ISEM 2011 Proceedings, September 21-23, Stellenbosch, South Africa 2011 ISEM

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1 PLANNING AND MONITORING TOOL TO CONTROL PROFITABILITY IN A MANUFACTURING CONCERN: A CASE STUDY AT C CHEMICALS. S Mhlanga1*, W Dlamn 2,C Mbohwa 3 and JHC Pretorus 4 1 Department of Mechancal Engneerng, Faculty of Engneerng and the Bult Envronment, Unversty of Johannesburg, South Afrca, smhlanga126@gmal.com 2 Department of Industral and Manufacturng Engneerng, Faculty of Industral Technology, Natonal Unversty of Scence & Technology, Zmbabwe, mrwdlamn@gmal.com 3 Department of Qualty and Operatons Management, Faculty of Management, Unversty of Johannesburg, South Afrca, cmbohwa@uj.ac.za 4 Department of Mechancal Engneerng, Faculty of Engneerng and the Bult Envronment, Unversty of Johannesburg, South Afrca, jhcpretorus@uj.ac.za ABSTRACT The am of ths paper s to analyse the key proft drvers n a manufacturng concern, and develop a decson tool based on optmsaton technques. The am s plannng and managng these proft drvers so that the target revenue and proft are realsed durng an operatng perod. Ths was done through developng a forecastng tool based on Vsual Basc that was used by the organsaton to project the future demand of ts products. Ths tool was lnked to a database that had detals on past product performance. Plannng software was developed usng lnear programmng, whch dentfed the optmum product mx and the sales outlets locatons to maxmse revenue and proft. *Correspondng author 93-1

2 1. INTRODUCTION The stuaton n many busnesses s that the operatons department s burdened as t struggles to fnd ways to deal wth bottlenecks that hnder proftablty. In a bd to mprove proftablty, the theory of constrants was appled. It was realzed that there s a need to look at the problem from a strategc vew. The paper revews the theory of constrants at the operatonal level and then looks at the supply chan from a holstc vewpont. The case study company s dscussed and the challenges faced n plannng and montorng are ndcated. A mathematcal formulaton of the product-mx was developed and results from Mcrosoft Excel Solver showng the quanttes that the company needs to produce to acheve proftablty are presented. C Chemcal manufactures chemcal products classfed nto nne market segments based on applcaton. The segments are Dary and Agrculture, Metal Treatment, Boler and Water Treatment, Mnng, Engneerng and Heavy Industral Manufacture, Transport & Haulage, Commercal and Industral Laundry, Hosptalty and Housekeepng, Hosptal and Healthcare, Food and Beverage. The problem was how can the operatons department plan and control the plant to maxmse revenue. The paper set out to demonstrate the benefts acheved through plannng for the optmum product mx before dealng wth the constrants that hamper company achevng performance goals. 2. LITERATURE REVIEW The bass of the paper was n the followng areas: 2.1 Theory of Constrants (TOC) Theory of constrants s a management phlosophy proposed by Goldratt that deals wth managng constrants of bottlenecks that prevents the company from achevng ts goal of makng proft, [1], [2]. Bottlenecks are the machnes or processes whch control the throughput of the system, thus managng them effectvely and effcently yelds hgher system throughput. Many producton control systems have been proposed to mprove throughput n the past. Among them are the Materals Requrement Plannng (MRP), Just- In-Tme (JIT), Kanban, Constant Work-n-Process (CONWIP), Drum-Buffer-Rope (DBR) system, [2]. The approach prevously has been to deal wth the problem wthn the company. However, there s a need to look at the entre supply chan. Salm hghlghted how supply chan speed and flexblty have become key levers for compettve dfferentaton and ncreased proftablty, [3]. The faster the supply chan, the better a company can respond to changng market stuatons and the less t needs nventory thus resultng n hgher return on captal employed. Supply Chan flexblty, on the other hand, has become an mportant factor because customers requre ncreasngly customzed products that satsfy ther unque needs. The goal of supply chan management s to replace some of the physcal stock wth ntellgent nformaton and plannng, that s, wth the ablty to analyze what s possble n dfferent parts of the chan and the best global plan. The challenges of supply chan management vary by ndustry and customer segment. Supply chans fall nto three man classes dependng on where the major constrants le: materal-ntensve, asset-ntensve and dstrbuton-ntensve ndustres, [2]. In materal-ntensve-ndustres, such as hgh-tech and machnery manufacturng, the largest costs and constrants are related to the management of procured and manufactured materals. In asset-ntensve ndustres, such as paper and metal, planners need to concentrate on optmzng machne capacty utlzaton and the product mx. In dstrbuton-ntensve ndustres, such as the consumer packaged goods ndustry, the major 93-2

