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Assessing Supply Chain Risk with Few Compulsory Subcontractors.pdf
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  Journal of Finance & Economics Volume 2, Issue 2 (2014), 01-15 ISSN 2291-4951 E-ISSN 2291-496X Published by Science and Education Centre of North America ~ 1 ~   Assessing Supply Chain Risk with Few Compulsory Subcontractors Dror Parnes 1*   1  College of Business, University of South Florida, Tampa, USA *   Correspondence: Dror Parnes, College of Business, BSN 3127, University of South Florida, 4202 E. Fowler Avenue, Tampa, FL 33620-5500, USA. Tel: 1-813-974-6357; E-mail: dparnes@usf.edu DOI: 10.12735/jfe.v2i2p1 Abstract In this study we propose a supply chain risk analytical model that incorporates three compulsory subcontractors, which are structured in a two-layer formation, while fault recognition by the monitoring chief manufacturer might be delayed. We analyze this precise production line since it simultaneously covers both a sole mandatory supplier and a second production layer, which includes two subcontractors that can partially back up each other throughout the production. This specific configuration not only can assist future investigations in building different quantitative schemes, but this formation also is rather widespread across the semiconductor industry. We therefore present a current illustrative example from this sector. In addition, we authenticate the validity of the proposed model and examine several sensitivities within through multiple numerical simulations. We find that the expected time to the next production failure is mostly sensitive to output variations within the sole supplier, and least sensitive to temporary pauses in the information flow across the supply network. JEL Classifications:  C63, D24  Keywords : supply chain risk, production disruption, compulsory subcontractors, fault recognition, chief manufacturer 1.   Introduction Over the past decade, numerous scholars have shown mounting interest in defining, classifying, and exploring the roots of Supply Chain Risk (SCR). In general, SCRs evolve from manufacturers’ inability to generate products on time and deliver those goods to the final assembly line at the required quantity or quality. These production problems habitually cause a failure to meet customers’ demand or even compromise safety provisos thus can further trigger financial,  performance, physical, reputation, social, and time losses to the parties involved. In this study we offer a novel analytical SCR model that incorporates three compulsory subcontractors, which are structured in a two-layer formation, while fault recognition by the monitoring chief manufacturer might be delayed. We construct this particular production line since, in essence, it simultaneously covers both a sole mandatory supplier as well as a second production layer, which includes two subcontractors that can back up each other throughout the production, at least to some degree. It is not only that this specific configuration can assist future investigations in  building different SCR quantitative schemes, but also it appears that this formation is rather widespread across the semiconductor industry. We therefore present a current illustrative example  Dror Parnes Submitted on January 06, 2014 ~ 2 ~   from this sector. Furthermore, we authenticate the validity of the proposed model and examine several model sensitivities through multiple numerical simulations. Throughout history, there are numerous legendary examples of significant supply chain disruptions. Among others we recollect that in 1998, a computer glitch at Ford’s supplier of door and trunk latches resulted in a three-day shutdown of the Fiesta and Puma manufacturing facilities in Germany. These three days alone cost Ford nearly 70 million pounds and the production of roughly 7,000 vehicles. Other instances include Ericsson that lost approximately 400 million euros after its supplier’s semiconductor plant caught on fire in 2000. Ford closed again five major factories for several days after all air traffic was suspended shortly after the September 11, 2001 terror attack in the U.S. Land Rover laid off 1,400 employees when one of its subcontractors filed for bankruptcy in 2001. More recently, Dell had to recall four million hazardous laptop batteries made by Sony in 2006. Ordinarily, there are multiple sources of SCR. Disruptions to production could take place  because of output variability and time inconsistency across the supply chain, natural disasters like earthquakes, flood, fire, or volcano eruptions, wars and terrorism, non-compliance to production specifications, sudden liquidation of subcontractors, complications for multinationals in foreign exchange rates, management failures such as fraud or dishonesty, violations of protection rights or intellectual property laws, or other contractual impediments with subcontractors. The implications of SCR are vast. These largely include the risk of inadequate inventory, price escalation, layoffs, legal disruptions, loss of reputation, or other financial consequences. The importance of studying and developing quantitative SCR models naturally escalates due to several current industry trends. These modern universal tendencies include the rise in the globalization of markets, the increase in strategic outsourcing by worldwide manufacturers, the growth in the reliance on specialized suppliers (mostly among technological firms), the expansion of global search for competitive advantages, and the emergence of information technologies that assist in monitoring complex supply networks. In light of these recent global trends and because production hazards often have an enormous impact on corporate profitability, we deploy hereafter a quantitative model that assists supply chain managers in gauging the likelihoods of future production disruptions. These prognostic estimations may direct production managers to better concentrate on the critical points throughout the manufacturing line. Risk auditors can also integrate the computed failure probabilities suggested here with the likely cost estimations at their specific production lines and through that assess the comprehensive distribution of probable manufacturing expenses. The study proceeds as follows. In Section 2 we provide a relatively short yet representative survey of the recent literature on SCR. In Section 3 we outline our SCR model with few compulsory subcontractors and possible delay to the information flow. In Section 4 we illustrate the validity of the proposed scheme with a genuine example from the semiconductor industry. In Section 5 we enhance intuition on the matter by simulating the main derivations and exploring several parameter sensitivities. In Section 6 we conclude. 