the Rev MRP is 20 briefly recalled and a third section comparing the results of Rev MRP runs with those of the MRP in use in the company, either in the case of exact forecast than in the case of frequent demand forecast. 2. The context 25 The analyzed manufacturing company has to cope with a highly variable demand. Specifically, some products may experience a lumpy demand, i.e. long periods of absence of requests and sudden surges. This situation entails a considerable forecasting effort and an extensive use of 30 stockpiling of external components, work in process items (WIP) and finished products, all the more so because the MPS cannot be completely controlled by the manufacturer (Ho, Kim, & Koo, 2001). Stockpiling significantly increases costs and thus affect the company 35 competitiveness, especially in the specific economic situation: indeed, the increased difficulties in accessing credit have made the lowering of working capital and the streamlining of the supply chain a primary target (Protopappa-Sieke and Seifert 2010). On top of this, uncertainty of the demand, forecast updating, order batching and other factors cause an amplified up-stream propagation of the down-stream demand fluctuations (Lee and Everett 1986, Chen et al. 2000). This worsen the situation, resulting in the well know bullwhip effect 45 (Forrester 1958). The company experienced serious problems in the management of material flows and therefore was looking for innovative solutions. Thus, the authors proposed to test the Rev MRP. The simulation and the benchmarking analysis con- 50 cern one finished product (a washing machine, WM) and a subset of items in its BOM, consisting of an external component (an electronic PCB) and a WIP item (the door of the WM): one PCB is assembled on each door and one door is required for the production of one WM. 55 The company provided three sets of data referring to three different runs of the company s MRP (in days 7, 12 and 17), each one including the updated WM demand forecast, the scheduled order receipts and the stock level, up to day 107, on a time horizon of 5 months. PCB supply lead time equals 12 days while 5 days are scheduled for door assembly. The company adopts a fixed order quantity (FOQ) lot sizing rule of 100 PCBs and of 16 doors, with the possibility of ordering multiple lots in the same period. Given the average value of WM s demand (i.e. 345 units), it is to note that the comparatively small size of the 16-units doors lot determines only a round-off of the released orders and therefore the system operates very similarly as if a lot-for-lot (L4L) lot sizing rule is applied. Figure 1 shows the differences among the abovementioned three different data sets concerning the WM demand forecast throughout the time horizon. In particular it is possible to observe sizeable changes between two consecutive forecasts, i.e. between two adjacent curves in the figure, although they have been generated only 5 days from one another. Some of the most remarkable differences between two consecutive demand forecasts concerning the same day are highlighted in the following figure through the percentages placed inside the callouts. For example, in Data Set 2 the forecast concerning the 25th day is reduced by 57% compared to Data Set 1. Those changes along with strict and inadequate lot sizing rules determine noteworthy problems for the entire supply chain such as urgently rescheduling and therefore nervousness, stockpiling and inefficiencies in general (Ho and Ireland 1998). Moreover it must be highlighted that the company holds a SS levels that are generated by increasing every released order of a fixed percentage value, i.e. 5% for PCBs and 3% for doors. This practice, although widespread, is not supported by any theoretical basis: as it is well known, the SS should be used to hedge from the uncertainty of demand and therefore should be computed as a function of its standard deviation and not only from its mean (Hadley and Whitin 1963). This also generates a wavering projected inventory, that further distorts the pattern of released orders. 3. A brief sketch of the Rev MRP algorithm The idea underlying the Rev MRP is to exploit the well known capacity of the L4L technique to reduce MRP nervousness (Lee, Padmanabhan, and Whang, 1997) but at the same time releasing orders adopting the more appropriate lot-sizing rule for that item. In fact, although the L4L is the most effective technique for reducing the system s nervousness, it is rarely the best solution in term of total costs because it entails the release of an excessive number of orders and therefore high ordering costs (Ho 1999). In order to present a clear explanation of the Rev MRP logic, the traditional MRP procedure is