3 challenge s to manage the dstrbuton network and fnshed goods nventory from the factory to the end-customer. The goal of supply chan development should not be the blnd standardzaton of processes accordng to someone else s best practce templates but to streamlne and ntegrate the entre supply chan based on the needs of the endcustomer. The development team should constantly search for mprovements that wll ncrease the customer value of supply chan delvered products and servces whle maxmzng the total margn, [2]. Some companes have used standard tools such as Enterprse Resources Plannng (ERP) software to do transactons and control systems. However such tools are lmted when t comes to supply chan analyss and proft optmzaton. Accordng to the Gartner Group, ERP systems accommodate only the smplest supply chan optmzaton requrements [4]. ERP systems cannot gve optmal answers to many central plannng questons, such as, what should be produced, when and where, so that the level of customer servce and supply chan proftablty are as hgh as possble. Such questons fall n the area of Product Mx decsons. There s need to support the ERP s capabltes wth the vrtues of an optmzaton tool to maxmze value n the plannng and controllng of the supply chan actvtes. A number of tools have been used to address supply chan optmzaton. Mabn and Daves looked at applcaton of theory of constrants to product-mx problem. One way to solve the Product Mx problem s to use Lnear Programmng, whch can handle multple plannng perods and multple product lnes, [5]. 2.2 Lnear Programmng (LP) LP s a mathematcal technque concerned wth the optmal allocaton of scarce resources, [6]. Ths s allocaton amongst competng actvtes. It s a procedure to optmze the value of some objectve (for example, maxmum proft or mnmum cost) when factors nvolved (for example, labour, or machne hours) are subject to some constrants (for example, 1000 labour hours are avalable n a week). Thus LP can be used to solve problems, whch conform to the followng: The problem must be capable of beng stated n numerc terms. All factors nvolved n the problem must have lnear relatonshps, e.g. a doublng of output requres a doublng of labour hours; f one unt provdes $10 contrbuton 10 unts wll produces $100 and so on. The problem must permt a choce between alternatve courses of acton. There must be one or more restrctons on the factors nvolved. These may be restrctons on resources (labour hours, tons of materal, etc.) but they may be on partcular characterstcs, for example, a fertlzer must contan a mnmum of 15% phosphates and 30% ntrogen or a patent fuel must contan no more than 6% ash, 2% phosphorous and 1% sulphur. In expressng LP Problems there are two major factors to consder: Objectves: The frst step n LP s to decde what result s requred,.e. the objectve. Ths may be to maxmze proft or contrbuton, mnmze cost or tme or some other approprate measure. Havng decded upon the objectve t s now necessary to state mathematcally the elements nvolved n achevng ths. Lmtatons or Constrants: Crcumstances always exst whch govern the achevement of the objectves. These factors are the lmtatons or constrants to the LP problem. These, n any gven stuaton must be clearly dentfed, quantfed, and expressed mathematcally. They must be lnear. 93-3

