Master-Seminar Operations Management

Transcription

1 Master-Seminar Operations Management Chair of Logistics and Quantitative Methods in Business Administration January Syllabus 1.1 Objectives Supply chain management encompasses the planning and management of all activities involved in sourcing and procurement, conversion, and all logistics management activities. Importantly, it also includes coordination and collaboration with channel partners, which can be suppliers, intermediaries, third-party service providers, and customers. In essence, supply chain management integrates supply and demand management within and across companies. Supply chain management is an integrating function with primary responsibility for linking major business functions and business processes within and across companies into a cohesive and high-performing business model. It includes all of the logistics management activities noted above, as well as manufacturing operations, and it drives coordination of processes and activities with and across marketing, sales, product design, finance and information technology. (Council of Supply Chain Management Professionals (CSCMP) 2011) Today, Supply Chain management (SCM) is strongly supported by stateof-the-art ERP and Advanced Planning Systems; in addition with smarter machines and ubiquitously available sensor the available amounts of data call for exploitation for SCM purposes. The aim of this seminar is to familiarize participants with various opportunities for data driven SCM. 1.2 Contents This seminar offers qualitative topics which focus on literature review as well as quantitative ones which include the work with real-world data. The qualitative topics require the participants to read, compare and summarize the relevant literature (non-exhaustive proposals for literature are provided in each topic description). Additionally, the participants are required to synthesize their findings and critically discuss underlying assumptions and limitations. Our quantitative topics focus on models, methods and concepts to implement data driven SCM. 1

2 Through participation in a common lecture with appropriate preparation and wrap-up, the participants shall become acquainted with an holistic overview of supply chain collaboration-related problems and solution methodologies. All dates will be announced ahead of time on our homepage 1.3 Grading To pass the seminar, a seminar paper must be completed. To this end, participating in the introductory course is necessary, where the topics will be assigned. In addition, it is mandatory to participate in the chairs scientific writing seminar (separate application via Maya Michels (maya.michels@uni-wuerzburg.de). The participants must also participate in an intermediate workshop. At a final workshop they present their work. All dates will be published ahead of time on the chair s webpage. Active participation at all meetings is required. The final grade is based on the grade for the seminar paper (weight: 2/3) and the grade for the presentation and participation in discussion of all topics (weight: 1/3). 1.4 Seminar paper The seminar paper on a qualitative topic shall comprise approx pages. For quantitative topics it can be pages. It can be written in German or English. Details about how to prepare the paper will be provided at the introduction. Rough structure of seminar paper (that should be adapted to specific topics): Introduction to the problem: concise presentation of the planning problem, description of goals and available alternatives Literature review: how can the problem be classified in the relevant (scientific) literature? What comparable and similar problems and possible solutions are there? How does the particular problem differ from other problems? Formally present the planning problem: goals, alternatives, modeling Solution: how was the issue addressed? evaluated? How were various alternatives Results: which alternative was selected for what reason? To what extent were different objectives considered and weighted? Final evaluation: critical analysis of problem, criticism, suggestions for improvement, transferability to other problems The deadline for this paper will be published ahead of time on the chair s webpage. The paper must be handed in electronically (pdf) only to Maya Michels (maya.michels.@uni-wuerzburg.de). 2

3 1.5 Presentation The presentation should be minutes in length and serve to impart the relevant problems and possible solutions to other participants. In evaluating the presentation, focus will be placed on how precisely and comprehensibly the problem is explained and how convincingly the approach (or possible criticism) is presented. Following the presentation, minutes of time will be reserved for discussion. 1.6 Contacts Julian Kurz Jan Meller Fabian Taigel 2 Topics The topics can be worked on individually or in groups of two. 2.1 Benefit assessment of improved forecasting The rise of sensor-equipped machinery opens opportunities for condition based forecasting of maintenance demand. Integrating information about the condition of the machinery, which is the main driver for demand, can significantly increase the forecast accuracy. However, the introduction of condition based forecasting approaches comes with significant costs for maintenance service providers and their customers. In order to evaluate the profitability of such an investment, we need to estimate the potential benefit of improved forecast accuracy in each specific setting. Your task will be to evaluate the potential improvements on spare parts inventory costs in various scenarios. A scenario is described by the typical demand pattern for a specific spare part, the attached inventory holding costs and the penalty costs if a stock out occurs. Your task consists of three parts: Define realistic scenarios based on literature on the classification of spare parts (demands). Estimate the potential benefits using our sophisticated simulation tool for benefit assessment that you find here: Discuss and extend the inventory policies that are already implemented in the tool 3