3 Production Planning & Control 3 Figure 1. Comparison of different forecasts of the WM demand. 5 briefly recalled. Orlicky s MRP follows four sequential steps: (1) net requirements computation, considering gross requirements coming from the order releases of the previous echelon (i.e. the upper level of the 10 BOM) and inventory levels; (2) lot-sizing, using pre-defined criteria in accordance to the company s supply rules; (3) offsetting, considering the production/supplying lead time from planned order releases to planned 15 order receipts; (4) BOM explosion, i.e. go to next level of the BOM. For this purpose, the Rev MRP version adopted in the simulations presented in this paper consists of two 20 algorithms that operate in parallel for each echelon of the supply chain: a shadow routine (called simulated MRP ) computes order releases according to the original logic of the Orlicky s MRP, only using the 25 L4L rule. The net requirements computation is performed basing on gross requirements and simulated inventory levels, computed considering the orders released by the simulated MRP ; 30 then, a main routine (called main MRP ) takes in input these order proposals and releases the actual orders by merely aggregating them by implementing a pre-selected lot-sizing technique - in accordance with the company s supply rules 35 - and taking into account the actually production/supplying capacity. Only those orders lead the production/supply activities. In fact while traditional MRP system does not consider capacity constraints, and finite capacity planning has always represented a practical problem (Taal and Wortmann 1997), the Rev MRP natively considers those constraints and therefore releases only feasible plans. Figure 2 shows the Rev MRP functioning logic, which is repeated identically for each supply chain echelon. In particular, focusing on a specific echelon, the simulated MRP receives gross requirements from the previous echelon and releases simulated orders with a L4L lot sizing technique. These simulated orders are then aggregated by the main MRP according to the company s lot sizing criteria and afterward actually released, becoming the input for the next echelon. While the original version of Rev MRP (Bregni et al. 2011) autonomously performs the demand forecast by analyzing the past demand, the version presented in this paper has been adapted because of the company s need of using its own forecasts. Furthermore, the company has indicated the batch sizes to be used in the main MRP routine and therefore the algorithm has been adapted for this requirement as well. This adaption made possible the comparison between the company s MRP and Rev MRP, since the differences depend only on the algorithm s logics and not on the aggregation s logics. 4. Rev MRP vs traditional MRP At first, the differences between Rev MRP and traditional MRP behavior have been evidenced using each data set separately and therefore forecast updates have not been considered. This because forecasts updates are one of the major causes of the MRP nervousness (Kimms 1998) and so, in this way, this first comparison was performed in a case in which MRP could perform at its best, i.e. in an ideal case of exact forecast, without

4 4 M. D Avino et al. Figure 2. The Rev MRP logic. Figure 3. Orders released by company s MRP (left) and Rev MRP (right): data set 1. Figure 4. Orders released by company s MRP (left) and Rev MRP (right): data set 2. Figure 5. Orders released by company s MRP (left) and Rev MRP (right): data set 3.

5 Production Planning & Control 5 uncertainty. In spite of this, in Figures 3 5 it is possible to see how the company s MRP releases excessive orders with respect to the effective demand (i.e. overshoots), 5 while the Rev MRP shows a significantly more stable pattern of orders released. Specifically, each of the following figures shows the comparison between the company s MRP and the Rev MRP based on a different data set, and in all of these it is possible to observe that 10 the Rev MRP (on the right) has a more stable behavior than the company s MRP (on the left). The results shows that the Rev MRP is effective in smoothing the production order releases, mainly for lower level of the BOM, eliminating overshoots and 15 drastically reducing the inventory levels along the supply chain. In particular: irregularity in orders release, estimated by computing their standard deviation, is lowered by 27% for PCBs and by 1% for door items; 20 overshoots, estimated as the maximum order released, are lowered by 48% for PCBs and 1% for door items; inventory carrying costs, estimated by computing the average stock level, are lowered by 90% for 25 PCBs and 83% for door items; warehouses overall dimension, estimated by computing the maximum stock level, is lowered by 42% for PCBs and 93% for door items. Figure 6 summarizes the above-mentioned results by graphically comparing the performances achieved by the company s MRP and by the Rev MRP with respect to some key performance indicators (KPIs). In addition the improvements reached by the Rev MRP are highlighted in the figure through the percentages placed inside the ovals Comparison analysis with forecast updates Differently from what previously analyzed, considering forecast updates stockouts and sudden orders may arise. As a matter of fact, in the transitional period between two forecasts, the company experiences a demand that may differ from the one for which the orders currently in reception were released, because of forecasting errors. To protect from the demand uncertainty it is necessary to take advantage of the SS. In order to obtain a fair comparison of the Rev MRP and the company s MRP algorithm, in the Rev MRP the SS has been set equal to 10 units for PCBs and to 5 units for door items similarly to what has been set by the company. Figure 7 compares the order release patterns between Rev MRP and the traditional MRP, sequentially adopting each data set as the forecast updates of the previous one. In particular it is to note that the Rev MRP (on the right) avoids the release of sudden and excessive orders and is characterized by a Figure 6. Key performance indicators for company s MRP and Rev MRP in the case of no forecast updates. Figure 7. Orders released by company s MRP (left) and Rev MRP (right) by considering the forecasts updates.