4 Al-Aomar, [7], hghlghted lnear programmng applcaton to solvng Product-Mx problem but stll has potental n Southern Afrca regon. 2.3 Synergy Between Lnear Programmng and Theory Of Constrants TOC and LP approaches are complementary to each other. LP offers advantages of flexblty, unversalty, combned wth speedy LP soluton, consderable what-f? nformaton and ablty to change quckly and easly. LP provdes a good startng pont for producton plannng. TOC provdes a phlosophy wthn whch to use the LP to gan extra advantage; t encourages us to be more nnovatve n explorng the statc LP. Mabn and Gbson, hghlghted how TOC encourages rgour n explotng the constrants fully before subordnatng other actvtes to sut the constrant, and before addng new capacty, [8]. 3. CASE STUDY 3.1 Operaton Need C Chemcals s a chemcal processng company wth nne market segments. The general process s mxng and processng of raw materal for dfferent products. Most of the raw materals are mported and products may share common raw materals. The company s top management after havng set a busness plan document contanng both strategy and budgets ready for presentaton to the Board of Drectors had left a gap on mplementaton. The Operatons Department had to deal wth the constrants n the plants to meet company goal of proftablty. Ths led to the department tryng out the Theory of Constrants technques n the plant to satsfy Sales Department demands. 3.2 Complexty of the Current System These are some realtes that constran busness s ablty to supply the optmum product mx all the tme: ) Very wde and dverse product portfolo, whch makes the plannng task complex. The company has about 284 SKUs n total n the nne product famles. ) The Enterprse Resources Plannng (ERP) system (Sage Lne 200) beng used could only delver accuracy n carryng out transactons but not recommend when and what to supply optmally. Ths means t has no capacty to provde advce on what to purchase, manufacture and supply n terms of type of product, ts quanttes and tme, bearng n mnd the dynamcs of both the nternal and external operatng envronment. ) There are many products that share the same scarce resources (raw materal, people and plant). Ths makes plannng the allocatons very essental but more complex. v) Many of the nput materals are mported and the dynamcs of foregn currency exchange rates have a bearng on what to deal n. Thus the need to develop a system that manages the product mx to maxmse revenue and proftablty and to set revenue and proft targets nstead of volume targets. 4. MODEL DEVELOPED The use of optmum product mx problem s enhanced by a smple cyclc flow dagram developed by the authors gven n Fgure 1. Ths model deals wth product mx decson before lookng at how constrants can be changed usng theory of constrants. 93-4

5 4.1 Descrpton of the Problem The problem set s formulated nto a smple Lnear Programmng problem wthn the followng premses: ) Product sold n all the market segments (categores) company-wde can only be as much as the total product manufactured or purchased by the organsaton. ) The total proft made s made up of the sum total of all the proft per unt for all the product sold ) The product manufactured and/or sold are zero or greater than zero n quantty (non-negatve) v) The sellng prce of each product sold s ether zero or greater (non-negatve) v) All product sold by the company put together should be at least worth the amount v) of money the busness needs to sustan ts operatons The cost of materals and resources used n producng the products should be at most equvalent to the workng captal plus other captal that the busness has allocated for ths actvty v) The cost of the total human resource used to produce the product the busness s sellng should be at most equvalent to the money the busness has allocated n the budget for that resource v) The busness operatons must be such that the busness at least makes enough money to take care of all of t oblgatons x) The busness uses full absorpton costng and there s an overhead recovery rate appled to each product sold x) The busness has nne market segments and does not want to close or prejudce any of these n any of ts operatng months x) The busness has three operatng branches and does not want any of these to be closed, deprved or short-suppled wth products at any one gven tme Supply Chan Actvtes to Delver Product Mx Demand Related Factors Plannng the product Mx - Optmzer Sellng of Product Aganst Optmum Product Mx Measurement of Actual Performance Aganst Plan Fgure 1: Process Flow for the Supply of the Optmum Product Mx 4.2 Problem Formulaton The followng equaton summarzes the model for the product mx whch s to maxmze proft: 93-5

6 Z = N ( s = 1 Subject to: N = 1 N = 1 N =1 c mat c man Cover ) * x Equaton 4 x * cmat Bmat Equaton 5 x * cman Bman Equaton 6 x c B N x =1 * over over Equaton 7 P cap Equaton 8 x, cmat, cman, Bover, Bman, Bmat 0 Equaton 9 Where, Z = objectve functon, proft to be optmzed s = sellng prce per unt of product c mat = cost of materal n producng one unt of product c man = manpower cost n producng product one unt of product c over = cost of factory overheads n producng one unt of product x = quantty of product for = 1 to = N B mat = budget allocated for all producton materals B man = budget allocated for producton manpower B over = budget allocated for producton overheads P cap = maxmum plant capacty Equaton 4: gves the computaton of the proft contrbutons for all products rangng from =1 to N. The equaton means that the sum of revenue from product x to product x N less the manpower, materals and overhead costs ncurred n producng these products gves the objectve functon value, whch s the proft target. Ths model s constraned by the followng factors: Equaton 5: The cost of all the materals used to produce all the x products rangng from =1 to N s less than or equal to the amount of money allocated n the materals budget for the perod under revew. Equaton 6: The cost of all the drect and ndrect manpower attrbuted to the producton of all the x products rangng from =1 to N s less than or equal to the amount of money allocated n the manpower budget for the perod under revew. Equaton 7: All the costs ncurred n manufacturng overheads that are assocated wth the producton of products x for =1 to N are to be less than or equal to the amount of money the busness has budgeted for ths purpose. Otherwse the producton operaton has to be wthn budget. Equaton 8: The total volume of product produced wthn the tme n queston s not more than the plant s maxmum capacty. The maxmum capacty s the sum of all capactes for dfferent product portfolos. 93-6

