Combining predetermined and measured assembly time techniques: Parameter estimation, regression and case study of fenestration industry

Time estimation is an important element of the effort evaluation process, which is indispensable along many phases of business development from bidding for the competitive contract to design and production phases. In particular, the time estimates are useful in the resource planning process, especially when the precision of the provided estimates is quantitatively characterised. We propose in this work an approach that combines the techniques developed within predetermined time methods (such as MODAPTS and MINIMOST) with the statistical techniques that use the real data (collected along the stopwatch time measurements). Our approach allows to obtain not only time estimates themselves, but also the confidence intervals for them. This information helps the practitioner to decide whether provided time estimates (coupled with accuracy parameters) meet his criteria. The proposed approach can be used in time estimations for the project containing several operations of different nature, and the application of our methodology is discussed in detail for the case of fenestration industry.

[1]  Joshua D. Summers,et al.  Assembly Time Modeling through Connective Complexity Metrics , 2010, 2010 International Conference on Manufacturing Automation.

[2]  Kang Le,et al.  A virtual reality‐based maintenance time measurement methodology for complex products , 2013 .

[3]  Welch Bl.  THE GENERALIZATION OF ‘STUDENT'S’ PROBLEM WHEN SEVERAL DIFFERENT POPULATION VARLANCES ARE INVOLVED , 1947 .

[4]  M. J. Nadworny.  Frederick Taylor and Frank Gilbreth: Competition in Scientific Management , 1957, Business History Review.

[5]  Mikell P. Groover,et al.  Work systems and the methods, measurement, and management of work , 2006 .

[6]  Ofer Zwikael,et al.  A New Technique for Estimating the Distribution of a Stochastic Project Makespan , 2010, Int. J. Inf. Technol. Proj. Manag..

[7]  George Kanawaty,et al.  Introduction to work study. 4th revised edition , 1992 .

[8]  Jaeil Park,et al.  Time estimation method for manual assembly using MODAPTS technique in the product design stage , 2014 .

[9]  F. E. Satterthwaite.  An approximate distribution of estimates of variance components. , 1946, Biometrics.

[10]  Michel Aldanondo,et al.  ETO bid solutions definition and selection using configuration models and a multi-criteria approach , 2017, 2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM).

[11]  Mikhail Nikulin,et al.  On the problem of the means of weighted normal populations , 1995 .

[12]  R. Fisher.  THE FIDUCIAL ARGUMENT IN STATISTICAL INFERENCE , 1935 .

[13]  Yuval Cohen,et al.  A technique for integrated modelling of manual and automatic assembly , 2015 .

[14]  Wayne Lee.  EXperimental design and analysis , 1975 .

[16]  Richard Honeck,et al.  Experimental Design and Analysis , 2006 .

[17]  Hiroshi Nakayama,et al.  A study on setting standard time using work achievement quotient , 2002 .

[18]  Vladimir Polotski,et al.  Work-time identification and effort assessment: Application to fenestration industry and case study? , 2018 .

[19]  George Kanawaty,et al.  Introduction to work study , 1992 .

[20]  Yuval Cohen,et al.  From product documentation to a ‘method prototype’ and standard times: a new technique for complex manual assembly , 2014 .

[21]  Yao Wang,et al.  Incorporating motion analysis technology into modular arrangement of predetermined time standard (MODAPTS) , 2016 .

[22]  Kjell B. Zandin,et al.  MOST® Work Measurement Systems , 1990 .

[23]  Satterthwaite Fe.  An approximate distribution of estimates of variance components. , 1946 .