Revised MRP for reducing inventory level and smoothing

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TPPC 764579,19 January 2013 Initial,2 M D Avino et al. and when the supply chain is composed of several eche 12 and 17 each one including the updated WM demand. 5 lons forecast the scheduled order receipts and the stock level. The aim of this paper is to test the Rev MRP algorithm up to day 107 on a time horizon of 5 months PCB sup. on the case of one of the largest Italian manufacturing ply lead time equals 12 days while 5 days are scheduled 60. companies of home appliances Indeed the effectiveness for door assembly The company adopts a xed order. of the new algorithm in a real context is not obvious sim quantity FOQ lot sizing rule of 100 PCBs and of 16. 10 ulations environments require many unrealistic assump doors with the possibility of ordering multiple lots in. tions in particular those concerning the demand trend and the same period Given the average value of WM s. the lot sizing rules On top of the theoretical interest of demand i e 345 units it is to note that the compara 65. evaluating the actual behavior of the Rev MRP on real tively small size of the 16 units doors lot determines. industrial data the company s management has already only a round off of the released orders and therefore the. 15 expressed its interest in an eventual implementation of the system operates very similarly as if a lot for lot L4L. algorithm on its information systems lot sizing rule is applied. This paper is structured as follows a preliminary sec Figure 1 shows the differences among the above 70. tion introducing the speci c case of analysis and the mentioned three different data sets concerning the WM. input data a second section in which the Rev MRP is demand forecast throughout the time horizon In particu. 20 brie y recalled and a third section comparing the results lar it is possible to observe sizeable changes between. of Rev MRP runs with those of the MRP in use in the two consecutive forecasts i e between two adjacent. company either in the case of exact forecast than in the curves in the gure although they have been generated 75. case of frequent demand forecast only 5 days from one another Some of the most remark. able differences between two consecutive demand fore. casts concerning the same day are highlighted in the. 2 The context following gure through the percentages placed inside. 25 The analyzed manufacturing company has to cope with a the callouts For example in Data Set 2 the forecast con 80. highly variable demand Speci cally some products may cerning the 25th day is reduced by 57 compared to. experience a lumpy demand i e long periods of absence Data Set 1 Those changes along with strict and inade. of requests and sudden surges This situation entails a quate lot sizing rules determine noteworthy problems for. considerable forecasting effort and an extensive use of the entire supply chain such as urgently rescheduling and. 30 stockpiling of external components work in process therefore nervousness stockpiling and inef ciencies in 85. items WIP and nished products all the more so general Ho and Ireland 1998. because the MPS cannot be completely controlled by the Moreover it must be highlighted that the company. manufacturer Ho Kim Koo 2001 Stockpiling sig holds a SS levels that are generated by increasing every. ni cantly increases costs and thus affect the company released order of a xed percentage value i e 5 for. 35 competitiveness especially in the speci c economic situ PCBs and 3 for doors This practice although wide 90. ation indeed the increased dif culties in accessing spread is not supported by any theoretical basis as it is. credit have made the lowering of working capital and well known the SS should be used to hedge from the. the streamlining of the supply chain a primary target uncertainty of demand and therefore should be computed. Protopappa Sieke and Seifert 2010 On top of this as a function of its standard deviation and not only from. 40 uncertainty of the demand forecast updating order its mean Hadley and Whitin 1963 This also generates 95. batching and other factors cause an ampli ed up stream a wavering projected inventory that further distorts the. propagation of the down stream demand uctuations pattern of released orders. 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 3 A brief sketch of the Rev MRP algorithm. problems in the management of material ows and there The idea underlying the Rev MRP is to exploit the well. fore was looking for innovative solutions Thus the known capacity of the L4L technique to reduce MRP 100. authors proposed to test the Rev MRP nervousness Lee Padmanabhan and Whang 1997 but. The simulation and the benchmarking analysis con at the same time releasing orders adopting the more. 50 cern one nished product a washing machine WM and appropriate lot sizing rule for that item In fact although. a subset of items in its BOM consisting of an external the L4L is the most effective technique for reducing the. component an electronic PCB and a WIP item the system s nervousness it is rarely the best solution in 105. door of the WM one PCB is assembled on each door term of total costs because it entails the release of an. and one door is required for the production of one WM excessive number of orders and therefore high ordering. 55 The company provided three sets of data referring to costs Ho 1999 In order to present a clear explanation. three different runs of the company s MRP in days 7 of the Rev MRP logic the traditional MRP procedure is. TPPC 764579,19 January 2013 Initial,Production Planning Control 3. Figure 1 Comparison of different forecasts of the WM demand. 5 brie y recalled Orlicky s MRP follows four sequential In fact while traditional MRP system does not consider. steps capacity constraints and nite capacity planning has. always represented a practical problem Taal and 40. 1 net requirements computation considering gross Wortmann 1997 the Rev MRP natively considers those. requirements coming from the order releases of constraints and therefore releases only feasible plans. the previous echelon i e the upper level of the Figure 2 shows the Rev MRP functioning logic. 10 BOM and inventory levels which is repeated identically for each supply chain eche. 2 lot sizing using pre de ned criteria in accordance lon In particular focusing on a speci c echelon the 45. to the company s supply rules simulated MRP receives gross requirements from the. 3 offsetting considering the production supplying previous echelon and releases simulated orders with a. lead time from planned order releases to planned L4L lot sizing technique These simulated orders are. 15 order receipts then aggregated by the main MRP according to the. 4 BOM explosion i e go to next level of the company s lot sizing criteria and afterward actually 50. BOM released becoming the input for the next echelon. While the original version of Rev MRP Bregni et al. For this purpose the Rev MRP version adopted in the 2011 autonomously performs the demand forecast by. simulations presented in this paper consists of two analyzing the past demand the version presented in this. 20 algorithms that operate in parallel for each echelon of paper has been adapted because of the company s need 55. the supply chain of using its own forecasts Furthermore the company. has indicated the batch sizes to be used in the main. a shadow routine called simulated MRP,MRP routine and therefore the algorithm has been. computes order releases according to the origi, adapted for this requirement as well This adaption made.
