Skip to main content

Part of the book series: Lecture Notes in Mechanical Engineering ((LNME))

  • 2149 Accesses

Abstract

Modeling and planning the spare part item stock is a quite complex, but utmost rewarding task regarding the operational excellence of a manufacturing industry company. The challenge with the design is derived from multi-variable optimization tasks, including e.g. consumption rate of an item, division of stocking costs, and potential shortage costs. This applies especially in case of new spare part item with no history data for defining the consumption rate. The extra reward from a systematic spare part stock design process with the simulation based method is the ability to test stocking scenarios (optimized own stock vs stock outsourcing) and to obtain summary reports of different stocking cost type distributions (e.g. net interests costs, stock upkeep costs, order costs and shortage costs). In practice, the design process often focuses on finding optimal stocking parameters for hundreds or thousands of stock items that belong to the same criticality class. The optimization is either done to fulfill service rate requirement or for direct cost optimization purposes. The whole process helps to refine gathered history data and expert knowledge to true understanding, and with the systematic method to identify key indicators for the optimal spare part stock (min. risks, max. availability, min. costs). A Finnish advanced analytics expert company, called Ramentor Oy, together with the research institute (Tampere University of Technology) and several industry partners, has developed a systematic methodology combined with a pragmatic software tool that guides through the stock design and optimization process. The tool, called StockOptim, also provides interface to operative IT systems (ERP, CMMS, EAM) and to modern data analytics tools (e.g. ELMAS) for managing the precedent process modeling, data collection, criticality and risk analyses tasks. StockOptim simulates spare part stock events, calculates stocking costs and stock items shortage data (e.g. mean shortage time or mean shortage costs). The software is also able to find approximation for the items optimal stocking parameters (reorder point and order amount). The simulation based approach is flexible, also for adding new details or extensions, and not as exposed to distorting and restricting assumptions as analytic-numeric methods.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 329.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  • Al-Rifai, M. H., & Rossetti, (2007). An efficient heuristic optimization algorithm for a two-echelon (R, Q) inventory system. International Journal of Production Economics, 109, 195–213.

    Article  Google Scholar 

  • Hagmark, P.-E. & Pernu, H, (2006). Risk evaluation of spare part stock by stochastic simulation. In Proceedings and Monographs in Engineering, Water and Earth Sciences, Safety and Reliability for Managing Risk, pp. 525–529, Estroril, Portugal, Taylor & Francis, Boca Raton, FL, 18–22 Sept.

    Google Scholar 

  • Kennedy, W. J., Patterson, J. W., & Fredendall, L. D. (2002). An overview of recent literature on spare parts inventories. International Journal of Production Economics, 76, 201–215.

    Article  Google Scholar 

  • Tajbakhsh, M. M. (2010). On the distribution free continuous-review inventory model with a service level constraint. Computers & Industrial Engineering, 59(4), 1022–1024.

    Article  Google Scholar 

  • Whitley, D. (2001). An overview of evolutionary algorithms: practical issues and common pitfalls. Information and Software Technology, 43, 817–831.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Joel Turpela .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Turpela, J., Lehtinen, T. (2016). Spare Part Stock Modeling and Cost Optimization. In: Koskinen, K., et al. Proceedings of the 10th World Congress on Engineering Asset Management (WCEAM 2015). Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-27064-7_61

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-27064-7_61

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27062-3

  • Online ISBN: 978-3-319-27064-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics