LOGIN TO YOUR ACCOUNT

Username
Password
Remember Me
Or use your Academic/Social account:

CREATE AN ACCOUNT

Or use your Academic/Social account:

Congratulations!

You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.

Important!

Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message

CREATE AN ACCOUNT

Name:
Username:
Password:
Verify Password:
E-mail:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Hedenstierna, Carl Philip
Languages: English
Types: Doctoral thesis
Subjects: HE
This thesis investigates production-inventory systems where replenishments are\ud received every period (for example every day or shift), but where production\ud plans are determined less frequently (weekly, fortnightly, or monthly). Such\ud systems are said to use staggered deliveries. This practice is common in industry,\ud but the theoretical knowledge is limited to a small set of inventory models, none\ud of which include capacity costs. This thesis uses time series analysis to expand\ud our understanding of staggered deliveries from the perspectives of inventory\ud and production-inventory control.\ud The contribution to inventory theory consists in the development of an\ud optimal policy for autocorrelated demand and linear inventory costs, including\ud exact expressions for costs, availability, and fill rate. In addition the thesis\ud identifies a procedure for finding the optimal order cycle length, when a onceper-\ud cycle audit cost is present. Notably, constant safety stocks are suboptimal,\ud and cause both availability and fill rate to fluctuate over the cycle. Instead,\ud the safety stocks should vary over time, causing the availability, but not the fill\ud rate, to be constant.\ud The contribution to production-inventory theory comes from two perspectives:\ud First, an optimal policy is derived for quadratic inventory and capacity\ud costs; second, four pragmatic policies are tested, each affording a different\ud approach to production smoothing and the allocation of overtime work (once\ud per cycle, or an equal amount of overtime every period). Assuming independent\ud and identically distributed demand, these models reveal that all overtime or\ud idling should be allocated to the first period of each cycle. Furthermore, it\ud is shown that the order cycle length provides a crude production smoothing\ud mechanism. Should a company with long reorder cycles decide to plan more\ud often, the capacity costs may increase. Therefore, supply chains should implement\ud a replenishment policy capable of production smoothing before the order\ud cycle length is reduced.\ud ii
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • 7.1 Review of research questions . . . . . . . . . . . . . . . . . . . . 102 7.2 Review of results . . . . . . . . . . . . . . . . . . . . . . . . . . 104 7.3 Managerial implications . . . . . . . . . . . . . . . . . . . . . . 106 7.4 Limitations and research opportunities . . . . . . . . . . . . . . 106 7.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
    • A Piecewise linear cost models 122 A.1 Inventory costs . . . . . . . . . . . . . . . . . . . . . . . . . . . 122 A.2 Capacity costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123
    • B Proofs 125 B.1 Proof of Theorem 4.2 . . . . . . . . . . . . . . . . . . . . . . . . 125 B.2 Proof of Theorem 4.4 . . . . . . . . . . . . . . . . . . . . . . . . 126 B.3 Proof of Theorem 5.6 . . . . . . . . . . . . . . . . . . . . . . . . 127 B.4 Proof of Lemma 5.7 . . . . . . . . . . . . . . . . . . . . . . . . . 129 Forrester, J. W., 1958. Industrial dynamics: a major breakthrough for decision makers. Harvard Business Review, 36(4):37-66.
    • Forrester, J. W., 1994. System dynamics, systems thinking, and soft OR. System Dynamics Review, 10(2-3):245-256.
    • Forrester, J. W. and Senge, P. M., 1978. Tests for building confidence in system dynamics models. Technical report, System Dynamics Group, Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA.
    • Gaalman, G., 2006. Bullwhip reduction for ARMA demand: The proportional order-up-to policy versus the full-state-feedback policy. Automatica, 42(8):1283-1290.
    • Gallo, G., 2004. Operations research and ethics: Responsibility, sharing and cooperation. European Journal of Operational Research, 153(2):468-476.
