Resource optimization of product development projects with time-varying dependency structure

Abstract

Project managers are continuously under pressure to shorten product development durations. One practical approach for reducing the project duration is lessening dependencies between different development components and teams. However, most of the resource allocation strategies for lessening dependencies place the implicit and simplistic assumption that the dependency structure between components is static (i.e., does not change over time). This assumption, however, does not necessarily hold true in all product development projects. In this paper, we present an analytical framework for optimally allocating resources to shorten the lead time of product development projects having a time-varying dependency structure. We build our theoretical framework on a linear system model of product development processes, in which system integration and local development teams exchange information asynchronously and aperiodically. Utilizing a convexity result from the matrix theory, we show that the optimal resource allocation can be efficiently found by solving a convex optimization problem. We provide illustrative examples to demonstrate the proposed framework. We also present boundary analyses based on major graph models to provide managerial guidelines for improving empirical PD processes.

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References

  1. Adler P, Mandelbaum A, Nguyen V, Schwerer E (1995) Project to process management: empirically-based framework for analyzing product development time. Manag Sci 41(3):458–484

    MATH  Article  Google Scholar 

  2. Ahmadi R, Wang RH (1999) Managing development risk in product design processes. Oper Res 47:235–246

    MATH  Article  Google Scholar 

  3. Alcaraz J, Maroto C, Ruiz R (2003) Solving the multi-mode resource-constrained project scheduling problem with genetic algorithms. J Oper Res Soc 54(6):614–626

    MATH  Article  Google Scholar 

  4. Baldwin C, Clark K (2000) Design rules: the power of modularity. MIT Press, Cambridge

    Google Scholar 

  5. Barabási AL, Albert R (1999) Emergence of scaling in random networks. Science 286(5439):509–512

    MathSciNet  MATH  Article  Google Scholar 

  6. Boctor FF (1996) A new and efficient heuristic for scheduling projects with resource restrictions and multiple execution modes. Eur J Oper Res 90(2):349–361

    MathSciNet  MATH  Article  Google Scholar 

  7. Borjesson F, Hölttä-Otto K (2014) A module generation algorithm for product architecture based on component interactions and strategic drivers. Res Eng Des 25(1):31–51

    Article  Google Scholar 

  8. Boyd S, Vandenberghe L (2004) Convex optimization. Cambridge University Press, Cambridge

    Google Scholar 

  9. Boyd S, Kim SJ, Vandenberghe L, Hassibi A (2007) A tutorial on geometric programming. Optim Eng 8(1):67–127

    MathSciNet  MATH  Article  Google Scholar 

  10. Braha D, Bar-Yam Y (2004a) Information flow structure in large-scale product development organizational networks. J Inf Technol 19(4):244–253

    Article  Google Scholar 

  11. Braha D, Bar-Yam Y (2004b) Topology of large-scale engineering problem-solving networks. Phys Rev E 69(1):016113

    Article  Google Scholar 

  12. Braha D, Bar-Yam Y (2006) From centrality to temporary fame: dynamic centrality in complex networks. Complexity 12(2):59–63

    Article  Google Scholar 

  13. Braha D, Bar-Yam Y (2007) The statistical mechanics of complex product development: empirical and analytical results. Manag Sci 53(7):1127–1145

    MATH  Article  Google Scholar 

  14. Braha D, Bar-Yam Y (2009) Time-dependent complex networks: dynamic centrality, dynamic motifs, and cycles of social interactions. In: Gross T, Sayama H (eds) Adaptive networks: theory, models and applications. Springer, Berlin, pp 39–50

    Google Scholar 

  15. Browning TR (2016) Design structure matrix extensions and innovations: a survey and new opportunities. IEEE Trans Eng Manag 63(1):27–52

    MathSciNet  Article  Google Scholar 

  16. Browning TR, Ramasesh RV (2007) A survey of activity network-based process models for managing product development projects. Prod Oper Manag 16(2):217–240

    Article  Google Scholar 

  17. Cai J, Liu X, Xiao Z, Liu J (2009) Improving supply chain performance management: a systematic approach to analyzing iterative KPI accomplishment. Decis Support Syst 46(2):512–521

    Article  Google Scholar 

  18. Chen J, Reilly RR, Lynn GS (2012) New product development speed: Too much of a good thing? J Product Innov Manag 29(2):288–303

    Article  Google Scholar 

  19. Cheng H, Chu X (2012) Task assignment with multiskilled employees and multiple modes for product development projects. Int J Adv Manuf Technol 61(1–4):391–403

    Article  Google Scholar 

  20. Cicmil S, Williams T, Thomas J, Hodgson D (2006) Rethinking project management: researching the actuality of projects. Int J Project Manag 24(8):675–686

    Article  Google Scholar 

  21. Collyer S, Warren CM (2009) Project management approaches for dynamic environments. Int J Project Manag 27(4):355–364

    Article  Google Scholar 

  22. Cooke-Davies T (2002) The “real” success factors on projects. Int J Project Manag 20(3):185–190

    Article  Google Scholar 

  23. Cui Q, Hastak M, Halpin D (2010) Systems analysis of project cash flow management strategies. Constr Manag Econ 28(4):361–376

    Article  Google Scholar 

  24. Erdős L, Rényi A (1959) On random graphs. I. Publ Math 6:290–297

    MathSciNet  MATH  Google Scholar 

  25. Fefferman N, Ng K (2007) How disease models in static networks can fail to approximate disease in dynamic networks. Phys Rev E 76(3):031919

    MathSciNet  Article  Google Scholar 

  26. Frenken K (2006) A fitness landscape approach to technological complexity, modularity, and vertical disintegration. Struct Change Econ Dyn 17(3):288–305

    Article  Google Scholar 

  27. Hahn GJ, Kuhn H (2012) Designing decision support systems for value-based management: a survey and an architecture. Decis Support Syst 53(3):591–598

    Article  Google Scholar 

  28. Hartmann S, Briskorn D (2010) A survey of variants and extensions of the resource-constrained project scheduling problem. Eur J Oper Res 207(1):1–14

    MathSciNet  MATH  Article  Google Scholar 

  29. Hill SA, Braha D (2010) Dynamic model of time-dependent complex networks. Phys Rev E 82(4):046105

    Article  Google Scholar 

  30. Holme P (2015) Modern temporal network theory: a colloquium. Eur Phys J B 88(9):234

    Article  Google Scholar 

  31. Hölttä-Otto K, de Weck O (2007) Degree of modularity in engineering systems and products with technical and business constraints. Concurr Eng Res Appl 15(2):113–125

    Article  Google Scholar 

  32. Huberman BA, Glance NS (1993) Evolutionary games and computer simulations. Proc Natl Acad Sci 90(16):7716–7718

    MATH  Article  Google Scholar 

  33. Huberman BA, Wilkinson DM (2005) Performance variability and project dynamics. Comput Math Org Theory 11(4):307–332

    MATH  Article  Google Scholar 

  34. Joglekar NR, Ford DN (2005) Product development resource allocation with foresight. Eur J Oper Res 160(1):72–87

    MATH  Article  Google Scholar 

  35. Joglekar NR, Yassine AA, Eppinger SD, Whitney DE (2001) Performance of coupled product development activities with a deadline. Manag Sci 47(12):1605–1620

    Article  Google Scholar 

  36. Kim D (2007) On representations and dynamic analysis of concurrent engineering design. J Eng Des 18(3):265–277

    Article  Google Scholar 

  37. Kingman JFC (1961) A convexity property of positive matrices. Q J Math 12(1):283–284

    MathSciNet  MATH  Article  Google Scholar 

  38. Krishnan V, Ulrich KT (2001) Product development decisions: a review of the literature. Manag Sci 47(1):1–21

    Article  Google Scholar 

  39. Lee H, Padmanabhan V, Whang S (1997) Information distortion in a supply chain: the bullwhip effect. Manag Sci 43:546–558

    MATH  Article  Google Scholar 

  40. Lee SG, Ong KL, Khoo LP (2004) Control and monitoring of concurrent design tasks in a dynamic environment. Concurr Eng Res Appl 12(1):59–66

    Article  Google Scholar 

  41. Lin H, Antsaklis PJ (2007) Switching stabilizability for continuous-time uncertain switched linear systems. IEEE Trans Autom Control 52(4):633–646

    MathSciNet  MATH  Article  Google Scholar 

  42. Lin H, Antsaklis P (2009) Stability and stabilizability of switched linear systems: a survey of recent results. IEEE Trans Autom Control 54(2):308–322

    MathSciNet  MATH  Article  Google Scholar 

  43. Loch CH, Terwiesch C (1998) Communication and uncertainty in concurrent engineering. Manag Sci 44(8):1032–1048

    MATH  Article  Google Scholar 

  44. Loch C, Terwiesch C (1999) Accelerating the process of engineering change orders: capacity and congestion effects. J Product Innov Manag 16:145–159

    Article  Google Scholar 

  45. Martin MV, Ishii K (2002) Design for variety: developing standardized and modularized product platform architectures. Res Eng Des 13(4):213–235

