The behavior of lean and the theory of constraints in the wider supply chain: a simulation-based comparative study delving deeper into the impact of noise

  1. Puche-Regaliza, J. C. 1
  2. Ponte, B. 2
  3. Costas, J. 3
  4. Pino, R. 2
  5. de la Fuente, D. 2
  1. 1 Department of Applied Economics, Faculty of Economics and Business, University of Burgos
  2. 2 Universidad de Oviedo
    info

    Universidad de Oviedo

    Oviedo, España

    ROR https://ror.org/006gksa02

  3. 3 Department of Engineering, Florida Centre de Formació, Florida Universitária, Catarroja, Valencia
Libro:
Lecture Notes in Management and Industrial Engineering

Editorial: Springer

ISSN: 2198-0772 2198-0780

ISBN: 9783030677077

Ano de publicación: 2021

Páxinas: 149-159

Tipo: Capítulo de libro

DOI: 10.1007/978-3-030-67708-4_16 GOOGLE SCHOLAR lock_openAcceso aberto editor

Obxectivos de Desenvolvemento Sustentable

Referencias bibliográficas

  • Cannella S, Ciancimino E (2010) On the bullwhip avoidance phase: supply chain collaboration and order smoothing. Int J Prod Res 48(22):6739–6776
  • Chatfield DC, Pritchard AM (2013) Returns and the bullwhip effect. Transp Res Part E: Log Transp Rev 49(1):159–175
  • Costas J, Ponte B, de la Fuente D, Pino R, Puche J (2015) Applying Goldratt’s theory of constraints to reduce the bullwhip effect through agent-based modeling. Exp Syst Appl 42(4):2049–2060
  • Costas J, Ponte B, de la Fuente D, Lozano J, Parreño J (2017) Agents playing the beer distribution game: solving the dilemma through the drum-buffer-rope methodology. Springer, In Engineering Systems and Networks, pp 337–345
  • Dominguez R, Framinan J M, Cannella S. (2014) Serial vs. divergent supply chain networks: a comparative analysis of the bullwhip effect. Int J Prod Res 52(7):2194–2210
  • Gardiner SC, Blackstone JH Jr, Gardiner LR (1993) Drum-buffer-rope and buffer management: Impact on production management study and practices. Int J Oper Prod Manag 13(6):68–78
  • Ghadimi P, Toosi FG, Heavey C (2018) A multi-agent systems approach for sustainable supplier selection and order allocation in a partnership supply chain. Eur J Oper Res 269(1):286–301
  • Goldratt EM (1990) Theory of constraints. North River, Croton-on-Hudson, NY
  • Goltsos TE, Ponte B, Wang S, Liu Y, Naim MM, Syntetos AA (2018) The boomerang returns? Accounting for the impact of uncertainties on the dynamics of remanufacturing systems. Int J Prod Res, in press
  • Govindan K, Soleimani H, Kannan D (2015) Reverse logistics and closed-loop supply chain: a comprehensive review to explore the future. Eur J Oper Res 240(3):603–626
  • Grünwald H, Striekwold PET, Weeda PJ (1989) A framework for quantitative comparison of production control concepts. Int J Prod Res 27(2):281–292
  • Guide VDR, Harrison TP, van Wassenhove LN (2003) The challenge of closed-loop supply chains. Interfaces 33(6):3–6
  • Gupta MC, Boyd LH (2008) Theory of Constraints: a theory for operations management. Int J Oper Prod Manag 28(10):991–1012
  • Hines P, Holweg M, Rich N (2004) Learning to evolve: A review of contemporary lean thinking. International Journal of Operations & Production Management 24(10):994–1011
  • Hilmola OP, Gupta M (2015) Throughput accounting and performance of a manufacturing company under stochastic demand and scrap rates. Exp Syst Appl 42(22):8423–8431
  • Holweg M, Bicheno J (2002) Supply chain simulation–a tool for education, enhancement and endeavour. Int J Prod Econ 78(2):163–175
  • Ikeziri LM, Souza FBD, Gupta MC, de Camargo Fiorini P (2018) Theory of constraints: review and bibliometric analysis. International Journal of Production Research, in press
  • Jairman WE (1963) Problems in industrial dynamics. MIT Press, Cambridge, MA
  • Jensen K, Kristensen LM, Wells L (2007) Coloured Petri Nets and CPN Tools for modelling and validation of concurrent systems. Int J Softw Tools Technol Transfer 9(3–4):213–254
