EVALUATING ACCURACY OF A TIME ESTIMATOR IN A PROJECT

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1 EVALUATING ACCURACY OF A TIME ESTIMATOR IN A PROJECT Thanh-Lam Nguyen, Graduae Insiue of Mechanical and Precision Engineering Wei-Ju Hung, Deparmen of Indusrial Engineering and Managemen Ming-Hung Shu, Deparmen of Indusrial Engineering and Managemen Lin-Ying Hsu, Deparmen of Indusrial Engineering and Managemen Yueh-Hung Yeh, Deparmen of Indusrial Engineering and Managemen ABSTRACT Purpose: Each aciviy in a projec mus be clearly defined and specified wih when o sar, how much ime is required for compleion and when o finish. I is vial ha he ime esimaes for each work obained be realisic in order o produce a good schedule for meeing deadlines and avoiding unnecessary projec coss. This paper aims a evaluaing he effeciveness and accuracy of he ime esimaor. Design/Mehodology/Approach: As racking signal, a widely used mehod in forecasing domain, has alarming abiliy in deecing srucural changes in ime series by responding o ou-of-conrol signals, i is, herefore, srongly suggesed o be employed in monioring he ime esimaion of criical asks, hrough which he effeciveness and accuracy of he ime esimaor is accordingly evaluaed. Findings: In his paper, i was found ha alhough he oal ime of a projec was well esimaed and me, he qualiy of he ime esimaion was sill no good enough. Therefore, he esimaor mus ake special aenion o assign more realisic ime and more appropriae personnel in he criical asks ha were deeced ou-of-conrol. By doing his way, he/she can perform his/her job beer and ge o know he employees beer and beer. Originaliy/Value: Though racking signal has been widely used in monioring he performance of a forecasing model, i is he firs ime in his paper ha i is addressed o be used in evaluaing he accuracy of a job performance. Keywords: Time esimaion, accuracy evaluaion, monioring criical asks INTRODUCTION Projec managemen in he business and indusry fields is defined as managing and direcing ime, maerials, personnel and coss o complee a paricular projec in an orderly economical manner and o mee esablished objecives of ime, cos and echnical resuls (Spinner, 1992). The managemen usually involves in using one or several echniques such as nework planning, cos analysis, personnel allocaion, and managemen by objecives, ec. Among S5-1

2 hem, nework planning has been considered he mos popular (Spinner, 1992). Usually being used o plan, schedule and conrol a projec consising of a group of inerrelaed aciviies, nework planning is especially useful for hose projecs which have a well-defined saring poin and a well-defined objecive. I has several applicaions in pracice, for example, consrucion projecs, adminisraive programs, mainenance operaions and any oher series of aciviies ha, when combined, can form a complee program wih a sar and a finish (Spinner, 1992). There are hree phases in he projec managemen cycle: planning, scheduling and conrol. In planning phase, i is criical o deermine wha aciviies mus be done in compleing a projec, heir sequence and inerrelaions which are hen ofen presened on an arrow diagram; whereas, in he scheduling phase, iming aspecs of each work are carefully considered. Each aciviy mus be specified wih when o sar, how much ime is required for compleion and when o finish. I is vial ha he ime esimaes for each work obained be realisic in order o produce a good schedule for meeing deadlines and avoiding unnecessary projec coss. The ime esimaes can be eiher done along wih he iniial diagram o make proper adjusmens based on pre-defined objecives or modified afer he projec begins o idenify if here is a delay in he work ha will exend he duraion of he projec or he work is progressing faser han expeced. The nework diagram normally uses a single ime for each work. The ime esimae, usually deermined by an experienced person, is he amoun of ime ha he work will require under a specified se of condiions. As suggesed based on personal experience, he esimae is usually biased and may be higher. In order o offse his bias, hree ime esimaes, including opimisic, normal and pessimisic, are used o creae one ime esimae as per he equaion (1). Opimisic ime 4* Normal ime Pessimisic ime Time esimae (1) 6 Normal ime is he ime ha would be mos frequenly required if he work were repeaed many imes under he similar condiions. While opimisic ime is he shores possible ime required for compleing a work under he assumpion ha everyhing goes one as planned, for insance, maerial deliveries are on ime as scheduled, machines operae wihou major breakdowns, personnel perform work wihin work sandard, ec., pessimisic ime is he maximum possible ime required for compleing he work in he wors siuaions; paricularly, delayed deliveries, accidens, bad weaher, and so on (Spinner, 1992). Spinner (1997) poined ou ha he hree-esimae approach can cause he expeced ime o be biased oward he pessimisic ime which is usually overly pessimisic; hus, he mehod is used only when he resuls from one ime esimae are probably unrealisic. Once a projec is carefully planned and scheduled, monioring is aciviies is of imporance o keep i under conrol. By comparing he sar and finish schedule wih he acual performance, cerain warning signals may arise for furher invesigaion and correcive acions. Some aciviies may be behind schedule and some may be ahead. In order o compensae he laeness in some aciviies o keep he projec on arge, here is frequenly an effor o adjus he iming of oher criical ones which may lead o a cerain revision of he S5-2