    • Gardner, E. S., 1990. Evaluating forecast performance in an inventory control system. Management Science, 36(4):490-499.
    • Goldratt, E., 2004. The Goal: A Process of Ongoing Improvement. North River Press, Great Barrington, MA.
    • Guijarro, E., Cardós, M., and Babiloni, E., 2012. On the exact calculation of the fill rate in a periodic review inventory policy under discrete demand patterns. European Journal of Operational Research, 218(2):442-447.
    • Hadley, G. F. and Whitin, T. M., 1963. Analysis of Inventory Systems: Prentice Hall International Series in Management and Quantitative Methods Series. Prentice-Hall, Englewood Cliffs, NJ.
    • Harré, R., 1976. The constructive role of models. In The use of models in the social sciences, pp. 16-43. Tavistock, London.
    • Harris, F. W., 1990. How many parts to make at once. Operations Research, 38(6):947-950.
    • Hedenstierna, C. P. T. and Disney, S. M., 2012. Impact of scheduling frequency and shared capacity on production and inventory costs. In Pre-prints of the 18th International Working Seminar on Production Economics, vol. 2, pp. 277-288. Innsbruck, Austria.
    • Hedenstierna, C. P. T. and Disney, S. M., 2014. The inventory ripple effect in periodic review systems with auto-correlated demand. In R. W. Grubbström and H. H. Hinterhuber, eds., Pre-prints of the 18th International Working Seminar on Production Ecnonomics, vol. 3, pp. 207-218. Innsbruck, Austria.
    • Hedenstierna, P., 2009. Modelling the Benefits of Supply Chain Segmentation: An Empirical Study in a Global Fast Moving Consumer Goods (FMCG) Company. M.Sc. thesis, Cranfield University, Cranfield.
    • Heyman, D. P. and Sobel, M. J., 1984. Stochastic Models in Operations Research, Vol. II: Stochastic Optimization. McGraw-Hill, New York.
    • Holmström, J., 1998. Business process innovation in the supply chain - a case study of implementing vendor managed inventory. European Journal of Purchasing & Supply Management, 4(2-3):127-131.
    • Holt, C. C., Modigliani, F., Muth, J. F., and Simon, H. A., 1960. Planning Production, Inventories, and Work Force. Prentice-Hall, Englewood Cliffs, NJ.
    • Hosoda, T. and Disney, S. M., 2009. Impact of market demand mis-specification on a two-level supply chain. International Journal of Production Economics, 121(2):739-751.
    • Hosoda, T. and Disney, S. M., 2012. On the replenishment policy when the market demand information is lagged. International Journal of Production Economics, 135(1):458-467.
    • Howard, R. A., 1963. System Analysis of Linear Models. In H. E. Scarf, D. M. Gilford, and M. W. Shelly, eds., Multistage Inventory Models and Techniques, pp. 143-184. Stanford University Press, Stanford.
    • Isaksson, O. H. D. and Seifert, R. W., 2016. Quantifying the bullwhip effect using two-echelon data: A cross-industry empirical investigation. International Journal of Production Economics, 171, Part 3:311-320.
    • John, S., Naim, M., and Towill, D. R., 1994. Dynamic analysis of a WIP compensated decision support system. International Journal of Manufacturing System Design, 1(4):283-297.
    • Johnson, M. E., Lee, H. L., Davis, T., and Hall, R., 1995. Expressions for item fill rates in periodic inventory systems. Naval Research Logistics, 42(1):57-80.
    • Jonsson, P. and Mattsson, S.-A., 2005. Logistik: Läran om effektiva materialflöden. Studentlitteratur, Lund. In Swedish.
    • Jonsson, P. and Mattsson, S.-A., 2013. Lagerstyrning i svensk industri: 2013 års användning, användningssätt och trender. Technical report, Chalmers University of Technology, Gothenburg. In Swedish.
    • Kaku, B. K. and Krajewski, L. J., 1995. Period Batch Control in group technology. International Journal of Production Research, 33(1):79-99.
    • Kelle, P. and Silver, E. A., 1990. Decreasing expected shortages through order splitting. Engineering Costs and Production Economics, 19(1-3):351-357.
    • Kirk, D. E., 1997. Optimal Control Theory: An introduction. Dover Publications, New York.
    • Lansburgh, R. H., 1928. Industrial Management. John Wiley & Sons, New York.
    • Lee, H. and Whang, S., 1999. Decentralized multi-echelon supply chains: Incentives and information. Management Science, 45(5):633-640.
