A resilience by teaming framework for collaborative supply networks

A framework for design and operation of resilient supply networks is developed.Resilience is obtained by teaming disruption-prone agents to overcome disruptions.Teaming increases fault tolerance using fewer resources than traditional approaches.Protocols increase resilience in production and distribution networks' applications. Supply network resilience is an emerging concept related to the ability of a network to tolerate disruptions; current understanding of its meaning and dimensions, its role in the design and operation of supply networks, and its relation to sustainability is at its early stages. Existing approaches are based on the trade-off between increased resources and higher fault-tolerance. The Fault Tolerance by Teaming (FTT) principle of Collaborative Control Theory has been applied in sensor networks effectively and appears as a promising original approach not based on the aforementioned trade-off and capable of producing networks with higher resilience.Inspired by the FTT principle, a Resilience by Teaming Framework (RBT) for supply networks is developed to address the design and operation of resilient supply networks. RBT is tested and validated through the application of its protocols to case studies in production and distribution networks. Evidence from case studies' results suggests that through FTT-based protocols and RBT it is possible to achieve higher fault tolerance with fewer resources than under traditional approaches.

[1]  Tapas K. Das,et al.  Intelligent dynamic control policies for serial production lines , 2001 .

[2]  Deniz Türsel Eliiyi,et al.  The state of the art on buffer allocation problem: a comprehensive survey , 2014, J. Intell. Manuf..

[3]  Ruud H. Teunter,et al.  Inventory models with lateral transshipments: A review , 2011, Eur. J. Oper. Res..

[4]  Shimon Y. Nof.  Collaborative control theory for e-Work, e-Production, and e-Service , 2007, Annu. Rev. Control..

[5]  Dennis E. Blumenfeld,et al.  An analytical formula for throughput of a production line with identical stations and random failures. , 2005 .

[6]  Scott Jackson,et al.  Resilience principles for engineered systems , 2013, Syst. Eng..

[7]  Y. Sugimori,et al.  Toyota production system and Kanban system Materialization of just-in-time and respect-for-human system , 1977 .

[8]  Mehmet Savsar,et al.  Effects of kanban withdrawal policies and other factors on the performance of JIT systems : a simulation study , 1996 .

[9]  Yoram Koren,et al.  Operation of Manufacturing Systems with Work-in-process Inventory and Production Control , 2004 .

[10]  Shimon Y. Nof,et al.  A collaborative sensor network middleware for automated production systems , 2009, Comput. Ind. Eng..

[11]  Keely L. Croxton,et al.  ENSURING SUPPLY CHAIN RESILIENCE: DEVELOPMENT OF A CONCEPTUAL FRAMEWORK , 2010 .

[12]  Refik Güllü.  Base stock policies for production/inventory problems with uncertain capacity levels , 1998, Eur. J. Oper. Res..

[13]  M. C. Holcomb,et al.  Understanding the concept of supply chain resilience , 2009 .

[14]  Behrooz Karimi,et al.  Hub network design problem in the presence of disruptions , 2012, Journal of Intelligent Manufacturing.

[15]  David L. Woodruff,et al.  CONWIP: a pull alternative to kanban , 1990 .

[16]  Samir Dani,et al.  Resilience: the concept, a literature review and future directions , 2011 .

[17]  Tadeusz Sawik,et al.  Selection of a dynamic supply portfolio in make-to-order environment withrisks , 2011, Comput. Oper. Res..

[18]  Brian Tomlin,et al.  On the Value of Mitigation and Contingency Strategies for Managing Supply Chain Disruption Risks , 2006, Manag. Sci..

[19]  Stanley B. Gershwin,et al.  Setting Real Time WIP Levels in Production Lines , 2006 .

[20]  Shi Qian,et al.  Evaluation of network resilience, survivability, and disruption tolerance: analysis, topology generation, simulation, and experimentation , 2013, Telecommun. Syst..

[21]  Y. Sheffi,et al.  A supply chain view of the resilient enterprise , 2005 .

[22]  Alain Martel,et al.  Modeling approaches for the design of resilient supply networks under disruptions , 2012 .

[23]  T. Sawik.  Selection of resilient supply portfolio under disruption risks , 2013 .