4 Syntetos,A. A.; Boylan, J. E.; Croston, J. D. (2005). On the Categorization of Demand Patterns. The Journal of the Operational Research Society, 56(5), Molenaers, An; Baets,Herman; Pintelon, Liliane; Waeyenbergh, Geert (2012) Criticality classification of spare parts: A case study, International Journal of Production Economics, Volume 140, Issue 2, December 2012, Pages Babaï, M. Z.; Syntetos, A. A.; Dallery, Y.; Nikolopoulos, K. (2009): Dynamic re-order point inventory control with lead-time uncertainty: analysis and empirical investigation. In International Journal of Production Research 47 (9), pp DOI: / Lin, X.; Basten, R. J. I.; Kranenburg, A. A.; Houtum, V. G. (2012) Condition based spare parts supply. BETA Research Institute Plus additional references. 2.2 Forecasting demand of spare parts inventories Spare part inventories typically contain numerous different parts which exhibit a broad variety of demand patterns. From extremely slow moving parts, with 0 demand for many periods to fast moving parts with large volumes. The patterns might as well vary in terms of demand variability. Classifying such patterns and determining the best forecast method is a crucial task. For this topic, you will work with a large scale real-world dataset. Therefore, you should expect to work with some kind of statistical software, such as R. With R you can easily apply the relevant time-series based forecasting methods, such as exponential smoothing, moving averages and their extensions. The interesting part will be to tackle the tasks of classifying demand patterns and choosing a forecasting method in an integrated way. Given the large set of over 3000 different parts this needs to be automated. Review literature on demand pattern classification Review literature on time series-based forecasting Find typical patterns and determine reasonable forecasting method Find a classification method/system/algorithm that proposes a forecasting method for each demand pattern Establish significant measures for the performance of the classification method 4

5 Data: Ralph Snyder, Forecasting sales of slow and fast moving inventories, European Journal of Operational Research, Volume 140, Issue 3, 2002, Pages , Andrea Bacchetti and Nicola Saccani Spare parts classification and demand forecasting for stock control, Omega, Volume 40, Issue 6, 2012, Pages Plus additional references. large scale data set with part consumptions of over 3000 different spare parts 2.3 Data-driven spare parts management An important task within the maintenance management domain is the concise planning and control of spare parts inventories. However, due to highly stochastic demand patterns, accurate forecasting of spare parts demands is a very difficult task. In recent years, the tremendous growth of sensor and production data provides ways to more accurately predict such demands, resulting in new opportunities to more efficiently manage spare parts inventories. Within this topic, several tasks can be tackled: The installation of sensors within production equipment but also storage and analysis of the accumulated data is expensive. However, the benefits of such a predictive maintenance solution are hard to quantify in advance. Based on a simulation tool that was developed at our Chair, examine the impact of improved forecasting accuracy on total maintenance costs under different scenarios. Are there specific characteristics under which predictive maintenance is particularly beneficial? Structure your findings in a reasonable classification scheme. Besides the uncertainty related to future demand information, an important factor of inventory performance is the replenishment of required parts. Despite the widespread assumptions of deterministic lead times, especially for slow-running spare parts replenishment times might vary considerably with potentially significant impact on the total service quality and equipment uptime. Your task is to extend the aforementioned simulation tool to reflect stochastic lead times and present the effects for an exemplary case. Campuzano, F.; Mula, J. (2011): Supply chain simulation: A system dynamics approach for improving performance. London: Springer. 5

