Value Stream Maps for Industrial Energy Efficiency

Part of the Green Energy and Technology book series (GREEN, volume 129)


Lean thinking is an engineering approach to avoid non-value adding tasks or processes in manufacturing. Most of the lean studies in the energy field are focused on savings in manufacturing processes. This paper suggests a future-oriented energy value stream mapping approach that aims to improve energy efficiency in small- and medium-sized manufacturing companies. Energy value stream mapping is a graphical technique that allows identifying the level of energy use and, thereby, discovering saving opportunities at each step of different processes either in production or in facility support. To analyze the possible outcomes of improvement options, future scenarios are developed using Bayesian networks. The suggested model can be used not only for diagnostic purposes but also for energy budgeting and saving measures. An application is given to demonstrate the use of energy value stream maps (E-VSMs).


Energy Efficiency Bayesian Network Energy Usage Joint Probability Distribution Energy Efficiency Improvement 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Abbas Z, Khaswala N, Irani S (2001) Value network mapping (VNM): visualization and analysis of multiple flows in value stream maps. Proceeding of the lean management solutions conference, September 10–11; St. Louis, MOGoogle Scholar
  2. Abdulaziz EA, Saidur R, Mekhilef S (2011) A review on energy saving strategies in industrial sector. Renew Sustain Energy Rev 15:150–168CrossRefGoogle Scholar
  3. Abdulmalek FA, Rajgopal J (2007) Analyzing the benefits of lean manufacturing and value stream mapping via simulation: a process sector case study. Int J Prod Econ 107:223–236CrossRefGoogle Scholar
  4. Antony J (2011) Six sigma vs lean: some perspectives from leading academics and practitioners. Int J Prod Perform Manag 60:185–190CrossRefGoogle Scholar
  5. Ben-Gal I (2007) Bayesian networks. In: Ruggeri F, Faltin F, Kenett R (eds) Encyclopedia of statistics in quality & reliability. Wiley, LondonGoogle Scholar
  6. Bunse K, Vodicka M, Schönsleben P, Brilhart M, Ernst FO (2009) Integrating energy efficiency performance in production management—gap analysis between industrial needs and scientific literature. J Clean Prod 19:667–679CrossRefGoogle Scholar
  7. Chai K, Yeo C (2012) Overcoming energy efficiency barriers through systems approach-A conceptual framework. Energy Policy 46:460–472CrossRefGoogle Scholar
  8. Charniak E (1991) Bayesian networks without tears. AI Mag 12:50–63Google Scholar
  9. Cinar D, Kayakutlu G (2010) Scenario analysis using Bayesian networks: a case study in energy sector. Knowl-Based Syst 23:267–276CrossRefGoogle Scholar
  10. Cooper G, Herskovits E (1992) A Bayesian method for the induction of probabilistic networks from data. Mach Learn 9:309–347MATHGoogle Scholar
  11. Fraizer RC (2008) Bandwidth analysis, lean methods, and decision science to select energy management projects in manufacturing. Energy Eng 105:24–45Google Scholar
  12. Frey BJ (1998) Graphical models for machine learning and digital communication. MIT Press, BostonGoogle Scholar
  13. Garcia P, Amandi A, Schiaffino S, Campo M (2007) Evaluating bayesian networks’ precision for detecting student’s learning styles. Comput Educ 49:794–808CrossRefGoogle Scholar
  14. Graus W, Blomen E, Worrell E (2011) Global energy efficiency improvement in the long term: a demand- and supply-side perspective. Energ Effi 4:435–463CrossRefGoogle Scholar
  15. Gurumurthy A, Kodali R (2011) Design of lean manufacturing systems using value stream mapping with simulation. J Manufact Technol Manag 22:444–473CrossRefGoogle Scholar
  16. Haque B, James-Moore M (2004) Applying lean thinking to new product introduction. J Eng Des 15:1–31CrossRefGoogle Scholar
  17. Heckerman D (2008) A Tutorial on Learning with Bayesian Networks. In: Holmes DE, Jain LC (eds) Innovations in Bayesian networks: theory and applications. Springer, BerlinGoogle Scholar
  18. Heckerman D, Geiger D, Chickering DM (1995) Learning Bayesian networks: the combination of knowledge and statistical data. Mach Learn 20:197–243MATHGoogle Scholar
  19. Jones BJ, Jenkinson I, Yang Z, Wang J (2010) The use of Bayesian network modelling for maintenance planning in a manufacturing industry. Reliab Eng Syst Saf 95:267–277CrossRefGoogle Scholar
