Development of a Tool Management System for Energy Sector Company

Transcription

1 Development of a Tool Management System for Energy Sector Company Varun Raj C. 1, Sharath Kumar K. M. 2, Danel Berald Boch 3 1- M.Sc. [Engg.] Student, 2-Asst. Professor, Department of Mechancal and Manufacturng Engneerng, M. S. Ramaah School of Advanced Studes, Bangalore Senor Manager Producton, WEG Industres Inda Pvt. td., Hosur Abstract As the world of busness contnues to change rapdly and dramatcally, organsatons are also subjected to contnuous changes. Startng from 21 st century, the concept of a global maret place s nfluencng the manufacturng organsatons throughout the world n contrastng ways. However wth the product lfe cycles becomng shorter, nventory management s becomng as dcey as to predct one s future. Moreover owng to the uncertantes n demand of, managng nventory levels s becomng hghly complex and hence crucal to a company s survval. Snce fewer mathematcal models have been used to manage nventores, an attempt has been made to apply models to manage nventores n the tool warehouse. In the study, 10% reducton n the total supply chan cost through the development of a tool management system has been targeted. Inventory related data has been collected from Aprl 2011 to October After analysng the demand patterns, tools have been classfed nto certan and uncertan demand category of. Under certan demand, EOQ Model, complete aggregaton model and talored aggregaton model has been used for low, medum and hgh valued respectvely. On the contrary, uncertan demand have been modelled usng safety stoc and cycle servce levels. Fnally comparatve analyss has been carred out between legacy and used models n terms of total supply chan cost. Results revealed 7% savngs from the EOQ model for low value ; 21% savngs from aggregaton model for medum value ; 18% savngs from talored aggregaton method for hgh value and 23% savngs from probablty model for wth uncertan demand. In addton, most frequently used carbde nserts have been aggregated and correspondng optmal order frequency and lot szes have been arrved. Further, the study can be carred out for other categores of n the selected energy sector company. Key Words: EOQ, Complete Aggregaton Method, Talored Aggregaton Method, Cycle Servce evel 1. INTRODUCTION The world of busness contnues to change rapdly and dramatcally. Busness organsatons are under constant pressure to renvent and reorganse themselves contnually n order to meet demands of the global maret place. When t comes to manufacturng ndustry, the scenaro s more ntrcate due to the complextes of the sundry actvtes nvolved. A manufacturng organsaton can be vewed as a system whch transforms raw materals nto fnshed through the use of manufacturng resources. The manufacturng resources are crucal aspects n ths process and can be consdered to be anythng that s requred to produce the fnal product. As a manufacturng resource, toolng s a fundamental aspect of ths transformaton process, and t s an nescapable fact of manufacturng lfe that every manufactured product s bult usng a tool or by a machne that was made usng tools. In a tradtonal manufacturng envronment, dedcated machnery handled dedcated toolng and a lmted number of tools have been assocated wth each machne on the shop floor and operators montored, mantaned, and replenshed these n lason wth stores personnel who managed the toolng resources wth the ad of card/ndex system. Advances n manufacturng technology such as Machnng Cell and Flexble manufacturng system also requred the drect supply of tools from a local tool storage area. However, ncreasng versatlty of NC and CNC machnes, and a correspondng ncrease n wor dversty, has resulted n ncreasng number of tools beng assocated wth ndvdual machnes. As a result, the operators have no longer been suffcently n control to accept the responsblty for management of toolng. Further, the avalablty of correct toolng n the rght condton has been vtal to contnuous flow of producton n any manufacturng envronment. Gven these factors, t has been mperatve that management and control of toolng resources should receve far more attenton. Mathematcal nventory modelng has been now consdered the way forward for managng toolng resources. 1.1 Study Executon The area allotted for study executon has been tool warehouse where all the cuttng tools and accessores necessary for performng all the machnng operatons requred to manufacture all the have been stored. The tools used have been broadly classfed nto two consumables and non consumables based on ther consumpton pattern. 1.2 Products under Study There have been lots of varetes of tools stored n the tool warehouse. The most crtcal one dentfed has been carbde nserts as shown n Fgure 1 of varous szes and SASTECH 92 Volume 11, Issue 1, Apr 2012

