Abstract
In Chap. 2, we argue that supply chain configuration is one of the principal supply chain management decisions and that it has a profound impact on other subsequent managerial decisions. As described therein, the supply chain configuration problem is a complex problem, which is composed of several sub-problems. It is also emphasized that the solutions to these problems require design, modeling, and problem-solving techniques based on knowledge from various fields such as systems science, systems engineering, operations research, industrial engineering, decision sciences, management science, statistics, information sciences, computer science, and artificial intelligence. Some of the prominent techniques utilized from these fields are information modeling, process modeling, simulation modeling, data mining, and optimization. We build on this proposition by adopting a key problem of information integration in the supply chain, which has an embedded structure representing various sub-problems, and how its management relates many of the concepts espoused in this book about supply chain configuration. Also, this problem serves as a prime example of how crosscutting approaches drawn from various disciplines highlighted above may be adopted in devising solutions for the complex supply chain configuration problem. Before we proceed further, let us first develop a clear understanding of the information integration problem in the supply chain.
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References
Ackoff RL (1971) Towards a system of systems concepts. Manag Sci 17:661–671
Bellamy MA, Basole RC (2013) Network analysis of supply chain systems: a systematic review and future research. Syst Eng 16:235–249
Blanchard SB, Fabrycky WJ (1990) Systems engineering and analysis. Prentice Hall, Englewood Cliffs, NJ
Cachon GP (1999) Managing supply chain demand variability with scheduled ordering policies. Manag Sci 45:843–856
Chandra C (1997) Enterprise architectural framework for supply-chain integration. Proceedings of the 6th annual industrial engineering research conference, Miami Beach, USA, pp. 873–878, Accessed 17–18 May
Chandra C, Grabis J, Tumanyan A (2007) Problem taxonomy: a step towards effective information sharing in supply chain management. Int J Prod Res 45:2507–2544
Chen F, Drezner Z, Ryan JK, Simchi-Levi D (2000a) Quantifying the bullwhip effect in a simple supply chain: the impact of forecasting lead times and information. Manag Sci 46:436–443
Chen F, Ryan JK, Simchi-Levi D (2000b) Impact of exponential smoothing forecasts on the bullwhip effect. Nav Res Logist 47:269–286
Clark AJ (1972) An informal survey of multi-echelon inventory theory. Nav Res Logist Q 19:621–650
Clark AJ, Scarf H (1960) Optimal policies for multi-echelon inventory problem. Manag Sci 6:475–490
Cohen MA, Lee HL (1989) Resource deployment analysis of global manufacturing and distribution networks. J Manuf Oper Manag 2:81–104
Deleersnyder JL, Hodgson TJ, King RE, O’Grady PJ, Savva A (1992) Integrating kanban type pull systems and MRP type push systems: insights from a Markovian model. IIE Trans 24:43–56
Diks EB, De Kok AG (1998) Optimal control of a divergent multi-echelon inventory system. Eur J Oper Res 111:75–97
Diks EB, De Kok AG, Lagodimos AG (1996) Multi-echelon systems: a service measure perspective. Eur J Oper Res 95:241–263
Drew SAA (1975) The application of hierarchical control methods to a managerial problem. Int J Syst Sci 6:371–395
Farquhar A, Fikes R, Rice J (1997) The ontolingua server: a tool for collaborative ontology construction. Int J Hum Comput Stud 46:707–727
Fensel D, Harmelen F, Horrocks I, McGuinness DL, Patel-Schnaider PF (2001) OIL: an ontology infrastructure for semantic web. IEEE Intell Syst 2:38–45
Fensel D, Hendler J, Lieberman H, Wahlster W (2002) On-to-knowledge: semantic web enabled knowledge management semantic web technology. MIT Press, Boston, MA
Fox MS, Gruninger M (1999) Ontologies for enterprise integration. Department of Industrial Engineering, University of Ontario, Oshawa, ON
Fox MS, Barbuceanu M, Teigen R (2000) Agent-oriented supply-chain management. Int J Flex Manuf Syst 12:165–188
Graves SC (1982) Using Lagrangean techniques to solve hierarchical production planning problems. Manag Sci 28:260–275
Graves SC (1999) A single-item inventory model for a nonstationary demand process. Manuf Serv Oper Manag 1:50–61
Gruber TR (1993) A translation approach to portable ontologies. Knowl Acquis 5:199–220
Gruber TR (1995) Toward principles for the design of ontologies used for knowledge sharing. Int J Hum Comput Stud 43:907–928
Grubic T, Fan I (2010) Supply chain ontology: review, analysis and synthesis. Comput Ind 61:776–786
Grubic T, Veza I, Bilic B (2011) Integrating process and ontology to support supply chain modelling. Int J Comput Integrated Manuf 24:847–863
Gruninger M (1997) Integrated ontologies for enterprise modelling. In: Kosanke K, Nel J (eds) Enterprise engineering and integration building international consensus. Springer, Berlin, pp 368–377
Gruninger M, Atefi K, Fox MS (2000) Ontologies to support process integration in enterprise engineering. Comput Math Organ Theor 6:381–394
Guarino N (1995) Formal ontology conceptual analysis and knowledge representation. Int J Hum Comput Stud 43:625–640
Gupta S, Loulou R (1998) Process innovation, product differentiation and channel structure: strategic incentives in a duopoly. Market Sci 17:301–316
Hackman ST, Leachman RC (1989) A general framework for modeling production. Manag Sci 35:478–495
