PENN S TATE © T. W. S IMPSON PENN S TATE Timothy W. Simpson Professor of Mechanical & Industrial Engineering and Engineering Design The Pennsylvania State University University Park, PA USA phone: (814) ME Designing Product Families - IE 546 Assessing Variability in Commonality Indices © T. W. S IMPSON

PENN S TATE © T. W. S IMPSON Assessing Variability in PCI Research questions:  How and to what extent do humans introduce variability in the calculation of PCI?  How do we reduce those variations to provide a more systematic (and automatic) commonality assessment? For details on experiment, forms, templates, etc., see: Thevenot, H. J. and Simpson, T. W. (2007) “Guidelines to Minimize Variation when Estimating Product Line Commonality through Product Family Dissection,” Design Studies, Vol. 28, No. 2, pp PCI = n i x f 1i x f 2i x f 3i - i = 1 P P P n i - i = 1 P 1 ni2ni2 1 ni2ni2 x 100     Many aspects of PCI calculation are subject to human variability

PENN S TATE © T. W. S IMPSON Experimental Protocol 1.Read overview and sign informed consent form 2.Dissect each product to the lowest level possible 3.Identify the parts as being either: common, variant, or unique to each product within the product family 4.Take a picture of each product after it is dissected using the digital camera provided in the laboratory 5.Complete the Excel spreadsheet template where the rows represent the parts sorted by name, and the columns represent the different products in the family (an additional column was used to identify the commonality among parts in each product 6.Compute the PCI for one of the other product families that was dissected by another team using a new Excel spreadsheet template

PENN S TATE © T. W. S IMPSON Experimental Set Up Ordering of dissection and PCI calculation for each team: Products dissected in each family:

PENN S TATE © T. W. S IMPSON Example of a Completed Spreadsheet

PENN S TATE © T. W. S IMPSON Summary of Results There is little variation in the PCI values for the Fujifilm family (68.3 to 71.5) but considerable variation in the PCI values for the Mr. Coffee coffeemakers (58.8 to 74.5) and Kodak one-time-use cameras (41.5 to 63.3) There is consistency of the values within families: the PCI values for Kodak are always lower than the PCI values for Fujifilm even with the large range of variation The second team’s calculated PCI value is always higher than the first team’s calculated PCI value

PENN S TATE © T. W. S IMPSON Identifying the Sources of Variation We examined both dissection portion of the experiment and then the computation portion of the experiment and identified three major contributors to PCI variation: 1. Different levels of dissection 2. Parts omitted from analysis 3. Different values for f ji factors 1. Different levels of dissection: Specific rules should be developed to ensure that each team dissects their products to the same level Level of dissection varied by group, especially for electrical components

PENN S TATE © T. W. S IMPSON 2. Parts Omitted from Analysis A detailed parts list (BOM) would be helpful to minimize the number of omitted parts during analysis and ensure that they are named properly Many parts were voluntarily or involuntarily omitted during the analysis and computation of PCI  Cameras were small enough to arrange side- by-side, unlike coffeemakers  Some groups “rushed” to get done and overlooked components  Different naming schemes

PENN S TATE © T. W. S IMPSON Resolving Omitted Parts Analyzed results for each team for each product family:  Standardized part names and removed unique components  Identified reason for differences in parts within family  Completed omitted values using average scores Kodak single-use camera example:

PENN S TATE © T. W. S IMPSON “Corrected” PCI Values Using the “completed” data, each team’s PCI value was recalculated and “corrected”: Despite “corrections”, trends remain the same:  Variation remains small in the Fujifilm single-use cameras (68.3 to 71.5)  The PCI variations are still quite large for the Mr. Coffee coffeemakers and Kodak single-use cameras (54.4 to 79.0 and 47.0 to 64.0, respectively)  Second team’s PCI value still higher than first team’s PCI value …now we can examine differences in the f ji factors

PENN S TATE © T. W. S IMPSON 3. Differences in Values of f ji Factors Majority of variability in PCI values arose from f ji factors assigned by each team; consider Kodak family:

PENN S TATE © T. W. S IMPSON Percent Variation in f ji Factors by Family By performing this analysis for each family, we can compute the ratio of parts where each factor is different to the total number of parts: Observations based on these ratios:  Least amount of variation in f 3i factor (assembly and fastening)  Moderate amount of variation in f 2i factor (material and manf)  Most variation in f 1i factor (size and shape) with largest variation occurring in the Kodak single-use camera family Why the large variation in the Kodak family?

PENN S TATE © T. W. S IMPSON Variations in f 1i for Kodak Family Different interpretations of “identical size and shape”  Example: front covers of Kodak single-use cameras Different understanding of “identical material”  Example: different color film advance mechanisms We need more precise definitions for “identical”, “similar”, and “unique” parts, assemblies, materials, etc.

PENN S TATE © T. W. S IMPSON Student Feedback Students asked to complete a five-item questionnaire to evaluate their understanding of commonality before and after the product dissection activity (n = 24) Written feedback at the end of the semester:  “I liked the dissection of the Kodak single-use camera”  “It would be interesting if one team dissects competing products (e.g., Kodak and Fuji) and compares the range of PCI for each.” Future assessment will be more objective (e.g., pre- and post-test) in addition to self-reported measures