Identifying Factors that Affect the Downtime of a Production Process

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1 Identfyng Factors that Affect the Downtme of a Producton Process W. Nallaperuma 1 *, U. Ekanayake 1, Ruwan Punch-Manage 2 1 Department of Physcal scences, Rajarata Unversty, Sr Lanka 2 Department of Statstcs and Computer Scence, Unversty of Peradenya, Sr Lanka *Correspondng author: Emal: warun.rnallaperuma@gmal.com 1 INTRODUCTION In ths compettve world, manufactures always try to make ther productons a top prorty. Producton effcency s vtal n ths regard. Unplanned downtme s the major contrbutng factor for loss of proft even wth new technologes. Unplanned downtmes occur by machne breakdown, delayng materals, falures, and defects. The overall producton depends on the effectve operaton of machneres, tools and etc. Equpment downtme occurs mostly due to unplanned actons. To maxmze profts, companes have made operatonal effcency a top prorty. Even f a company had nstalled new technologcal methods, more often the planned producton does not exceed 50%. Ths may be due to the downtme of falures, defects, and machnery problems. However, the unplanned stops are the most common unexpected factors that effect on the overall productvty. The requrements of outstandng performance force, companes need to reduce ther total downtme frequency. In ths study we used sx sgma tools to understand the major factors that affect the total down tme of a producton process of a world class apparel manufacturer n Sr Lanka. 2 METHODOLOGY In ths study we receved data from a world-class apparel manufacturer operatng n Sr Lanka who s engaged n product desgn, development, executon and marketng to global super brands. We used three year producton performance evaluaton data, n ths case total down tme per month snce 2014 January to 2016 December. There are 31 downtme types ncludng ths data set (ncludng the responsble department) as well. There are nne departments. Downtme (target<2%): We defned the downtme as the perod durng whch an equpment or machne s not functonal or cannot work. We have notced that techncal falures, machne adjustments, mantenance and mssng raw materals, labor and power. The requred producton capacty and effcency s not acheved due to the total downtme (Table 1). The fshbone dagram: Fsh bone dagram s a sx sgma tool that used for statstcal process control. Ths can be used to understand the major causes behnd the effect. In a fsh bone dagram the problem statement (effect) s wrtten frst. A crcle s drawn around t and horzontal arrow runnng nto t. Major categores (causes) were dscussed. Usually, Methods, Machnes, People, Materals, Measurement, Envronment are the major bones of the fsh dagram. Subcauses are branchng off the major causes. Pareto Charts: Pareto Analyss s a smple technque that used 80:20 Rule. Pareto (1897) assumed that 80% percent ISSN The Open Unversty of Sr Lanka 551

2 Proceedng of the 15 th Open Unversty Research Sessons (OURS 2017) of the effects are due to 20% of causes and vce versa. Pareto analyss s very useful n the control phases of the Sx Sgma methodology. In Pareto analyss cumulatve percentage are gven n a lne chart and percentage of causes explan by each effects are plotted n bars. Causes are lsted n the X-axs and percentage of effects s gven n the Y-axs (Scrucca, 2004). ABC-Analyss: ABC analyss s an extenson of the Pareto chart that groups causes n to three groups. A stands for the most mportant causes (mportant few), B for moderate and C for least mportant. Two axs s X and Y represent effort (E ) and yeld (Y ) respectvely. The algorthm s based on an ABC analyss and calculates these lmts on the bass of the mathematcal propertes of the dstrbuton of the analyzed tems. The ABC analyss compares the ncrease n yeld (mportance) to the requred effort. Let X 1, X 2,..., X n be a set of n postve values ( X 0) of n dfferent varables of an emprcal data set wth respect to the property mportant. The dstrbuton of the values x s unequal (few large values and many small values (Thrun, Lotsch and Ultsch 2017). x s are sorted n descendng order ( X X ). The fracton of the frst 1 elements to n represent the effort E and yeld s represented by n x k 1 Y k. All the analyss were x 1 performed usng software R (R Core Team 2017). Table 1: Downtme type department wse. Department Downtme Type Engneerng Bundle tme due to machne problem, machne adjustment, thread unbalance, uneven measurement. needle cut and needle hole, needle breakages, burn mark and lue mark, Tape unbalance, crackng, stan,skp, uneven edge, stan, bundle tme due to machne problem, Cuttng Input delay, cuttng defects Plannng No nput, plannng ssue Materal and Qualty Assurance MQA defect (MQA) Raw Materal Warehouse RMW defects, accessory delay,label delay, (RMW) Customer servce External operatons ssues, customer servce ssue, development ssues, Producton Layout changes, NSU bondng ssues, machne try out tme,soup tme, CTP Plannng ssues, under producton, no nput, Producton development Center Techncal ssues,development ssue (PDC) Purchasng Purchasng ssue, materal development ssue FGW FGW delays IT SAP ssue PNA Power falure WRK General downtme 552 ISSN The Open Unversty of Sr Lanka