2 geometres used for specfc machnng applcatons. The other tems shown n nclude drlls for producng round holes, taps for mang threads, end mlls and mllng cutters for materal removal applcaton and adaptors used for clampng the above mentoned tools onto the machne. Fg. 1 Carbde nserts 1.3 Ratonale for Selectng the Topc for Study The man problems dentfed after consultaton wth the concerned toolng-n-charge have been hgh tool nventory and consequently hgh nventory cost. Presently there has been no proper system to montor the nventory levels, re-orderng strateges and also the costs assocated wth procurement of tools and accessores. Ths emphaszed the need for havng a dedcated tool management system that could help the concerned people assocated wth the toolng to decde on what to be ordered at what tme n what quanttes so that the total cost as well as the nventory levels has been optmzed. The current demand pattern for the tools beng used has been studed usng a plot data collecton and the same has been appled n developng specfc mathematcal models for the tool management system. The attempt of developng a tool management system as the sole am of ths study also focuses on reducng the total cost wthout compromsng on the product avalablty. 2. PROBEM DEFINATION The man problems dentfed n the tool warehouse operatons have been tool unavalablty, hgh nventory and dscrepances n the re-orderng of tools. These problems have been drectly contrbutng to the hgh cost assocated wth the tool ware house operatons. Thus a need for a scentfc mathematcal nventory models for optmsng the nventory, re-orderng frequences and also to reduce the total annual costs has been dentfed 2.1 Am To develop a Tool Management System (TMS to reduce the total supply chan cost by 10 % 2.2 Objectves of the Study To revew the lterature on varous tool management and nventory management systems n supply chan To understand and collect data of the exstng process n the tool warehouse To analyse the flow of tools based on the demand pattern, optmum orderng quantty and frequency To develop a sutable Tool Management System (TMS to sut the above mentoned requrements To valdate and recommend TMS usng plot data (Apr 2011-Oct 2011 to arrve at optmum orderng frequences 2.3 Methods and Methodology Used terature revew for tool management and nventory management systems has been carred out by referrng journals, boos, manuals and related documents Exstng process has been studed usng a process mappng procedure Data has been collected usng company documents and observatons Demand pattern of the tools has been analyzed usng current orderng quantty, re-orderng frequency and nventory levels TMS model has been developed usng EOQ model for low valued Aggregaton method for medum valued Talored aggregaton method for hgh valued Safety nventory for wth uncertan demand usng probablty concepts The TMS has been valdated and recommended based on the results obtaned from the past data 3. SOUTION PROCEDURE The analyss of the collected data has been done and t has been understood that the specfc mathematcal nventory models have to be developed for specfc product categores based on demand pattern followed by those. Ths detaled step by step procedure of modelng of varous nventory models and also ther llustraton has been explaned below. 3.1 Selecton of Inventory Models Many organzatons mae use of mathematcal nventory models to maxmze ther proft. The varous nventory models assst n determnng the optmal tmes to procure or order and also advse the quantty of product that must be procured n order to eep the total supply chan costs down. SASTECH 93 Volume 11, Issue 1, Apr 2012

3 Stuatons Demand ead tme Table 1. Crtera for selectng nventory model Table 1 shows the crteron for selectng a partcular nventory model for dfferent stuatons based on how demand and lead tme for a partcular product vares over a partcular tme span. From the analyss of the data collected, t has been understood that the two dfferent stuatons applcable to the exstng scenaro has been 1 and 3. After havng a dscusson wth the tool warehouse n charge, t has been decded that determnstc models have been used for stuaton 1 where the demand and lead tme for procurement has been farly stable and probablstc model for stuaton 3 where the demand has been varable and lead tme for procurement has been constant. The varous mathematcal nventory models that have been appled for a group of selected after analyss and dscusson have been summarsed as follows. Determnstc Model EOQ model for low valued Aggregaton method for medum valued Talored aggregaton method for hgh valued Probablstc Model Type of Model to be adopted 1 Constant Constant Determnst c Model 2 Constant Varable Probablst c Model 3 Varable Constant Probablst c Model 4 Varable Varable Probablst c Model Safety nventory for wth uncertan demand 3.2 Developng Determnstc Inventory Models For dong the dfferent determnstc nventory models the followng nputs have been assumed D = Annual demand of the product S = Fxed cost ncurred per order s = Product - specfc fxed cost C = Cost per unt h = Holdng cost as a fracton of product cost (20% assumed Economc Order Quantty (EOQ Model EOQ model has been appled for low cost to fnd out the order quantty that mnmzes total nventory, orderng costs and holdng costs. In ths study, EOQ model has been appled for a group of nown as Fasteners whch have been commonly used for mechancally jonng or fxng two or more objects together. Steps nvolved n developng EOQ model The lot szng decson to mnmze the total cost has been made by consderng the followng ndvdual costs Annual Materal Cost Annual Orderng Cost Annual Holdng Cost Snce purchase prce has been ndependent of lot sze, 1. Annual Materal Cost =... (1 CxD 2. The number of orders must suffce to meet the annual demand D. Gven a lot sze of Q Therefore number of orders per year = D... (2 An order cost of S has been ncurred Q for each order placed, 3. Annual Order Cost = D... (3 S Gven a lot sze of Q, the average Q* nventory accounts to Q/2. Thus the annual holdng cost has been the cost of holdng Q/2 unts n nventory for one year (sx months n ths partcular case. 4. Annual Holdng Cost = Q* = Q*... (4 H hc Thus the Total Annual Cost, TC = CxD + D + Q*... (5 S Optmal lot sze s the one that mnmzes hc Q* 2 the total cost and t has been found by tang 2DSthe frst dervatve of the total cost wth respectve Q* = to Q and settng t equal t to 0. hc The optmal lot s sze has been referred to as the Economc Order Quantty (EOQ. 6. Optmal lot sze,... (6 7. The optmal orderng frequency s gven by D DhC n* = = Q* 2S Complete Aggregaton Model... (7 Aggregatng replenshments across or supplers n a sngle order helps to reduce lot sze for ndvdual because fxed orderng and transportaton costs have been spread across multple or supplers. The objectve of ths model has been to arrve at lot szes and an orderng polcy that mnmze the total annual cost. The approach used under ths model has been to jontly order dfferent. Snce there have been nearly hundred varetes of carbde nserts beng used n the machnng department and developng an aggregaton model for all of them beng seemngly mpossble, the most frequently used fve nsert types have been chosen. SASTECH 94 Volume 11, Issue 1, Apr 2012

4 Steps nvolved n developng Complete Aggregaton Model:- All the fve have been ncluded each tme an order has been placed. Therefore the combned fxed order cost per order has been gven by S * = S + s... (8 = 1 Assumng n as the number of orders placed per year, Annual Order Cost = S *n... (9 Annual Holdng Cost = DhC... (10 = 1 2n The next step has been to dentfy * the optmal orderng frequency. The optmal orderng frequency mnmzes the total annual cost and has been obtaned by tang the frst dervatve of the total cost wth respect to n and settng t equal to 0. Ths has resulted n the optmal orderng frequency. D hc... (11 1 n* = = 2S * DhC Total Annual Cost = S *n* + =... (12 1 2n * Talored Aggregaton Model The talored aggregaton model nvolves a more selectve approach n combnng to be ordered jontly. An orderng polcy wth optmum cost has been developed by applyng ths model. The ey to applyng ths model has been dentfyng the product that has to be ordered most frequently. The frequences wth whch the other have been ordered and ncluded n subsequent orders have been decded. Snce there have been numerous varetes of Taps beng used n the machnng department and developng a talored aggregaton model for all of them beng apparently not vable, the most frequently used fve Taps have been chosen. Steps nvolved n developng Complete Aggregaton Model 1. As a frst step, the most frequently ordered product has been dentfed (Assumng each product has been ordered ndependently. In ths case a fxed order cost of has S been + s allocated to each product. The orderng frequency has been evaluated for each product selected for applyng the model. hcd 2( S + s... (13 Ths has been the frequency at whch each product would have been ordered f t has been the only product beng ordered. Assumng nto be the frequency of the most frequently ordered product, ths product has been ncluded each tme an order has been placed. 