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COMPLEXITY INDEX FOR A DESIGN ACTIVITY

COMPLEXITY INDEX FOR A DESIGN ACTIVITY. SANJEEV SINHA PhD Student Supervisors: DR. A. I. THOMSON (DMEM) DR. B. KUMAR (CIVIL ENGG.) UNIVERSITY OF STRATHCLYDE, GLASGOW, SCOTLAND. RESEARCH OBJECTIVES. DEVELOP A MODEL FOR MEASURING THE COMPLEXITY OF A DESIGN ACTIVITY

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COMPLEXITY INDEX FOR A DESIGN ACTIVITY

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  1. COMPLEXITY INDEX FOR A DESIGN ACTIVITY SANJEEV SINHA PhD Student Supervisors: DR. A. I. THOMSON (DMEM) DR. B. KUMAR (CIVIL ENGG.) UNIVERSITY OF STRATHCLYDE, GLASGOW, SCOTLAND

  2. RESEARCH OBJECTIVES • DEVELOP A MODEL FOR MEASURING THE COMPLEXITY OF A DESIGN ACTIVITY • IMPLEMENTATION OF THE MODEL • VALIDATING THE MODEL USING CASE STUDIES

  3. NEED FOR MEASURING COMPLEXITY IN DESIGN CONTEXT • CHECK OVERRUNS IN COST AND SCHEDULE OF DESIGN PROJECTS • COMPLEXITY AS ONE OF THE TOOLS FOR DETERMINING MANAGERIAL ACTIONS • APPROPRIATE PRACTICAL ACCEPTANCE IN MANAGING PROJECTS

  4. IMPORTANCE OF MEASUREMENT “ When you can measure ----------- you know something about it; but when you cannot -------- your knowledge is of meagre and unsatisfactory kind - Lord Kelvin ‘You Cannot Control What You Cannot Measure’ - Tom De Marco

  5. CHANGING MARKET SCENARIO A CASE OF CONSTANT MOVING TARGET PRASAD BIREN,1998 DECREASEDTIMETO MARKET >5 YEARS 4-5 YEARS 2-3 YEARS 3-4 YEARS TIME TO MARKET TIME TO MARKET PRODUCT COMPLEXITY COMPLEX VERYCOMPLEX SIMPLE VERY SIMPLE INCREASED LEVEL OF COMPLEXITY YEARS

  6. PRELIMINARY INVESTIGATIONS • DEFINITION OF COMPLEXITY • Dynamic • Relative • Subjective • Context Dependent

  7. DESIGN COMPLEXITY AND ITS COMPLEXITY GENERATING FACTORS (CGFs) TIME WORK Availability of Resources Proximity of departments Length of the project Usage of Resources Familiarity with the tasks Geographical Locations DESIGN COMPLEXITY ETC. ETC. Competition for Resources Individual preference Company’s reputation Access to Resources Cultures involved Relationships among the workers ETC. SOCIAL MOTIVATIONAL ETC.

  8. PROPOSED MODEL FOR MEASURING COMPLEXITY OF A DESIGN ACTIVITY Partial CGFs Module Contextual Module Information Proceesing Information Content of the selected Pcgf Information Parameters (IP) Design Activity User Part of a context PCI OCI CGFs Type of Design Activities Pcgf Pcgfs PCI is Partial Complexity Index OCI is Overall Complexity Index

  9. Example: Manufacture of a pencil G F Wood Barrel E • Components of a pencil • E: Eraser • F: Ferrule • G: Graphite • Design Activities pertaining to: • Eraser: cut to length • Ferrule: blank, roll, stake • Wooden Barrel: saw, mill • Pencil:- assemble, paint, print, sharpen • Package-box, label,carton

  10. Problem Statement To measure the Complexity Index of machining activity involved in the manufacture of a pencil. Design activities involved are- • Machining • Assembling • Painting and packaging

  11. PROPOSED MODEL Assumptions • Human resources are skilled • Time associated with the different states of information parameters (IPs) has been set after taking into consideration the average skills needed by a machine operator to do that activity • Demand of the component is known

  12. COMPLEXITY MEASUREMENT: Function of information content associated with its Complexity Generating Factors(CGFs) N M Information Content = -Σ Σ pij log2 pij J=1 I=1 M= number of skills used N= number of Information Parameters (IPs) at skill j pij = probability of using the IP in state i

  13. CALCULATIONS Sub-Process Used: Machining Skill used : Basic Machining Operations Information parameters (IPs): Cutting, Milling Different States of IPs: set-up, production, idle All times are measured in minutes CUTTING MILLING RESOURCE INFORMATION Set up time 3 2 ----- 3 x 600 Total Set up time 2 Production time/pencil 6 6 x 600 2 x 600 Total Production time Idle time (Available time- total set up time - total prod. time) 3398-3x600-2x600 = 398 7000-2-6x600 = 3398

  14. CALCULATIONS REGARDING INFORMATION CONTENT OF IPs IN DIFFERENT STATES Information Parameters Cutting Production Idle Set up States 3398/7000= 0.4854 3600/7000= 0.5143 Probabilities 2/7000= 0.00029 Information Content (H) 0.5062 0.4934 0.0034 ‘H’ for Cutting 0.4968 Milling Production Set up Idle States Probabilities 1800/3398= 0.2571 1200/3398= 0.6857 398/3398= 0.5714 Information Content (H) 0.4856 0.3623 0.5303 ‘H’ for Milling 1.0159

  15. RESULTS Complexity Index (CI) of Machining activity (sub-activity) = Information Content associated with the information parameters (IPs) at various states of machining = 0.4968 + 1.0159 =1.5127 Overall Complexity Index (OCI)= Summation of CIs for all the design activities involved in the manufacture of a pencil

  16. DISCUSSIONS • Complexity Measurement • Inherent Variety • Uncertainty on account of variety • CI measurement does not indicate the cause of complexity • Design Activities can be distinguished on the basis of the CI • Occurrence of idle state results in reduced complexity

  17. CONCLUSIONS • COMPLEXITY INDEX OF A DESIGN ACTIVITY IS MEASURABLE TO A CERTAIN EXTENT WITHIN THE STATED ASSUMPTIONS • COMPLEXITY OF DESIGN ACTIVITY IS DEPENDENT ON THE AMOUNT OF INFORMATION CONTENT ASSOCIATED WITH ITS INFORMATION PARAMETERS(IPs)

  18. LIMITATIONS • PROCESS SPECIFIC • INFORMATION DEPENDENT • VARIABLE RESULTS • PROCESSING TIME

  19. FUTURE WORK • Information Parameters (IPs) with reference to other CGFs have to be identified and implemented • Extend the model in the area of project management • Modification of the equation used

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