7 Equaton 9: The number of products for = 1 to N, the cost of materals, the cost of manpower, the cost of overheads, the budget for materals, the budget for manpower and the budget for overheads are not less than zero. We cannot have negatve products, materal, manpower and overhead costs and budgets wthn the plannng perod. Ths s the non-zero constrant equaton. 4.3 Assumptons n Applyng the Model to a Real Lfe Stuaton The followng assumptons were consdered n applyng the model: ) The supply chan lead tmes are assumed to be zero or mmateral for the purposes of ths research. These are the purchasng lead tmes; the producton and delvery lead tmes. Ths assumpton mples that the demanded delverables become avalable as and when requred. These are delberately handled ths way because they are properly and adequately afforded the due attenton by the MRP n the ERP applcaton. ) The foregn currency s assumed to be avalable at the tme t s requred and n the exact amounts needed for the purchase of materals. ) The product cost and sellng prce are assumed to reman constant throughout the materal flow, between the solver s recommendaton through tll the product s sold and the money s realzed. Ths s far from realty, but essentally, the realty n the real world s that some products are purchased on cash-on-delvery (COD) bass; some pad wthn 15 days whle some go for the long 60 to 90 days. Ths s more based towards fnance where the focus s debtor days and cash flow. v) Manufacturng s expected to convert the materals nto fnshed products n the tmeframe allocated. Ths assumes no wndow of product unavalablty due to plant downtme. Ths assumpton s valdty s based on the huge capacty that the busness has. Ths does not mean that there are no breakdowns but usng theory of constrants one can breakdown effects are reduced by bufferng. v) The fundamental premse s that the model s solver s based on the sales performance data of the prevous perod. The wdth of the product portfolo/base s such that the seasonalty of products s smoothed out, as when one product wthn one famly begns to dsplay slowness n movement owng to a season change, one wthn the same famly would dsplay a rse n demand. Examples are on water treatment chemcals, where n the wet season, the demand for one product goes hgh and the other product s demand goes very low. Essentally, averagng the demand for such products wthn ther famly does not compromse sgnfcantly the essence of the solver s recommendaton tool. v) The sales volume of the three factores product mx shall be handled from the central offce. 5. RESULTS The Product Mx Fnder system was developed usng Vsual Basc wthn Mcrosoft Excel. The access page that gves the user entry nto the system s shown n Fgure 2. It s from ths page that the user can choose to see the table of contents or launch the mxer or close the applcaton. 93-7

8 Fgure 2: The Access Page to the Mx Fnder Applcaton Fgure 3 shows the snppet of the table of contents whch gves user access to the dfferent spreadsheets that provde nformaton such as sales forecast, labour cost and materal cost. These spreadsheets are used to update nformaton on a daly bass. For the sake of mantanng anonymty of company the spreadsheets were not shown. Fgure 3: Table of Contents snppet From access page shown n Fgure 2 one can proceed to launchng of the applcaton whch would lead to Fgure 4 shows the solver functons. The User can run the solver, vew solver results or ext the program. 93-8

9 Fgure 4: Solver Functons Fgure 5 gves access to data updates such as foregn currency rates, factory capactes, fnancal and sales detal. These contrbute to the calculatons of the proft and the constrants used n the solver. Fgure 5: Data Updates or Model updates Dalogue Box The detal of fnancal updates and sales updates are shown n Fgure 6 and Fgure 7 respectvely. 93-9