nal logic of the Orlicky s MRP only using the, possible the comparison between the company s MRP 60. 25 L4L rule The net requirements computation is, and Rev MRP since the differences depend only on the. performed basing on gross requirements and, algorithm s logics and not on the aggregation s logics. simulated inventory levels computed consid,ering the orders released by the simulated. MRP 4 Rev MRP vs traditional MRP, 30 then a main routine called main MRP takes At rst the differences between Rev MRP and tradi.
in input these order proposals and releases the tional MRP behavior have been evidenced using each 65. actual orders by merely aggregating them by data set separately and therefore forecast updates have. implementing a pre selected lot sizing technique not been considered This because forecasts updates are. in accordance with the company s supply rules one of the major causes of the MRP nervousness. 35 and taking into account the actually produc Kimms 1998 and so in this way this rst comparison. tion supplying capacity Only those orders lead was performed in a case in which MRP could perform at 70. the production supply activities its best i e in an ideal case of exact forecast without. TPPC 764579,19 January 2013 Initial,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. TPPC 764579,19 January 2013 Initial,Production Planning Control 5. uncertainty In spite of this in Figures 3 5 it is possible Figure 6 summarizes the above mentioned results by. to see how the company s MRP releases excessive orders graphically comparing the performances achieved by the 30. with respect to the effective demand i e overshoots company s MRP and by the Rev MRP with respect to. 5 while the Rev MRP shows a signi cantly more stable some key performance indicators KPIs In addition the. pattern of orders released Speci cally each of the improvements reached by the Rev MRP are highlighted. following gures shows the comparison between the in the gure through the percentages placed inside the. company s MRP and the Rev MRP based on a different ovals 35. 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 4 1 Comparison analysis with forecast updates. The results shows that the Rev MRP is effective in. Differently from what previously analyzed considering. smoothing the production order releases mainly for. forecast updates stockouts and sudden orders may arise. lower level of the BOM eliminating overshoots and, As a matter of fact in the transitional period between. 15 drastically reducing the inventory levels along the supply. two forecasts the company experiences a demand that 40. chain In particular, may differ from the one for which the orders currently in.
irregularity in orders release estimated by com reception were released because of forecasting errors To. puting their standard deviation is lowered by protect from the demand uncertainty it is necessary to. 27 for PCBs and by 1 for door items take advantage of the SS In order to obtain a fair com. 20 overshoots estimated as the maximum order parison of the Rev MRP and the company s MRP algo 45. released are lowered by 48 for PCBs and 1 rithm in the Rev MRP the SS has been set equal to 10. for door items units for PCBs and to 5 units for door items similarly to. inventory carrying costs estimated by computing what has been set by the company Figure 7 compares. the average stock level are lowered by 90 for the order release patterns between Rev MRP and the tra. 25 PCBs and 83 for door items ditional MRP sequentially adopting each data set as the 50. warehouses overall dimension estimated by forecast updates of the previous one In particular it is to. computing the maximum stock level is lowered note that the Rev MRP on the right avoids the release. by 42 for PCBs and 93 for door items 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. TPPC 764579,19 January 2013 Initial,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 inventory carrying costs estimated by computing 25. 5 the left the average stock level are lowered by 90 for. Figure 8 clearly shows the differences in inventory PCBs and 84 for door items. management between the two algorithms and above all warehouses overall dimension estimated by. the signi cant improvements that the Rev MRP allows computing the maximum stock level is lowered. In particular in the upper section of the gure it is possi by 73 for PCBs and 84 for door items 30. 10 ble to observe the inventory trend of PCBs while in the. lower section the inventory trend of doors In both of This allows the use of smaller warehouses and helps. these cases the Rev MRP outperforms the company s in reducing the logistic costs for the whole supply chain. MRP by keeping a consistently lower inventory level Figure 9 graphically represents the above mentioned. The numerical results con rmed that Rev MRP out results comparing the company s MRP and the Rev. 15 performs the company s MRP even in case of frequent MRP with respect to some KPIs As in the previous Fig 35. forecast updates In this speci c industrial case it turns ure 6 the improvements reached by the Rev MRP are. Revised MRP for reducing inventory level and smoothing order releases gross requirements coming from the Master Production lot sizing rule of 100 PCBs and of 16

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