3 3 RESULTS AND DISCUSSION Proceedng of the 15 th Open Unversty Research Sessons (OURS 2017) Fgure 1: Fshbone dagram (Departments are lsted n man bones, down tme types are lsted n sub bones) Accordng to fshbone dagram (Fgure 1) we notced eght departments contrbuted for downtme n year Engneerng and Raw Materal Warehouse shows number of down tmes. However, department contrbuton to the total downtme hour s not clear. Fgure 2: ABC plot under dfferent dstrbutons. Observe dstrbuton (blue lne), Proportonal (magenta lne), unform dstrbuton (green lne). The Break-Even pont: pont at slope of the ABC curve at ths equal to one (green star). The lmts of three sets A, B and C for the downtme data (red lnes) (Thrun et al., 2017). ISSN The Open Unversty of Sr Lanka 553

4 Proceedng of the 15 th Open Unversty Research Sessons (OURS 2017) Fgure 3: Revse fshbone dagram after wth vtal few down types (n Engneerng and Cuttng) ABC analyss s performed for year 2014 and we found that three down types contrbute for more than 75% of the total downtme (Fg. 2) and other eght down types contrbute for 10% total downtme. Fgure 3 shows fsh bone dagram for revsed analyss (after ABC) wth three vtal breakdowns three namely, M/C breakdown, cuttng defects and nput delay from two departments Engneerng and Cuttng. Pareto chart s gven n Fgure 3 and we found that three downtme types (.e. B3, L1 and C3) contrbute to 75% of the total down tme. Fgure 4 shows downtme as a percentage of total producton hrs. We notced that out of 36 months eght months ther downtme percentage s hgher than the Bootstrap upper and lower control lmts (see, Efron, B. 1979). Fgure 3: X Axs: Pareto chart for 14 down types. Y Axs: Down tme as percentage of total down tme. 554 ISSN The Open Unversty of Sr Lanka

5 Proceedng of the 15 th Open Unversty Research Sessons (OURS 2017) Fgure 4: Percentage of down tme for each month ( years) and ther bootstrap confdence ntervals. 4 CONCLUSIONS AND RECOMMENDATIONS Our study ndcates that most of the downtmes are due to few vtal down types. Therefore, a company can ncrease ts downtme effcency to 75% by controllng 3 down types. It s necessary to perform detal an analyss to understand out of control sgnals n the control chart. REFERENCES Efron, B. ((1979) Bootstrap Methods: Another Look at the Jackknfe. Ann. Statst. 7, no. 1, Mchael Thrun, Jorn Lotsch and Alfred Ultsch (2017). ABCanalyss: Computed ABC Analyss. R package verson R Core Team (2017). R: A language and envronment for statstcal computng. R Foundaton for Statstcal Computng, Venna, Austra. Scrucca, L. (2004). qcc: an R package for qual ty control chartng and statstcal proce ss cont ISSN The Open Unversty of Sr Lanka 555