3 enire projec plan depending on he severiy of he iming problem. However, when criical jobs are finished ahead of schedule, fuure criical ones need checked and rescheduled if required. These are he fundamenals of monioring a projec performance. Therefore, i can be said ha good esimaes reflec no only he capabiliy and level of experience of he incharge person bu also his/her level of knowing he acual abiliy of he employees performance and resul in less adjusmen in he schedule during he projec cycle. Bu, here has hardly been any research in evaluaing how good he ime esimaes for he whole projec are as well as how efficien he performance of he employees is. Thus, his paper aims a using racking signals o evaluae he effeciveness and accuracy of he esimaor in erms of assigning ime esimaes. This paper is organized as he following. Secion 2 discusses he use of racking signals. An empirical sudy is presened in Secion 3 o illusrae he applicabiliy of racking signals. Conclusion makes up he las secion. REVIEWS ON TRACKING SIGNAL In monioring he forecas errors, racking signal is usually employed due o is alarming abiliy in deecing srucural changes in ime series by responding o ou-of-conrol signals (Snyder and Koehler, 2006). Providing accurae and unbiased esimae is he ulimae endeavor of any forecasing issues (Wisner e al., 2008; Chary, 1995). Inaccurae and/or biased esimae is one of he main facors making subsanial increase in boh operaional and opporuniy coss o he involved organizaions ha use he forecas resul being significanly differen from he acual figure. Forecas error is acually he difference beween he observed and he forecas as expressed in equaion (1). OV FV (1) where ε, OV and FV respecively denoe he forecas error, he observed value and he forecas value a he period. Forecas bias, he endency of a forecas o be consisenly higher or lower han he acual can be expressed in cumulaive forecas error (CFE) and mean absolue deviaion (MAD), shown in equaion (2). They are wo among several indexes usually used o measure he forecas accuracy (Wisner e al., 2008). CFE MAD i1 i i1 i / The raio beween CFE and MAD is named as racking signal (TS) which was developed by Page (1955). I is a widely used ool o check he forecas bias (Wisner e al., 2008; Wallace and Sahl, 2002). Several scholars have horoughly accessed and improved i (Gardner, 1985; Li e al., 2012). Brown (1959) suggesed a new definiion of he racking signal by defining i as he quoien beween he simple cumulaive sum of errors and he simple smoohed MAD. The Brown s racking signal (BTS), given in equaion (3), is commonly employed in pracice nowadays (Li e al., 2012). S5-3 (2)

4 BTS CFE / SMAD (3) where SMAD (1 ) SMAD 1 denoes he simple smoohed MAD a he period. In he denoaion, α is a smooh parameer 0,1 be zero (Chary, 1995). and he iniial value SMAD is assumed o By using racking signal, forecas errors can be efficienly moniored wih a conrol char wih wo conrol limis (upper and lower) which represen he rack where he errors should go (Mele e al., 2001). If a signal falls ouside he conrol limis, he forecas is considered biased, eiher under or over-esimaed (Li, 2007; Chary, 1995); hence, furher invesigaion is needed (Wisner e al., 2008). Ideally, he value of racking signal is eiher zero or close o zero (Sevenson, 2005; Khanna, 2007). Wih hese characerisics, racking signal is, herefore, srongly suggesed o achieve he objecive saed previously. The conrol limis under he mehod are se as he following. Under he Brown s mehod, wih he assumpion ha he errors are normally disribued wih a mean of 0 and a sandard deviaion of σ, he relaionship beween σ and MAD is given by 1.25MAD (Lawrence e al., 2009). As such, if he conrol limis for BTS are se a 3.75 which is equivalen o 3, he probabiliy of an ou-of-conrol signal is only 0.27% (Mongomery, 2009). Pracically, he value of he conrol limis ranges from 4 o 8 be considered accepable in some paricular circumsances (Li, 2007; Wisner e al., 2008). This approach can be employed in various applicaions. As an example, in his paper, i is used o evaluae he accuracy of he ime esimaor in a projec. EMPIRICAL STUDY In our sudy, we followed a specific projec a a well-known company which specializes in manufacuring elecronic devices in Kaohsiung indusrial park, Taiwan. Is research and developmen (R&D) deparmen is in charge of no only improving he performance of is curren producs bu also searching for, designing and esing new producs. Because several funcions of an organizaion concurrenly are obligaed wih he planning and implemenaion of research and developmen projecs and basic scienific researches as well as he execuion of he cliens-commissioned echnical services responsible for inernal and exernal cusomers, he majoriy of he members are in charge of a differen proporion of research and developmen projecs, basic scienific researches and echnical service asks. There is only one leader who assigns he in-charge person wih ime esimaion for each ask. He is called ime esimaor. Due o he fac ha R&D deparmen is in charge of various aciviies, whose execuion ime can be as shor as a few hours or as long as up o hree monhs or more, he ime esimaor someimes has some errors in his esimaion. In order o improve his capabiliy and working experience in he ime esimaion, i is quie imporan o rack he accuracy of his esimaion. Therefore, we sugges using he racking signals o deec any abnormal siuaion where his esimaion is significanly differen from he acual observaion. In he projec, here were 20 criical asks o be conduced wihin 240 hours. Each of he asks was expeced o be compleed wihin expeced ime as shown in Table 1. However, due o he knowledge and skills of he engineers, some asks were finished sooner bu some were S5-4

