Skip to main content
SpringerLink
Log in

Dynamics and Uncertainties in Tactical Supply Chain Design for New Product Introduction

  • Chapter
  • First Online:
Quantitative Models for Value-Based Supply Chain Management

Part of the book series: Lecture Notes in Economics and Mathematical Systems ((LNE,volume 660))

Abstract

The task of tactical supply chain design (TSCD) for new product introduction (NPI) is to establish product-specific supply chains that ensure cost-efficiently on time and in full availability of new high quality products over the whole product life cycle (PLC). Besides cross-regional network aspects and inter-disciplinary factors the problem complexity is driven by dynamics and uncertainties of short PLCs. This raises the questions to what extent these complexity drivers affect supply chain design scenarios and how they are linked to company value. In the conceptual part of this chapter, dynamics and uncertainties are integrated in a proposed framework for value-based SCM which is linked to a discrete event simulation model. In the empirical part of this chapter, impacts of these complexity drivers on TSCD scenarios for NPI are illustrated by a case example from the fast moving consumer goods industry.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight 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

References

  • Adebanjo, D., and R. Mann. 2000. Identifying problems in forecasting consumer demand in the fast moving consumer goods sector. Benchmarking: An International Journal 7(3): 223–230.

    Article  Google Scholar 

  • Ayag, Z. 2005. An integrated approach to evaluating conceptual design alternatives in a new product development environment. International Journal of Production Research 43(4): 687–713.

    Article  Google Scholar 

  • Bernandes, E., and M. Hanna. 2009. A theoretical review of flexibility, agility and responsiveness in the operations management literature. International Journal of Operations and Production Management 29(1): 30–63.

    Article  Google Scholar 

  • Biswas, S., and Y. Narahari. 2004. Object oriented modeling and decision support for supply chains. European Journal of Operational Research 153(3): 704–726.

    Article  Google Scholar 

  • Blackhurst, J., T. Wu, and P. O’Grady. 2004. Network-based approach to modelling uncertainty in a supply chain. International Journal of Production Research 42(8): 1639–1658.

    Article  Google Scholar 

  • Bottani, E., and R. Montanari. 2010. Supply chain design and cost analysis through simulation. International Journal of Production Research 48(10): 2859–2886.

    Article  Google Scholar 

  • Brandenburg, M., and S. Seuring. 2010a. The influence of supply chain cost and working capital on company value: Benchmarking companies from the fast moving consumer goods industry. Logistics Research 3(4): 233–248.

    Article  Google Scholar 

  • Brandenburg, M., and S. Seuring. 2010b. A model for quantifying impacts of supply chain cost and working capital on the company value. Lecture Notes in Business Information Processing 46: 107–117.

    Article  Google Scholar 

  • Brandenburg, M., and S. Seuring. 2010c. Value-based strategic supply chain planning. In Pioneering solutions in supply chain management – A comprehensive insight into current management approaches, Operations and technology management, vol. 14, W. Kersten, T. Blecker, and C. Lüthje, 185–196. Berlin: Erich Schmidt Verlag.

    Google Scholar 

  • Butler, R., J. Ammons, and J. Sokol. 2006. Planning the supply chain network for new products: A case study. Engineering Management Journal 18(2): 35–43.

    Google Scholar 

  • Chang, Y., and H. Makatsoris. 2001. Supply chain modeling using simulation. International Journal of Simulation 2(1): 24–30.

    Google Scholar 

  • Chauhan, S., R. Nagi, and J.-M. Proth. 2004. Strategic capacity planning in supply chain design for a new market opportunity. International Journal of Production Research 42(11): 2197–2206.

    Article  Google Scholar 

  • Christensen, W., R. Germain, and L. Birou. 2007. Variance vs. average: Supply chain lead-time as a predictor for financial performance. Supply Chain Management: An International Journal 12(5): 349–357.

    Google Scholar 

  • Christopher, M., and L. Ryals. 1999. Supply chain strategy: Its impact on shareholder value. International Journal of Logistics Management 10(1): 1–10.

    Article  Google Scholar 

  • Chwif, L., M. Pereira Barretto, and E. Saliby. 2002. Supply chain analysis: Spreadsheet or simulation? In Proceedings of the winter simulation conference, ed. E. Yücesan, C.-H. Chen, J. Showdon, and J. Charnes, 59–66. New York: ACM; Piscataway: IEEE.

    Google Scholar 

  • Cooper, M.C., D.M. Lambert, and J.D. Pagh. 1997. Supply chain management – More than a new name for logistics. International Journal of Logistics Management 8(1): 1–14.

    Article  Google Scholar 

  • Corsten, D., and T. Gruen. 2003. Desperately seeking shelf availability: An examination of the extent, the causes, and the efforts to address retail out-of-stocks. International Journal of Retail and Distribution Management 31(12): 605–617.

    Article  Google Scholar 

  • Danne, C., and P. Häusler. 2010. Assessing the effects of assortment complexity in consumer goods supply chains. Lecture Notes in Business Information Processing 46: 118–129.

    Article  Google Scholar 

  • Dekker, R., E. van Asperen, G. Ochtman, and W. Kusters. 2009. Floating stocks in FMCG supply chains: Using intermodal transport to facilitate advance deployment. International Journal of Physical Distribution and Logistics Management 39(8): 632–648.

    Article  Google Scholar 

  • Goetschalckx, M., and B. Fleischmann. 2008. Strategic network design. In Supply chain management and advanced planning, 4th ed, ed. H. Stadtler and C. Kilger, 117–132. Berlin: Springer.

    Chapter  Google Scholar 

  • Graves, S., and S. Willems. 2005. Optimizing the supply chain configuration for new products. Management Science 51(8): 1165–1180.

    Article  Google Scholar 

  • Gunasekaran, A., and B. Kobu. 2007. Performance measures and metrics in logistics and supply chain management: A review of recent literature (1995–2004) for research and applications. International Journal of Production Research 45(12): 2819–2840.

    Article  Google Scholar 

  • Gunasekaran, A., C. Patel, and R. McGaughey. 2004. A framework for supply chain performance measurement. International Journal of Production Economics 87: 333–347.

    Article  Google Scholar 

  • Hahn, G.J., and H. Kuhn. 2010. Optimising a value-based performance indicator in mid-term sales and operations planning. Journal of the Operational Research Society 62: 515–525.

    Article  Google Scholar 

  • Hahn, G.J., and H. Kuhn. 2011. Value-based performance and risk management in supply chains: A robust optimization approach. International Journal of Production Economics. doi:10.1016/j.ijpe.2011.04.002.

    Google Scholar 

  • Halldorsson, A., and J. Arlbjørn. 2005. Research methodologies in supply chain management – What do we know? In Research methodologies in supply chain management, ed. H. Kotzab, S. Seuring, M. Müller, and G. Reiner. Heidelberg: Physica.

    Google Scholar 

  • Hendricks, K., and V. Singhal. 2003. The effect of supply chain glitches on shareholder wealth. Journal of Operations Management 21: 501–522.

    Article  Google Scholar 

  • Hendricks, K., and V. Singhal. 2005b. An empirical analysis of the effect of supply chain disruptions on long-run stock price performance and equity risk of the firm. Production and Operations Management 14(1): 35–52.

    Article  Google Scholar 

  • Higuchi, T., and M. Troutt. 2004. Dynamic simulation of the supply chain for a short life cycle product-lessons from the Tamagotchi case. Computers and Operations Research 31(7): 1097–1114.

    Article  Google Scholar 

  • Hofmann, E., and A. Locker. 2009. Value-based performance measurement in supply chain: A case study from the packaging industry. Production Planning and Control 20(1): 68–81.

    Article  Google Scholar 

  • Honkomp, S., S. Lombardo, O. Rosen, and J. Pekny. 2000. The curse of reality – Why process scheduling optimization problems are difficult in practice. Computers and Chemical Engineering 24(2–7): 323–328.