6 Babaï, M. Z.; Syntetos, A. A.; Dallery, Y.; Nikolopoulos, K. (2009): Dynamic re-order point inventory control with lead-time uncertainty: analysis and empirical investigation. In International Journal of Production Research 47 (9), pp DOI: / And the references therein. Software: simulation tool implemented in R: additional useful documentation 2.4 Performance-based Logistics Performance-based contracting is a concept that has met increasing attention over the last years in capital-intensive industries such as aerospace and defense. Known as power by the hour in the private sector and as performance-based logistics (PBL) in defense contracting, it aims to replace traditionally used fixed-price and cost-plus contracts to improve product availability and reduce the cost of ownership by tying a supplier s compensation to the output value of the product generated by the customer (buyer). Your task is to examine the implications of such a performance-based logistics concept for the customer and the service provider. Which preconditions are necessary to make such an offer successful for both of them? Present benefits and risks of this concept through a short literature review. Randall, Wesley S.; Pohlen, Terrance L.; Hanna, Joe B. (2010): Evolving a theory of performance-based logistics using insights from service dominant logic. In Journal of Business Logistics 31 (2), pp DOI: /j tb00142.x. Randall, Wesley S.; Nowicki, David R.; Hawkins, Timothy G. (2011): Explaining the effectiveness of performance-based logistics. A quantitative examination. In Int Jrnl Logistics Management 22 (3), pp DOI: / Kim, Sang-Hyun; Cohen, Morris A.; Netessine, Serguei (2007): Performance Contracting in After-Sales Service Supply Chains. In Management Science 53 (12), pp DOI: /mnsc And the references therein. 6

7 2.5 Roadmap to Predictive Maintenance We are currently collaborating with a cleaning equipment manufacturer in order to help them defining their roadmap to predictive maintenance. Their cleaning equipment is able to send operational data several times per day that is stored in a cloud. We want to investigate how this data (and other data that is currently not sent but could be sent) could potentially be used for predictive maintenance, i.e., to forecast when maintenance will be needed and what the maintenance requirements are. Based on literature and additional information about the cleaning equiment manufacturer, describe the necessary steps to achieve predictive maintenance. Rudin, Cynthia, et al. Machine learning for the New York City power grid. Pattern Analysis and Machine Intelligence, IEEE Transactions on 34.2 (2012): Tulabandhula, Theja, and Cynthia Rudin. Machine learning with operational costs. The Journal of Machine Learning Research 14.1 (2013): Tulabandhula, Theja, Cynthia Rudin, and Patrick Jaillet. Machine learning and the traveling repairman. Unpublished manuscript available on ArXiv at org/abs/ (2011). Muller, Alexandre, Adolfo Crespo Marquez, and Benoit Iung. On the concept of e-maintenance: Review and current research. Reliability Engineering & System Safety 93.8 (2008): Plus additional references. 2.6 Queueing with Future Information (Note: This is a quantitative/analytical topic.) Assume a maintenance service facility for technical equipment is modeled as a single server queue. Which models are available to integrated future information regarding job arrivals and service requirements for capacity planning or admission control? Identify and summarize the relevant literature. Spencer, Joel, Madhu Sudan, and Kuang Xu. Queuing with future information. The Annals of Applied Probability 24.5 (2014): Xu, Kuang, and Carri W. Chan. Using Future Information to Reduce Waiting Times in the Emergency Department via Diversion. (2014). 7

8 ZHANG, Shaoquan. Proactive Serving Decreases User Delay Exponentially. Diss. The Chinese University of Hong Kong, Plus additional references. 2.7 Yao s Millionaires Problem and secure computation Secure (multiparty) computation can be used to compute a result from private data such that the original sensitive data is not revealed. The idea behind SMC stems from Yao s Millionaires Problem where two millionaires want to find out which one of them is richer without revealing their actual wealth. Thus, with secure computation supply chain collaboration is feasible such that competitive information of the actors in the supply chain is not leaked. This may reduce the reluctance of supply chain actors to participate in collaboration. Describe Yao s Millionaires Problem and illustratively explain how it can be solved. Explain how secure computation can be used in the supply chain collaboration context. What are potential challenges and benefits. explain-it-like-im-five-thesocialist-millionaire-problem-and-secure-multi-party-computation Atallah, Mikhail, et al. Private collaborative forecasting and benchmarking. Proceedings of the 2004 ACM workshop on Privacy in the electronic society. ACM, Atallah, Mikhail, et al. Secure collaborative planning, forecasting, and replenishment (SCPFR). Multi-Echelon/Public Applications of Supply Chain Management Conference Pibernik, Richard, et al. Secure collaborative supply chain planning and inverse optimization The JELS model. European Journal of Operational Research (2011): And the references therein. 8