  20. Jordan MI (ed) (1999) Learning in graphical models. MIT Press, BostonGoogle Scholar
  21. Kayakutlu G, Şatoğlu ŞI, Durmuşoğlu B (2007) Waste detection and optimisation by applying bayesian causal map technique on value stream maps. Proceedings of 19th international conference on production research, Valparaiso, ChileGoogle Scholar
  22. Kissock JK, Eger C (2009) Measuring industrial energy savings. Appl Energy 85:347–361CrossRefGoogle Scholar
  23. Kissock K, Seryak J (2004) Lean Energy Analysis: Identifying, Discovering and Tracking Energy Savings Potential, Advanced Energy and Fuel Cell Technologies Conference, Society of Manufacturing Engineers, Livonia, MI, October 11–13Google Scholar
  24. Kjærulff UB, Madsen AL (2008) Bayesian networks and influence diagrams: a guide to construction and analysis. Springer, BerlinGoogle Scholar
  25. Lasa IS, Laburu CO, Vila RD (2008) An evaluation of the value stream mapping tool. Bus Process Manag J 14:39–52CrossRefGoogle Scholar
  26. Nadkarni S, Shenoy PP (2001) A Bayesian network approach to making inferences in causal maps. Eur J Oper Res 128:479–498MATHCrossRefGoogle Scholar
  27. Nadkarni S, Shenoy PP (2004) A causal mapping approach to constructing Bayesian networks. Decis Support Syst 38(2):259–281CrossRefGoogle Scholar
  28. Nagesha N (2008) Role of energy efficiency in sustainable development of small-scale industry clusters: an empirical study. Energy for Sustainable Development 12(3):34–39CrossRefGoogle Scholar
  29. Pavnaskar SJ, Gershenson JK (2003) Lean and Sustainable Enterprises: Extending Lean Tools Systematically. Proceedings of the 3rd Annual Lean Management Solutions Conference, Atlanta, Georgia, SeptemberGoogle Scholar
  30. Pavnaskar SJ, Gershenson JK, Jambekar AB (2004) Classification scheme for lean manufacturing tools. Int J Prod Res 41(13):3075–3090CrossRefGoogle Scholar
  31. Pearl J (1988) Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann, San MateoGoogle Scholar
  32. Pearl J, Russel S (2001) Bayesian Networks. In: Arbib M (ed) Handbook of brain theory and neural networks. MIT Press, CambridgeGoogle Scholar
  33. Rother M, Shook J (1999) Learning to See. The Lean Enterprise Institute, Inc., BrooklineGoogle Scholar
  34. Russell SJ, Norvig P (2002) Artificial intelligence: a modern approach, 2nd edn. Prentice Hall, Upper Saddle RiverGoogle Scholar
  35. Schmidt M, Raible C, Keil R, Graber M (2012) Energy and material stream mapping, accessed 20/06/2012,
  36. Shah R, Ward PT (2007) Defining and developing measures of lean production. J Oper Manag 25:785–805CrossRefGoogle Scholar
  37. Singh B, Sharma SK (2009) Value stream mapping as a versatile tool for lean implementation: an Indian case study of a manufacturing firm. Meas Bus Excell 13(3):58–68CrossRefGoogle Scholar
  38. Speigelhalter DJ, Dawid AP, Lauritzen SL, Cowell RG (1993) Bayesian analysis in expert systems. Stat Sci 8(3):219–247CrossRefGoogle Scholar
  39. Steck H, Tresp V (1999) Bayesian belief networks for data mining. Proceedings of the 2nd Workshop on Data Mining und Data Warehousing als Grundlage moderner entscheidungsunterstuetzender Systeme, DWDW99, Sammelband, Universität MagdeburgGoogle Scholar
  40. Thollander P, Danestig M, Rohdin P (2007) Energy policies for increased industrial energy efficiency: evaluation of a local energy program for manufacturing SMEs. Energy Policy 35:774–5783CrossRefGoogle Scholar
  41. US Environmental Protection Agency (2007) Lean and Energy Toolkit, accessed 21.06.2012, EPA CODE: EPA-100-K-07-003
  42. Verhoeven M, Arentze TA, Timmermans HJP, Waerden PJHJ Van Der (2006) Modelling consumer choice behaviour with Bayesian belief Networks. In: Proceedings of the RARCS conference, Budapest, HungaryGoogle Scholar
  43. Vorobev N (1963) Markov measures and Markov extensions. Theor Probab Appl 8:420–429CrossRefGoogle Scholar
  44. Womack JP, Jones DT (1996) Lean thinking. Simon & Schuster, New YorkGoogle Scholar
  45. Zheng M, Reader GT (2004) Energy efficiency analyses of active flow after treatment systems for lean burn internal combustion engines. Energy Convers Manag 45(15–16):2473–2493CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  1. 1.Energy Instituteİstanbul Technical UniversityIstanbulTurkey
  2. 2.Industrial Engineering Departmentİstanbul Technical UniversityIstanbulTurkey

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