    Article  Google Scholar 

  • Ivanov, D., B. Sokolov, and J. Kaeschel. 2010. Integrated adaptive design and planning of supply networks. Lecture Notes in Business Information Processing 46: 152–163.

    Article  Google Scholar 

  • Kelton, W., R. Sadowski, and D. Sadowski. 2002. Simulation with arena, 2nd ed. New York: McGraw-Hill.

    Google Scholar 

  • Kleijnen, P. 2005. Supply chain simulation tools and techniques: A survey. International Journal of Simulation and Process Modelling 1(1/2): 82–89.

    Google Scholar 

  • Lambert, D., and R. Burduroglu. 2000. Measuring and selling the value of logistics. International Journal of Logistics Management 11(1): 1–17.

    Article  Google Scholar 

  • Lambert, D., and T. Pohlen. 2001. Supply chain metrics. International Journal of Logistics Management 12(1): 1–19.

    Article  Google Scholar 

  • Losbichler, H., and M. Rothböck. 2006. Creating shareholder value through value-driven supply chain management. In Managing risks in supply chains – How to build reliable collaboration in logistics, ed. W. Kersten and T. Blecker, 281–296. Berlin: Erich Schmidt Verlag.

    Google Scholar 

  • Manuj, I., J. Mentzer, and M. Bowers. 2009. Improving the rigor of discrete-event simulation in logistics and supply chain management. International Journal of Physical Distribution and Logistics Management 39(3): 172–201.

    Article  Google Scholar 

  • Marquai, A., R. Alcantara, and M. Christopher. 2010. Using the systematic literature review procedure to identify the root causes of out-of-stock in retail supply chains. In Proceedings of the 17th international annual EurOMA conference, ed. R. Sousa. Porto: Catholic University of Portugal.

    Google Scholar 

  • Melnyk, S., R. Lummus, R. Vokurka, L. Burns, and J. Sandor. 2009. Mapping the future of supply chain management: A delphi study. International Journal of Production Research 47(16): 4629–4653.

    Article  Google Scholar 

  • Mena, C., A. Humphries, and R. Wilding. 2009. A comparison of inter- and intra-organizational relationships: Two case studies from UK food and drink industry. International Journal of Physical Distribution and Logistics Management 39(9): 762–784.

    Article  Google Scholar 

  • Mentzer, J., W. DeWitt, J. Keebler, S. Min, N. Nix, C. Smith, and Z. Zacharia. 2001. Defining supply chain management. Journal of Business Logistics 22(2): 1–25.

    Article  Google Scholar 

  • Meyr, H., and H. Stadtler. 2008. Types of supply chains. In Supply chain management and advanced planning, 4th ed, ed. H. Stadtler and C. Kilger, 65–80. Berlin: Springer.

    Chapter  Google Scholar 

  • Pero, M., N. Abdelkafi, A. Sianesi, and T. Blecker. 2010. A framework for the alignment of new product development and supply chains. Supply Chain Management: An International Journal 15(2): 115–128.

    Article  Google Scholar 

  • Poston, R., and S. Grabinski. 2001. Financial impacts of enterprise resource planning implementations. International Journal of Accounting Information Systems 2(23): 271–294.

    Article  Google Scholar 

  • Pourakbar, M., A. Sleptchenko, and R. Dekker. 2009. The floating stock policy in fast moving consumer goods supply chains, Transportation Research Part E 45(1): 39–49.

    Article  Google Scholar 

  • Randall, W., and M. Farris II. 2009a. Supply chain financing: Using cash-to-cash variables to strengthen the supply chain. International Journal of Physical Distribution and Logistics Management 39(8): 669–689.

    Google Scholar 

  • Reiner, G. 2005. Supply chain management research methodologies using quantitative models based on empirical data. In Research methodologies in supply chain management, ed. H. Kotzab, S. Seuring, M. Müller, and G. Reiner, 431–444. Heidelberg: Physica.

    Chapter  Google Scholar 

  • Reiner, G., and P. Hofmann. 2006. Efficiency analysis of supply chain processes. International Journal of Production Research 44(23): 5065–5087.