10 Fgure 6: Fnancal updates Fgure 7: Sales Updates Snce the busness serves nne market segments nne product famles were used. Fgure 8 shows the results after runnng the Product Mx Solver. The product famles are labelled 001 to 009. The parameters nput to the solver such as proft contrbuton, average cost, sellng prce, capacty of the plants and mnmum sales expected are calculated through lnks of spreadsheet to the raw data. The Product Mx Solver gave output of expected volumes for each product famly to maxmse proft

11 Product Famly Optmum Qty To Be Sold x 1 x 2 x 3 x 4 x 5 x 6 x 7 x 8 x 9 Proft Contrbuton/kg 11,197 6,598 9,573 13,310 19,588 8,503 10,118 7,194 3,353 Average Cost/kg 16,795 27,526 14,360 19,965 29,382 12,754 15,314 10,791 8,276 Sellng Prce/kg 27,992 34,123 23,933 33,276 48,970 21,257 25,432 17,985 11,628 Mn (Demand vs Capacty) 260,000 30,000 15,000 30, , ,000 20,000 15,000 10,000 Mn Sale 26,000 22,000 15,000 14,000 8,000 15,000 13,000 8,000 9,000 Budget Revenue Cost Proft 7,313,689,000 3,100,000,000 4,213,689,000 Solver's Output Volume 176,000 Cost 3,042,489,610 Revenue 4,730,664,402 Mnmum Volume 130,000 Maxmum Capacty 855,000 Fgure 8: Results of the model The results shown n Fgure 8 are a proft of Z$4 mllon from revenue of about Z$7 mllon. Product Famly 005 (Transport and Haulage) contrbuted the hghest proft of Z$19 558/kg whle Product Famly 001 (Food and Beverage) contrbuted to the second hghest proft of Z$11 197/kg whle the optmum quanttes were kg and kg respectvely. From ths result the Operatons Department proceeded to analyse the constrants that hndered the Product Famly 005 and Product Famly 001 from even contrbutng more to the proftablty of the company. Thus the teratve method s mantaned whch s a better plannng and montorng tool for C Chemcals to concentrate on machnes or systems that contrbute the most to the survval of the company. Ths tool helped the company survve n the turbulent envronment durng the economy melt down n Zmbabwe. 6. CONCLUSION The paper was amed at modellng an optmum product mx fnder that can be employed n a manufacturng concern to maxmse proftablty thus assstng management n decson-makng. Lterature on theory of constrants, lnear programmng and product mx was revewed to substantate the mathematcal formulaton for the case study company. Mcrosoft Excel and VBA programmng was used. Sample busness scenaro was smulated and results analysed. The case study company embraced the tool for mplementaton. The results gves the Operatons Department ablty to work on the constrants that most affect the proftablty of the company. As the constrants used were based on the stuaton on the ground n formulaton of the lnear programme model. Dealng wth the constrants wthout knowng ther contrbuton to the proftablty of company would lead to waste of 93-11

12 effort thus the synergy of Theory of Constrants and Lnear Programmng whch needs to be embraced by Operatons Managers wthn the regon to survve global competton. REFERENCES [1] Goldratt, E, (1984), The Goal: A process of Ongong Improvement, North Rver Press Inc [2] Apollon, S, Lando, M, and Savno, M M, (2001), Advanced Technques of Theory of Constrants and Actvty Based Costng for Schedulng of Hgh Technology Producton Lnes, In proceedngs of the 2 nd World Conference and 15 th Annual POMs Conference (CD-), Cancun: Mexco [3] Salm J (1998), Improve Your Proftablty Through Supply Chan Optmzaton, Sun Mcrosystems' Fnnsh Net@Work magazne Aprl, 1998 [4] ERP, (2010), ERP Vendors and the Myth of the 80/20 Rule n SCP, Gartner Group TV [5] Mabn V. J and Daves J., (2003), Framework for understandng the complementary nature of TOC frames: Insghts from the product mx dlemma, Internatonal Journal of Producton Research, Volume 41, Issue 4, 2003, Pages [6] Lucey, T, (1996), Quanttatve Technques, 5 th Edton, Ashford Colour Press Ltd, [7] Al-Aomar, R, (2000), Product-Mx Analyss Wth Dscrete Event Smulaton, Proceedngs of the 2000 Wnter Smulaton Conference, pp [8] Mabn V J and Gbson J, (1998), Synerges from spreadsheet LP used wth the theory of constrants- a case study, Journal of the Operatonal Research Socety, (1998), 48,