5 laer han expeced. The acual compleion ime of each ask is named as observed ime, also shown in Table 1. In his paper, for he ease of comparison he performance of he racking signal under differen α values, we consider i under he α values of 0.20, 0.25 and The relevan values are ploed on a conrol char wih he conrol limis of 3.75 as shown in figure 1. Table 1. Tracking signals under differen α values Task No. Expeced ime (hours) Observed ime (hours) CFE α = 0.20 α = 0.25 α = 0.30 SMAD BTS SMAD BTS SMAD BTS Toal Based on he racking signals in Table 1 and Figure 1, here are five, four and hree ou-ofconrol poins deeced, respecively o he α values of 0.20, 0.25 and Alhough he oal projec ime was sricly me, he esimaion was no really good enough. The esimaor should pay more aenion o he characerisics of each ask and assign appropriae engineers o perform he ask efficienly. Furhermore, i is easily found ha he smaller value of α, he smooher he racking curve and he more ou-of-conrol signals. Hence, i is suggesed ha if he esimaor is qualified as really experienced, we should se he α value small so ha i can give more olerance of he abnormal esimaion; whereas, larger α value is more suiable for senior esimaor. S5-5

6 Figure 1. Tracking signals under α values 0.20, 0.25 and 0.30 CONCLUSION Keep rack on he ime performance of employees in a projec is criical because any ou-ofconrol issue will affec he whole imeline of he projec. There are some circumsances where he employees already ry heir bes bu hey sill can mee he ime arge and here are oher ones where hey can easily achieve he se goal. These happen because of he inaccuracy of he ime esimaion and personnel assignaion given by an esimaor and/or his/her shorage in well knowing he acual capabiliy of he employees. In order o evaluae he effeciveness and accuracy of he esimaor in performing his job, i is suggesed ha we should keep monioring he ime esimaion of criical asks by using racking signal mehod. In his paper, wih a pracical example, i is found ha alhough he oal ime of a projec is well esimaed and me, he qualiy of he ime esimaion is sill no good enough. Therefore, he esimaor mus ake special aenion o assign more realisic ime and more appropriae personnel in he criical asks ha were deeced ou-of-conrol. By his way, he/she can perform his/her job beer and ge o know he employees beer and beer. REFERENCE 1. Brown, R.G. (1959), Saisical forecasing for invenory conrol, McGraw-Hill, New York. 2. Chary, S.N. (1995), Theory and Problems in producion operaion managemen, Ta McGraw-Hill Educaion, New Delhi. 3. Gardner, E.S. (1985), Exponenial smoohing: Sae of he ar, Journal of Forecasing, Vol. 4, pp Khanna (2007), Producion and Operaions Managemen, PHI Learning, New Delhi. 5. Lawrence, K.D., Klimberge, R.K. and Lawrence, S.M. (2009), Fundamenals of forecasing using excel, Indusrial Press, New York. 6. Li, L. (2007), Supply chain managemen: Conceps, Techniques and pracices, World Scienific, Singapore. S5-6

7 7. Li, Y., Lange, M. and Socks, C. (2012), Monioring forecasing sysems- Revisi Trigg s racking signal, paper presened a he 32nd Annual Inernaional Symposium on Forecasing (ISF2012), Boson, USA, available a: hp:// (accessed 25 January, 2013). 8. Mele, M., Secchiari, P., Serra, A., Ferruzzi, G., Paolei, F. and Biagioni, M. (2001), Applicaion of he racking signal mehod o monioring of udder healh and oesrus in dairy cows, Livesock Producion Science, Vol. 72, pp Mongomery, D.C. (2009), Inroducion o Saisical Qualiy Conrol, Wiley, Asia. 10. Page, E.S. (1995), A es for change in a parameer occurring a an unknown poin, Biomerica, Vol. 42, pp Snyder, R.D. and Koehler, A.B. (2006), Incorporaing a racking signal ino sae space models for exponenial smoohing, working paper 16/06, Monash Universiy, Ausralia. 12. Spinner, M.P. (1992), Elemens of Projec managemen: Plan, Schedule and Conrol, Prenice-Hall, New Jersey. 13. Spinner, M.P. (1997), Projec Managemen: Principles and Pracices, Prenice-Hall, New Jersey. 14. Sevenson, W.J. (2005), Operaions managemen, McGraw-Hill/Irwin, New York. 15. Wallace, T.F. and Sahl, R.A. (2002), Sales forecasing: A new approach, T.F. Wallace & Co, Ohio. 16. Wisner, J.D., Tan, K.C. and Leong, G.K. (2008), Principles of supply chain managemen: A balanced approach, Cengage Learning, Ohio. S5-7