    Article  Google Scholar 

  • Reiner, G., and M. Trcka. 2004. Customized supply chain design: Problems and alternatives for a production company in the food industry. A simulation based analysis. International Journal of Production Economics 89: 217–229.

    Google Scholar 

  • Rockwell Automation, ed. 2007. ArenaTM user’s guide, http://literature.rockwellautomation.com/idc/groups/literature/documents/um/arena-um001_-en-p.pdf. Accessed 7 Sep 2010.

  • Saad, S., and N. Gindy. 1998. Handling internal and external disturbances in responsive manufacturing environment. Production Planning and Control 9(8): 760–770.

    Article  Google Scholar 

  • Sandholm, T., D. Levine, M. Concordia, P. Martyn, R. Hughes, J. Jacobs, and D. Begg. 2006. Changing the game in strategic sourcing at Procter and Gamble: Expressive competition enabled by optimization. Interfaces 36(1): 55–68.

    Article  Google Scholar 

  • Schilling, R., H. Kuhn, and M. Brandenburg. 2010. Simulation-based evaluation of tactical supply chain design scenarios for new product introduction. In Proceedings of the 17th international annual EurOMA conference, ed. R. Sousa. Porto: Catholic University of Portugal.

    Google Scholar 

  • Seuring, S. 2001. Supply chain costing – A conceptual framework. In Cost management in supply chains, ed. S. Seuring and M. Goldbach, 15–30. Heidelberg/New York: Physica.

    Google Scholar 

  • Seuring, S., and M. Goldbach. 2001. Cost management in supply chains. Heidelberg/New York: Physica.

    Google Scholar 

  • Shapiro, J. 2007. Modeling the supply chain, 2nd ed. Duxbury. Brooks/Cole: Thompson.

    Google Scholar 

  • Siemieniuch, C., F. Waddell, and M. Sinclair. 1999. The role of ‘partnership’ in supply chain management for fast-moving consumer goods: A case study. International Journal of Logistics Research and Applications 2(1): 87–101.

    Google Scholar 

  • Silver, E., D. Pyke, and R. Peterson. 1998. Inventory management and production planning and scheduling, 3rd ed. New York: Wiley.

    Google Scholar 

  • Tahmassebi, T. 1998. An approach to management of multilevel distribution systems for consumer goods and chemicals industry under information uncertainty. Computers and Chemical Engineering 22: S263–S270.

    Article  Google Scholar 

  • Terzi, S., and S. Cavalieri. 2004. Simulation in the supply chain context: A survey. Computers in Industry 53: 3–16.

    Article  Google Scholar 

  • van Hoek, R.I., and P. Chapman. 2006. From tinkering around the edge to enhancing revenue growth: Supply chain-new product development. Supply Chain management: An International Journal 11(5): 385–389.

    Article  Google Scholar 

  • Villegas, F., and N. Smith. 2006. Supply chain dynamics: Analysis of inventory vs. order oscillations trade-off. International Journal of Production Research 44(6): 1037–1054.

    Google Scholar 

  • Wang, J., and Y.-F. Shu. 2007. A possibilistic decision model for new product supply chain design. European Journal of Operational Research 177(2): 1044–1061.

    Article  Google Scholar 

  • Wilding, R. 1998. The supply chain complexity triangle: Uncertainty generation in the supply chain. International Journal of Physical Distribution and Logistics Management 28(8): 599–616.

    Article  Google Scholar 

  • Wong, C., J. Arlbjørn, and J. Johansen. 2005. Supply chain management practices in toy supply chains. Supply Chain management: An International Journal 10(5): 367–378.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Chair of Supply Chain Management, University of Kassel, Kassel, Germany

    Dipl. Math. Marcus Brandenburg

Authors
  1. Dipl. Math. Marcus Brandenburg
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Brandenburg, M. (2013). Dynamics and Uncertainties in Tactical Supply Chain Design for New Product Introduction. In: Quantitative Models for Value-Based Supply Chain Management. Lecture Notes in Economics and Mathematical Systems, vol 660. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31304-2_8

Download citation

Publish with us

Policies and ethics

Search

Navigation