2. The frequency wth whch other has been ncluded wth the most frequently ordered product;.e. the order frequency for each product as a multple of the order frequency of the most frequently ordered product has been dentfed. The most frequently ordered product has been ordered each tme and all of the fxed cost S has thus been allocated to t. For each of the other, there has been only product-specfc fxed cost. The orderng frequency for all other has been calculated. hcd... (14 2s 3. The frequency of product relatve to the most frequently ordered product has been evaluated and fractonal values have been rounded off to the closest nteger. n m = n... (15 4. Havng decded the orderng frequency of each product, the orderng frequency of the most frequently ordered product, n has been calculated. = 1 2( S + hc m D s m... (16 5. Optmum orderng frequency and order sze for of each product has been evaluated n m... (17 D Q = n Fnally the total annual cost has been calculated TAC = Annual Holdng Cost + Annual Order Cost DhC = = +... (18 1 n * ns + 2 n s 3.3 Developng Probablstc Inventory Models When the demand for a certan product has been uncertan there have been chances that a product shortage mght occur f the actual demand exceeds the forecasted demand. In order to negotate the negatve effects of such a stuaton, an nventory named Safety Inventory has been carred to satsfy the demand that exceeds the amount forecasted for a gven perod. A replenshment polcy conssts of decsons regardng to when to reorder and how much to reorder. These decsons determne cycle and safety nventores along wth fll rate and CS (Cycle Servce evel. The followng general assumptons have been made for developng the nventory models. The replenshment polcy conssts of a lot sze Q ordered when the nventory on hand drops to the ROP It s assumed that the weely demand has been normally dstrbuted, wth mean D and standard devaton σ D SASTECH 95 Volume 11, Issue 1, Apr 2012

5 A replenshment lead tme of wees has also been assumed Evaluatng Safety Inventory gven a Replenshment Polcy The method for evaluatng safety nventory under contnuous revew polcy has been mentoned below n consderaton wth the above stated assumptons regardng demand and replenshment lead tme. Expected demand durng lead tme, D = D.... (19 Assumng that a replenshment order s placed when ROP are on hand, Safety nventory, ss = ROP - D... ( Evaluatng Cycle Servce evel (CS gven a Replenshment Polcy The procedure for evaluatng CS for contnuous revew polcy has been dscussed below. Gven a replenshment polcy the nventory model ams to evaluate the CS, the probablty of not stocng out n a replenshment cycle. A stoc out occurs n a cycle f demand durng lead tme has been larger than the ROP. Thus CS = Prob (demand durng lead tme of wees ROP... (21 After tang nto consderaton the general assumptons, the probablty has been evaluated as a functon of the normal dstrbuton. CS = F( ROP, D, σ Evaluatng Safety Inventory gven a Desred Cycle Servce evel... (22 In many practcal settngs, frms have desred level of product avalablty and want to desgn replenshment polces that acheve ths level. The goal has been to obtan the approprate level of safety nventory gven the desred CS. The general assumptons mentoned earler stands true here also. Further the followng nputs have been assumed. 1. Desred cycle servce level = CS 2. Mean Demand durng lead tme = D 3. Std. devaton of demand durng lead tme = As per Eq. no. (20 ROP = D + ss The safety nventory has been calculated such that the followng equaton s true Prob (demand durng lead tme of wees D + ss = CS Gven that demand has been normally dstrbuted [(Eq. no. (22], the safety nventory ss has to be dentfed such that the followng has been true F( D + ss, D, σ = CS σ... (23 Therefore tang the nverse of the normal dstrbuton, 1 D + ss = F ( CS, D, σ Or ss = F 1 CS, D σ D... (24 (, Usng the defnton of standard normal dstrbuton and ts nverse t can be shown that the followng has been true. 1 ss = F S ( CS σ 4. RESUTS AND DISCUSSIONS... (25 The plot data collecton has been carred out for a perod of 6 month perod from Aprl 2011 to October 2011 and the results obtaned from the mathematcal models have been compared aganst the exstng legacy system beng practced n the company. 4.1 Results Obtaned through EOQ Model for Fasteners The economc order quantty model has been done for the low valued product category of Fasteners. The optmal lot sze (EOQ and the optmal orderng frequency that could mnmze the total annual cost for the selected have been found out. Fg.2 Comparson of orderng quantty Fgure. 2 shows the dfferences n orderng quanttes of the three between the exstng legacy method and the proposed EOQ Model. Fgure 3 shows the comparson n the total annual cost of all the selected for dong the EOQ Model. By analyzng the above bar graph t has been understood that 7% savngs has been acheved after applyng the EOQ Model. (Annual materal cost has not been consdered snce t remans the same for both legacy system and the EOQ Model. SASTECH 96 Volume 11, Issue 1, Apr 2012

6 complete aggregaton Model. (Annual materal cost has not been consdered snce t remans the same for both the legacy system and the respectve model. 4.3 Results Obtaned through Talored Aggregaton Model for Taps The Talored Aggregaton model has been done for the hgh valued product category of TAPS. The optmal lot sze and optmal orderng frequency that mnmse the total annual cost for the selected have been found out. Fg. 3 Comparson of total annual cost 4.2 Results Obtaned through Complete Aggregaton Model for Carbde Inserts The Complete Aggregaton model has been done for the medum valued product category of Carbde nserts. The optmal lot sze and optmal orderng frequency that mnmze the total annual cost for the selected have been found out. Fg. 6 Comparson of lot szes Fgure 6 shows the lot szng followed under the legacy system and the changes happened to t after the applcaton of talored aggregaton model. Fg. 4 Comparson of optmal lot sze Fgure 4 shows the lot szng followed under the legacy system and the changes happened to t after applcaton of complete aggregaton model Fg. 5 Comparson of total annual cost Fgure 5 shows the comparson n the total annual cost of all the selected for developng the Complete Aggregaton Model. By analyzng the above bar graph t has been understood that 21% savngs over the exstng legacy system have been acheved after applyng the Fg. 7 Comparson of total annual cost Fgure 7 shows the comparson n the total annual cost of all the selected for developng the Talored Aggregaton Model. By analyzng the above bar graph t has been understood that 18% savngs over the exstng legacy system have been acheved after applyng the talored aggregaton Model. (Annual materal cost has not been consdered snce t remans the same for both the legacy system and the respectve model. SASTECH 97 Volume 11, Issue 1, Apr 2012

7 4.4 Results Obtaned through Probablty Model for Mllng Cutters and End Mlls The Probablty model has been done for the product category of Mllng cutters and end mlls. The safety nventory to be mantaned and the re-order pont for an expected CS of 80% that mnmzes the total annual cost for the selected have been found out. Fg. 8 Comparson of safety stoc and re-order pont Fgure 8 shows the comparson n the safety nventory mantaned and the re-order pont for the legacy system and the developed model. frequently consumed nserts have been aggregated nto a sngle order and ths resulted n a reducton of 21% n the total annual cost. The nventory model for the product group of TAPS has been done usng the talored aggregaton method and a 18% savngs n the total annual cost have been acheved. The nventory model usng the probablty concepts has been appled to the product group of mllng cutters and end mlls. The safety stoc and the cycle servce level to be mantaned has been found out and the model resulted n a 23% savngs. 7. REFERENCES [1] Anon. (2006 Tungsten carbde nserts [onlne] avalable from < Tungsten_Carbde_Inserts.jpg> [15 December 2011]. [2] Chopra, S., Mendl, P., and Kalra, D. V. (2006 Supply Chan Management: Strategy, Plannng and Operatons. 3rd edn. UK: Prentce Hall. [3] Shafagh, M. (1994 Computer Aded Tool Management System; An Implementaton Model. PhD thess. Sheffeld: Sheffeld Hallam Unversty. [4] Buxey, G., (2006 Reconstructng nventory management theory. Internatonal Journal of Operatons & Productons Management 26 (9, [5] Mehta, T. (2002 Normal dstrbuton [onlne] avalable from < [15 March 2012]. Fg 9 Comparson of total annual cost Fgure 9 shows the comparson n the total annual cost of the selected for developng the probablty model. By analyzng the above bar graph t has been understood that 23% savngs over the exstng legacy system have been acheved after applyng the developed model. (Annual materal cost has not been consdered snce t remans the same for both the legacy system and the respectve model. 5. CONCUSIONS From the present study we found that the EOQ model appled to the product group of Fasteners resulted n a consderable reducton n the optmal lot sze and also helped n reducng the total annual cost by 7%. The Complete Aggregaton model has been appled to the product group of Carbde Inserts. Fve of the most SASTECH 98 Volume 11, Issue 1, Apr 2012