    • Lee, H. L., Billington, C., and Carter, B., 1993. Hewlett-Packard gains control of inventory and service through design for localization. Interfaces, 23(4):1-11.
    • Lee, H. L., Padmanabhan, V., and Whang, S., 1997. Information distortion in a supply chain: The bullwhip effect. Management Science, 43(4):546-558.
    • Lee, H. L., So, K. C., and Tang, C. S., 2000. The value of information sharing in a two-level supply chain. Management Science, 46(5):627.
    • Lee, Y. S., 2014. A semi-parametric approach for estimating critical fractiles under autocorrelated demand. European Journal of Operational Research, 234(1):163-173.
    • Li, Q., Disney, S. M., and Gaalman, G., 2014. Avoiding the bullwhip effect using damped trend forecasting and the order-up-to replenishment policy. International Journal of Production Economics, 149:3-16.
    • Lian, Z., Deshmukh, A., and Wang, J., 2006. The optimal frozen period in a dynamic production model. International Journal of Production Economics, 103(2):648-655.
    • Luenberger, D. G., 1979. Introduction to Dynamic Systems: Theory, Models, and Applications. John Wiley & Sons, New York.
    • Maister, D. H., 1976. Centralisation of inventories and the “Square Root Law”. International Journal of Physical Distribution, 6(3):124-134.
    • Metters, R., 1997. Quantifying the bullwhip effect in supply chains. Journal of Operations Management, 15(2):89-100.
    • Minas, J. S., 1956. Formalism, Realism and Management Science. Management Science, 3(1):9-14.
    • Mingers, J., 2011. Soft OR comes of age-but not everywhere! 39(6):729-741.
    • Modigliani, F. and Hohn, F. E., 1955. Production planning over time and the nature of the expectation and planning horizon. Econometrica, Journal of the Econometric Society, 23(1):46-66.
    • Nathan, J. and Venkataraman, R., 1998. Determination of master production schedule replanning frequency for various forecast window intervals. International Journal of Operations & Production Management, 18(8):767-777.
    • Nise, N. S., 2011. Control Systems Engineering. John Wiley & Sons, Hoboken, NJ.
    • Ohno, T., 1988. Toyota Production System: Beyond Large-Scale Production. Productivity Press, New York.
    • Pagh, J. D. and Cooper, M. C., 1998. Supply chain postponement and speculation strategies: How to choose the right strategy. Journal of Business Logistics, 19(2):13-33.
    • Petropoulos, F., Makridakis, S., Assimakopoulos, V., and Nikolopoulos, K., 2014. 'Horses for Courses' in demand forecasting. European Journal of Operational Research, 237(1):152-163.
    • Pidd, M., 2003. Tools for Thinking. Wiley, Chichester, UK.
    • Potter, A. and Disney, S. M., 2010. Removing bullwhip from the Tesco supply chain. In Production and Operations Management Society Annual Conference, May 7th - 10th. Vancouver, Canada. Paper No. 015-0397, 19 pages.
    • Prak, D., Teunter, R., and Riezebos, J., 2015. Periodic review and continuous ordering. European Journal of Operational Research, 242(3):820-827.
    • Rao, P. P., 1990. A dynamic programming approach to determine optimal manpower recruitment policies. The Journal of the Operational Research Society, 41(10):983-988.
    • Rosenshine, M. and Obee, D., 1976. Analysis of a standing order inventory system with Emergency orders. Operations Research, 24(6):1143-1155.
    • Rostami-Tabar, B., Babai, M. Z., Syntetos, A., and Ducq, Y., 2013. Demand forecasting by temporal aggregation. Naval Research Logistics (NRL), 60(6):479-498.
    • Roundy, R., 1986. A 98%-effective lot-sizing rule for a multi-product, multistage production / inventory system. Mathematics of Operations Research, 11(4):699-727.
    • Rummler, G. A. and Brache, A. P., 1995. Improving Performance: How to Manage the White Space in the Organization Chart. Jossey-Bass, San Francisco.
    • Scarf, H. E., 1959. The optimality of (S,s) policies in the dynamic inventory problem. In K. J. Arrow, S. Karlin, and P. Suppes, eds., Mathematical Methods in the Social Sciences, 1959. Proceedings of the First Stanford Symposium, pp. 196-202. Stanford University Press, Stanford.
    • Schmitt, A. J. and Singh, M., 2012. A quantitative analysis of disruption risk in a multi-echelon supply chain. International Journal of Production Economics, 139(1):22-32.
    • Sethi, S. P., Yan, H., and Zhang, H., 2003. Inventory models with fixed costs, forecast updates, and two delivery modes. Operations Research, 51(2):321- 328.
    • Shingo, S., 1989. A Study of the Toyota Production System: From an Industrial Engineering Viewpoint. Productivity Press, New York.
    • Silver, E. A. and Bischak, D. P., 2011. The exact fill rate in a periodic review base stock system under normally distributed demand. Omega, 39(3):346-349.
    • Silver, E. A., Pyke, D. F., and Peterson, R., 1998. Inventory Management and Production Planning and Scheduling. Wiley, New York.
    • Simchi-Levi, D., 2002. Designing and Managing the Supply Chain: Concepts, Strategies, and Cases. McGraw-Hill, New York.
    • Simon, H. A., 1952. On the application of servomechanism theory in the study of production control. Econometrica: Journal of the Econometric Society, 20(2):247-268.
    • Simon, H. A., 1956. Dynamic programming under uncertainty with a quadratic criterion function. Econometrica, 24(1):74-81.
    • Simon, H. A. and Holt, C. C., 1954. The control of inventories and production rates-a survey. Journal of the Operations Research Society of America, 2(3):289-301.
    • Slack, N., 2015. Runners, Repeaters, and Strangers. In Wiley Encyclopedia of Management. John Wiley & Sons, Hoboken, NJ.
    • Sloan, A. P., 1963. My Years with General Motors. Doubleday, New York.
    • Sobel, M. J., 1970. Making short-run changes in production when the employment level is fixed. Operations Research, 18(1):35-51.
    • Sobel, M. J., 2004. Fill rates of single-stage and multistage supply systems. Manufacturing & Service Operations Management, 6(1):41-52.
    • Sterman, J. D., 2000. Business Dynamics: Systems Thinking and Modeling for a Complex World. McGraw-Hill Education, New York.
    • Strijbosch, L. W. G., Syntetos, A. A., Boylan, J. E., and Janssen, E., 2011. On the interaction between forecasting and stock control: The case of non-stationary demand. International Journal of Production Economics, 133(1):470-480.
    • Tang, O. and Grubbström, R. W., 2002. Planning and replanning the master production schedule under demand uncertainty. International Journal of Production Economics, 78(3):323-334.
    • Teunter, R. H., 2009. Note on the fill rate of single-stage general periodic review inventory systems. Operations Research Letters, 37(1):67-68.
    • Teunter, R. H., Babai, M. Z., and Syntetos, A. A., 2010. ABC classification: service levels and inventory costs. Production and Operations Management, 19(3):343-352.
    • Towill, D. R., 1982. Dynamic analysis of an inventory and order based production control system. The International Journal of Production Research, 20(6):671-687.
    • Tsypkin, I. Z., 1964. Sampling Systems Theory and its Application, vol. 1. Pergamon, Oxford.
    • Tustin, A., 1953. The Mechanism of Economic Systems: An Approach to the Problem of Economic Stabilization from the Point of View of Control-System Engineering. Heinemann, London.
    • Vassian, H. J., 1955. Application of discrete variable servo theory to inventory control. Journal of the Operations Research Society of America, 3(3):272-282.
    • Veinott, A. F., 1965. The Optimal Inventory Policy for Batch Ordering. Operations Research, 13(3):424-432.
    • Veinott, A. F., 1966. On the Optimality of (s, S) Inventory Policies: New Conditions and a New Proof. SIAM Journal on Applied Mathematics, 14(5):1067- 1083.
    • Wagner, H. M. and Whitin, T. M., 1958. Dynamic version of the economic lot size model. Management Science, 5(1):89-96.
    • Wang, X. and Disney, S. M., 2016. The bullwhip effect: Progress, trends and directions. European Journal of Operational Research, 250(3):691-701.
    • Zhang, J. and Zhang, J., 2007. Fill rate of single-stage general periodic review inventory systems. Operations Research Letters, 35(4):503-509.
    • Zhao, X. and Lee, T. S., 1993. Freezing the master production schedule for material requirements planning systems under demand uncertainty. Journal of Operations Management, 11(2):185-205.
    • Zhou, L., Disney, S., and Towill, D. R., 2010. A pragmatic approach to the design of bullwhip controllers. International Journal of Production Economics, 128(2):556-568.
  • No related research data.
  • No similar publications.

Share - Bookmark

Cite this article