[24]  Timothy J. Lowe,et al.  Location on Networks: A Survey. Part I: The p-Center and p-Median Problems , 1983 .

[25]  David Hutchison,et al.  Resilience and survivability in communication networks: Strategies, principles, and survey of disciplines , 2010, Comput. Networks.

[26]  Michel Gendreau,et al.  A review of dynamic vehicle routing problems , 2013, Eur. J. Oper. Res..

[27]  Shimon Y. Nof,et al.  Design of effective e-Work: review of models, tools, and emerging challenges , 2003 .

[28]  Shimon Y. Nof,et al.  Collaborative production line control: Minimisation of throughput variability and WIP , 2013 .

[29]  Shimon Y. Nof,et al.  Collaborative e-Work, e-Business, and e-Service , 2009, Handbook of Automation.

[30]  Ran S. Bhamra,et al.  Organisational resilience: development of a conceptual framework for organisational responses , 2011 .

[31]  Teodor Gabriel Crainic,et al.  Planning models for freight transportation , 1997 .

[32]  Yan Liu,et al.  Fault-tolerant sensor integration for micro flow-sensor arrays and networks , 2008, Comput. Ind. Eng..

[33]  Y. Liu,et al.  Distributed microflow sensor arrays and networks: design of architectures and communication protocols , 2004 .

[34]  J. Fiksel.  Sustainability and Resilience: Toward a Systems Approach , 2006, IEEE Engineering Management Review.

[35]  Mohammad Modarres,et al.  A New Approach to Find the Optimal Solution for Base Stock Policies , 2009 .

[36]  A. Andijani.  Trade‐off between maximizing throughput rate and minimizing system time in kanban systems , 1997 .

[37]  Kathryn E. Stecke,et al.  Throughput for Production Lines with Serial Work Stations and Parallel Service Facilities , 1996, Perform. Evaluation.

[38]  C. Sendil Kumar,et al.  Literature review of JIT-KANBAN system , 2007 .

[39]  Tim Mazzarol,et al.  The Resilience Architecture Framework: Four Organizational archetypes , 2014 .

[40]  C. S. Holling,et al.  Panarchy Understanding Transformations in Human and Natural Systems , 2002 .

[41]  Stanley B. Gershwin,et al.  Efficient algorithms for buffer space allocation , 2000, Ann. Oper. Res..

[42]  Nan Yang,et al.  Selecting a Portfolio of Suppliers Under Demand and Supply Risks , 2008, Oper. Res..

[43]  Burak Eksioglu,et al.  The vehicle routing problem: A taxonomic review , 2009, Comput. Ind. Eng..

[44]  M. Christopher,et al.  Building the Resilient Supply Chain , 2004 .

[45]  Sang Won Yoon,et al.  Demand and capacity sharing decisions and protocols in a collaborative network of enterprises , 2010, Decis. Support Syst..

[46]  R. L. Francis,et al.  State of the Art-Location on Networks: A Survey. Part II: Exploiting Tree Network Structure , 1983 .

[47]  Izak Duenyas,et al.  Base-stock control for single-product tandem make-to-stock systems , 1997 .

[48]  Andrea Matta,et al.  Analytical evaluation of the output variability in production systems with general Markovian structure , 2014, OR Spectr..

[49]  M. Migliore,et al.  An algorithm to find all paths between two nodes in a graph , 1990 .

[50]  Rodrigo Reyes Levalle,et al.  Resilience by teaming in supply network formation and re-configuration , 2015 .

[51]  J. Shanthikumar,et al.  Concavity of the throughput of tandem queueing systems with finite buffer storage space , 1990, Advances in Applied Probability.

[52]  Christopher S. Tang,et al.  The power of flexibility for mitigating supply chain risks , 2008 .

[53]  Bernhard Plattner,et al.  Network resilience: a systematic approach , 2011, IEEE Communications Magazine.

[54]  Douglas J. Thomas,et al.  Pooling Lead-Time Risk by Order Splitting: A Critical Review , 2006 .

[55]  Muris Lage Junior,et al.  Variations of the kanban system: Literature review and classification , 2010 .