6 6 M. D Avino et al. Figure 8. Comparison of inventory levels of PCBs and Doors. Figure 9. Key performance indicators for company s MRP and Rev MRP in the case of forecasts updates. much more stable behavior than the company s MRP (on 5 the left). Figure 8 clearly shows the differences in inventory management between the two algorithms and, above all, the significant improvements that the Rev MRP allows. In particular, in the upper section of the figure it is possi- 10 ble to observe the inventory trend of PCBs while in the lower section the inventory trend of doors. In both of these cases the Rev MRP outperforms the company s MRP by keeping a consistently lower inventory level. The numerical results confirmed that Rev MRP out- 15 performs the company s MRP even in case of frequent forecast updates. In this specific industrial case, it turns out to be even more effective in regularizing the order releases, avoiding overshoots and reducing the need of stockpiling in up-stream echelons. In particular: 20 irregularity in orders release, estimated by computing their standard deviation, is lowered by 19% for PCBs and by 6% for door items; overshoots, estimated as the maximum order released, are lowered by 86% for PCBs; inventory carrying costs, estimated by computing the average stock level, are lowered by 90% for PCBs and 84% for door items; warehouses overall dimension, estimated by computing the maximum stock level, is lowered by 73% for PCBs and 84% for door items. This allows the use of smaller warehouses, and helps in reducing the logistic costs for the whole supply chain. Figure 9 graphically represents the above-mentioned results comparing the company s MRP and the Rev MRP with respect to some KPIs. As in the previous Figure 6, the improvements reached by the Rev MRP are highlighted in the figure through the percentages placed inside the ovals. 5. Conclusions and future research Rev MRP proves to be a valuable option for a manufacturing company to reduce the MRP nervousness, outperforming the traditional MRP in real conditions in terms of regularity of order releases and of inventory level,

7 Production Planning & Control 7 therefore enabling a more efficient and cost-effective 5 management material flow, resulting in an increasing competitiveness for the whole supply chain. Despite the promising results, it is desirable to test the new algorithm in other industrial context, in order to verify its potentialities. 10 Notes on contributors Marco D Avino received his bachelor (2005), master (2007) and PhD in 15 Engineering and Management (2012) from the University of Rome Tor Vergata. In 2008 he obtained a master in Manufacturing Management from Linkoping Universitet. His areas of research include: MRP, 20 procurement, supply chain operations management, entrepreneurship. He has written several papers on MRP and supply chain integration. In the last five years he has worked for a top management consulting firm (Bain & Company). Valerio De Simone graduated with honours in Engineering and Management in 2012 at Tor Vergata University of Rome where he 30 is now attending an MBA and a PHD in Enterprise Engineering. His areas of research include: operations management, MRP, revenue management and demand forecasting. He works as a Consultant at Operations Management Team where he represents the technological promoter of the Rome Province for an innovative project on Healthcare System Optimization and is also founder and CEO of JoinJob, an ICT startup. Massimiliano M. Schiraldi is assistant professor in Operations Management at Tor Vergata University in Rome (Italy) since He received his PhD in Engineering 45 and Management in Every year he teaches to more than 200 students in the Engineering School. Up to now, he supervised more than 180 master thesis and 11 PhD students. He is the lecturer of 50 several courses in the field of Operations Management in various MBA programs. He is Guest Professor at the Guizhou University of Finance & Economics in Guiyang, China. He is the author of more than 60 scientific publications in Operations Management. 55 References Blackburn, J. D., D. H. Kropp, and R. A. Millen A comparison of strategies to dampen nervousness in MRP systems. Management Science 32 (4): Bregni, A., M. D'Avino, and M. Schiraldi A new 60 approach to lower MRP nervousness. p Vienna: DAAAM International. Carlson, R., S. Beckman, and D. Kropp The effectiveness of extending the horizon in rolling production scheduling. Decision Science (13), Chen, F., Z. Drezner, J. K. Ryan, and D. Simchi-Levi Quantifying the bullwhip effect in a simple supply chain: the impact of forecasting, lead times, and information. Management Science 46 (3): D'Avino, M., A. Bregni, and M. Schiraldi A revised and improved version of the MRP algorithm: Rev MRP. Advanced Materials Research. Forrester, J Industrial Dynamics: a Major Break Though for Decision-Makers. 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