    Article  Google Scholar 

  46. Masuda N, Lambiotte R (2016) A guide to temporal networks. World Scientific Publishing, Singapore

    Google Scholar 

  47. McDaniel CD (1996) A linear system framework for analyzing the automotive appearance design process. Ph.D. thesis, Massachusetts Institute of Technology

  48. Mihm J, Loch C, Huchzermeier A (2003) Problem-solving oscillations in complex engineering projects. Manag Sci 49(6):733–750

    Article  Google Scholar 

  49. Muller R, Geraldi J, Turner JR, Müller R, Geraldi J, Turner JR (2012) Relationships between leadership and success in different types of project complexities. IEEE Trans Eng Manag 59(1):77–90

    Article  Google Scholar 

  50. Newman MEJ (2010) Networks: an introduction. Oxford University Press, Oxford

    Google Scholar 

  51. Ogura M, Martin CF (2015) Stability analysis of linear systems subject to regenerative switchings. Syst Control Lett 75:94–100

    MathSciNet  MATH  Article  Google Scholar 

  52. Ogura M, Preciado VM (2016) Stability of Markov regenerative switched linear systems. Automatica 69:169–175

    MathSciNet  MATH  Article  Google Scholar 

  53. Ogura M, Preciado VM (2017) Optimal design of switched networks of positive linear systems via geometric programming. IEEE Trans Control Netw Syst 4(2):213–222

    MathSciNet  MATH  Article  Google Scholar 

  54. Ong KL, Lee SG, Khoo LP (2003) Homogeneous state-space representation of concurrent design. J Eng Des 14(2):221–245

    Article  Google Scholar 

  55. Patterson JH, Brian Talbot F, Slowinski R, Wegłarz J (1990) Computational experience with a backtracking algorithm for solving a general class of precedence and resource-constrained scheduling problems. Eur J Oper Res 49(1):68–79

    MATH  Article  Google Scholar 

  56. Peteghem VV, Vanhoucke M (2010) A genetic algorithm for the preemptive and non-preemptive multi-mode resource-constrained project scheduling problem. Eur J Oper Res 201(2):409–418

    MathSciNet  MATH  Article  Google Scholar 

  57. PMI (2013) A guide to the project management body of knowledge. Project Management Institute, Newtown Square

    Google Scholar 

  58. Richards J (1983) Analysis of periodically time varying systems. Springer, New York

    Google Scholar 

  59. Serrador P, Turner R (2015) The relationship between project success and project efficiency. Project Manag J 46(1):30–39

    Article  Google Scholar 

  60. Smith WL (1955) Regenerative stochastic processes. Proc R Soc A Math Phys Eng Sci 232(1188):6–31

    MathSciNet  MATH  Article  Google Scholar 

  61. Smith RP, Eppinger SD (1997) Identifying controlling features of engineering design iteration. Manag Sci 43(3):276–293

    MATH  Article  Google Scholar 

  62. Vazquez A, Rácz B, Lukács A, Barabasi AL (2007) Impact of non-Poissonian activity patterns on spreading processes. Phys Rev Lett 98(15):158702

    Article  Google Scholar 

  63. Watts DJ, Strogatz SH (1998) Collective dynamics of ‘small-world’ networks. Nature 393(6684):440–442

    MATH  Article  Google Scholar 

  64. Xiao R, Chen T, Ju C (2011) Research on product development iterations based on feedback control theory in a dynamic environment. Int J Innov Comput Inf Control 7(5):2669–2688

    Google Scholar 

  65. Yassine AA, Naoum-Sawaya J (2016) Architecture, performance, and investment in product development networks. J Mech Des 139(1):011101

    Article  Google Scholar 

  66. Yassine A, Joglekar N, Braha D, Eppinger S, Whitney D (2003) Information hiding in product development: the design churn effect. Res Eng Des 14(3):145–161

    Article  Google Scholar 

  67. Yu TL, Yassine AA, Goldberg DE (2007) An information theoretic method for developing modular architectures using genetic algorithms. Res Eng Des 18(2):91–109

    Article  Google Scholar 

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Acknowledgements

This work is funded in part by JSPS KAKENHI Grant number 18K13777 and the open collaborative research program at National Institute of Informatics (NII) Japan (FY2018).

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Correspondence to Masaki Ogura.

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Ogura, M., Harada, J., Kishida, M. et al. Resource optimization of product development projects with time-varying dependency structure. Res Eng Design 30, 435–452 (2019). https://doi.org/10.1007/s00163-019-00316-6

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Keywords

  • Project management
  • Resource management
  • Resource allocation systems
  • Time/cost/performance trade-offs
  • Project planning