Hariharan R, Zipkin P (1995) Customer-order information leadtimes and inventories. Manag Sci 41:1599–1607
Hirsch B (1995) Information system concept for the management of distributed production. Comput Ind 26:229–241
Huang C, Lin S (2010) Sharing knowledge in a supply chain using the semantic web. Expert Syst Appl 37:3145–3161
IMTR (1999) IMTR Technologies for Enterprise Integration, Rev 31; Integrated Manufacturing Technology Roadmapping Project. Oak Ridge Centers for Manufacturing Technology, Oak Ridge, Tennessee
ISO TC 184/SC 5/WG 1 (1997) Requirements for enterprise reference architectures and methodologies. http://www.melnistgov/sc5wg1/gera-std/ger-anxshtml
Jennings R (1994) Cooperation in industrial multi-agent systems, vol 43, World scientific series in computer science. World Scientific Publishing Co., Singapore
Klir GJ (1984) Architecture of systems problems solving. Plenum Press, New York
Klir GJ (1991) Facets of systems science. Plenum Press, New York
Kosanke K (1995) CIMOSA – overview and status. Comput Ind 27:101–109
Lambert DM, Cooper MC, Pagh JD (1998) Supply chain management: implementation issues and research opportunities. Int J Logist Manag 9:1–19
Lee HL (1993) Effective inventory and service management through product and process redesign. Oper Res 44:151–159
Lee HL, Billington C (1993) Material management in decentralized supply chains. Oper Res 41:835–847
Lee HL, Padmanabhan V, Whang S (1997a) The bullwhip effect in supply chains. Sloan Manage Rev 38:93–102
Lee HL, Padmanabhan V, Whang S (1997b) Information distortion in a supply chain: the bullwhip effect. Manag Sci 43:546
Lesperance Y, Levesque HJ, Lin F, Scherl RB (1995) Ability and knowing how in the situation calculus. Stud Logica 66:165–186
Little JDC (1992) Tautologies models and theories: can we find “laws” of manufacturing? IIE Trans 24:7–13
Liu S, Moizer J, Megicks P, Kasturiratne D, Jayawickrama U (2014) A knowledge chain management framework to support integrated decisions in global supply chains. Prod Plann Contr 25:639–649
Malone TW, Crowston K (1994) The interdisciplinary study of coordination. ACM Comput Surv 26:87–119
Malone TW, Crowston K, Lee J, Pentland B, Dellarocas C, Wyner G, Quimby J, Osborn CS, Bernstain A, Hermen G, Klein M, O’Donnel E (1999) Tools for inventing organizations: towards a handbook of organizational processes. Manag Sci 45:425–443
Marra M, Ho W, Edwards JS (2012) Supply chain knowledge management: a literature review. Expert Syst Appl 39:6103–6110
Masahiko A (1984) The co-operative game theory of the firm. Oxford University Press, Oxford, UK
McCarthy J (2003) What is artificial intelligence? Computer Science Department Stanford University. http://www-formalstanfordedu/jmc/whatisai/whatisaihtml
McKelvey B (1982) Organizational systematics taxonomy evolution classification. University of California Press, Berkeley, CA
Meersman R (2001) Ontologies and databases: more than a fleeting resemblance. Rome OES/SEO Workshop
Metters R (1997) Quantifying the bullwhip effect in supply chains. J Oper Manag 15:89–100
Morris WT (1967) On the art of modeling. Manag Sci 13:B707–B717
NIST (1999) Manufacturing enterprise integration program. National Institute of Standards and Technology, Gaithersburg, MD
Pritsker AAB (1997) Modeling in performance-enhancing processes. Oper Res 45:797–804
Pyke DF, Cohen MA (1990) Push and pull in manufacturing and distribution systems. J Oper Manag 9:24–43
Shaw MJ, Solberg JJ, Woo TC (1992) System integration in intelligent manufacturing: an introduction. IIE Trans 24:2–6
Sousa P, Heikkila T, Kollingbaum M, Valckenaers P (1999) Aspects of cooperation. Distributed manufacturing systems. Proceedings of the second international workshop on intelligent manufacturing systems, pp 685–717
Sowa J (2000) Ontology metadata and semiotics. International conference on conceptual structures ICCS’2000, Darmstadt, Germany, pp 4–18
Staab S, Schnurr HP, Studer R, Sure Y (2001) Knowledge processes and ontologies. IEEE Intell Syst 16:26–34
Stader J (1996) Results of the enterprise project. Proceedings of expert systems 1996 the 16th annual conference of the British Computer Society Specialist Group on Expert Systems, Cambridge, UK
Stumptner M (1997) An overview of knowledge-based configuration. AI Comm 10:111–125
Swaminathan JM, Smith SF, Sadeh NM (1998) Modeling supply chain dynamics: a multiagent approach. Decis Sci 29:607
Thomas DJ, Griffin PM (1996) Coordinated supply chain management. Eur J Oper Res 94:1–15
Tzafestas S, Kapsiotis G (1994) Coordinated control of manufacturing/supply chains using multi-level techniques. Comput Integrated Manuf Syst 7:206–212
Uschold M, Gruninger M (1996) Ontologies: principles methods and applications. Knowl Eng Rev 11:93–155
Whang S (1995) Coordination in operations: a taxonomy. J Oper Manag 12:413–422
Wooldridge M, Jennings NR (1995) Intelligent agents: theory and practice. Knowl Eng Rev 10:115–152
Xu K, Dong Y, Evers PT (2001) Towards better coordination of the supply chain. Transport Res E Logist 37:35–54
Younis MA, Mahmoud MS (1986) Optimal inventory for unpredicted production capacity and raw material supply. Large Scale Syst 11:1–17
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Chandra, C., Grabis, J. (2016). Knowledge Management as the Basis of Crosscutting Problem-Solving Approaches. In: Supply Chain Configuration. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-3557-4_6
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DOI: https://doi.org/10.1007/978-1-4939-3557-4_6
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