  • Jodlbauer H, Huber A (2008) Service-level performance of MRP, kanban, CONWIP and DBR due to parameter stability and environmental robustness. Int J Prod Res 46(8):2179–2195
  • Junior ML, Godinho Filho M (2010) Variations of the kanban system: literature review and classification. Int J Prod Econ 125(1):13–21
  • Koh SG, Bulfin RL (2004) Comparison of DBR with CONWIP in an unbalanced production line with three stations. Int J Prod Res 42(2):391–404
  • Liker JK (1997) Becoming lean: inside stories of US manufacturers. CRC Press, New York, NY
  • Mabin VJ, Balderstone SJ (2003) The performance of the theory of constraints methodology: analysis and discussion of successful TOC applications. Int J Oper Prod Manag 23(6):568–595
  • MacArthur JB (1996) From activity-based costing to throughput accounting. Strategic Finance 77(10):30
  • Macdonald JR, Frommer ID, Karaesmen IZ (2013) Decision making in the beer game and supply chain performance. Oper Manag Res 6(3–4):119–126
  • Martínez-Jurado PJ, Moyano-Fuentes J (2014) Lean management, supply chain management and sustainability: a literature review. J Cleaner Prod 85:134–150
  • Moore R, Schinkopf L (1998) Theory of constraints and lean manufacturing: friends or foes?. Chesapeake Consulting. www.tocca.com.au/uploaded/documents/lean%20and%20toc.pdf
  • Naylor JB, Naim MM, Berry D (1999) Leagility: Integrating the lean and agile manufacturing paradigms in the total supply chain. Int J Prod Econ 62(1–2):107–118
  • Ohno T (1988) Toyota production system: beyond large scale production. Productivity Press, Cambridge, MA
  • Ponte B, Costas J, Puche J, de la Fuente D, Pino R (2016) Holism versus reductionism in supply chain management: an economic analysis. Decis Support Syst 86:83–94
  • Ponte B, Costas J, Puche J, Pino R, de la Fuente D (2018) The value of lead time reduction and stabilization: a comparison between traditional and collaborative supply chains. Transp Res Part E 111:165–185
  • Puche J, Costas J, Ponte B, Pino R, de la Fuente D (2019) The effect of supply chain noise on the financial performance of Kanban and Drum-Buffer-Rope: An agent-based perspective. Exp Syst Appl 120:87–102
  • Puche J, Ponte B, Costas J, Pino R, de la Fuente D (2016) Systemic approach to supply chain management through the viable system model and the theory of constraints. Prod Plann Control 27(5):421–430
  • Pujari S, Mukhopadhyay S (2012) Petri net: a tool for modeling and analyze multi-agent oriented systems. Int J Intell Syst Appl 10:103–112
  • Senge P (1990) The fifth discipline: the art and practice of the learning organization. Currency/Doubleday, New York
  • Simatupang TM, Sridharan R (2002) The collaborative supply chain. Int J Log Manag 13(1):15–30
  • Simatupang TM, Wright AC, Sridharan R (2004) Applying the theory of constraints to supply chain collaboration. Supply Chain Manag Int J 9(1):57–70
  • Takahashi K, Morikawa K, Chen YC (2007) Comparing kanban control with the theory of constraints using Markov chains. Int J Prod Res 45(16):3599–3617
  • Takahashi K, Nakamura N (2002) Comparing reactive Kanban and reactive CONWIP. Prod Plann Control 13(8):702–714
  • Watson KJ, Blackstone JH, Gardiner SC (2007) The evolution of a management philosophy: The theory of constraints. J Oper Manag 25(2):387–402
  • Watson KJ, Patti A (2008) A comparison of JIT and TOC buffering philosophies on system performance with unplanned machine downtime. Int J Prod Res 46(7):1869–1885
  • Wilensky U (1999) NetLogo. The Center for Connected Learning and Computer-nased Modeling. Northwetern University. https://ccl.northwetern.edu/netlogo/
  • Womack JP, Jones DT, Roos D (1990) The machine that changed the world: the story of lean production. Macmillan/Rawson Associates, New York, NY