2.   Recent Literature on SCR To place the current investigation in the right context and to further accentuate our unique contribution we survey hereafter some recent studies that discuss principal issues of SCR. Due to the immense volume of relevant literature, the following list of references is naturally incomplete.  Nonetheless, these selected articles, composed from the last ten years, shall provide the readers sufficient knowledge on the current topic and motivate our analytical scheme.  www.todayscience.org/jfe Journal of Finance & Economics Vol. 2, Issue 2, 2014   ~ 3 ~ Harland, Brenchley, and Walker (2003) present a review of definitions and classifications of the types of risks that jointly assemble SCR. Among the various dimensions impacting supply networks the authors identify scale, technological novelty, quantity of sub-systems components, degree of customization within the final product or service, quantity of alternative design and delivery paths, number of feedback loops throughout the production line and delivery arrangements, variety of distinct knowledge bases, skills and competencies incorporated, intensity and extent of end user involvement, uncertainty of end user requirements, extent of supplier involvement in the innovation and transformation process, regulatory environment, number of agents in the supply network, complexity of the financial contracts supporting the production, and extent of political and stakeholder intervention. Zsidisin (2003) examines several case studies of setbacks within supply chains and recognizes that the roots of SCR usually evolve from individual supplier failures as well as from market factors. The individual supplier failures largely include the inability to handle demand variations, the incompetence to provide quality products, and the incapacity to stay in pace with technological innovations. Market determinants mostly contain protective patent registrations and other market capacity constraints. Bogataj and Bogataj (2004) explore two key problems of SCR by seeking optimal facilities’ location and ideal production capacity given to these facilities to achieve the best response time when market perturbations are feasible. Christopher and Lee (2004) comment that SCR has intensified over the years since product and technology life-cycles have shortened significantly and competitive product introductions make life-cycle demand difficult to predict. The authors add that improving end-to-end visibility among subcontractors could mitigate SCR. Faisal, Banwet, and Shankar (2006) provide a hierarchy-based framework to reduce SCR by understanding the mutual relationships among various enablers. In particular, the authors recommend that production managers devote their maximum attention to the group of enablers having a high driving power and low dependence. Tang (2006) reviews several quantitative models for managing SCR and discusses the broad demand for new models in this field. Wagner and Bode (2006) further investigate the relationship  between supply chain vulnerability and SCR by conducting a large-scale survey among 760 executives from firms operating in Germany. The authors report that supply chain characteristics such as a firm’s dependence on certain customers and suppliers, the degree of single sourcing, and the reliance on global supply sources are the most dominant aspects of SCR. Bogataj and Bogataj (2007) propose a quantitative scheme of SCR that accommodates the costs in the event that certain  products are not at the required location, at the expected time, and of the essential quality or quantity. Goh, Lim, and Meng (2007) present a stochastic model for the multi-stage global supply chain network problem while incorporating a set of related hazards, including supply, demand, exchange, and disruption risks. Ritchie and Brindley (2007) suggest a different perspective on SCR by integrating the dimensions of supply and performance sources, drivers, consequences, and management responses and further provide a categorization of the respective risk drivers. Manuj and Mentzer (2008) focus on global SCR by surveying the literature and deploying a qualitative study based on 14 interviews and a focus-group meeting with senior supply chain executives of multinationals. Schoenherr, Tummala, and Harrison (2008) inspect the process used by a U.S. manufacturing company to assess SCR within the context of an offshore sourcing decision and empirically derive and cluster 17 relevant risk factors into main-objectives and sub-objectives. Main-objectives include sourcing characteristics related to the product, the partner, and the environment. Sub-objectives are further divided into product aspects and partner aspects. Product aspects include quality and cost, and  partner aspects comprise service and management capabilities. Tang and Tomlin (2008) offer a unified framework and five stylized analytical models that delve into the open question of how much flexibility is really needed to mitigate SCR. Wu and Olson (2008) first display three SCR evaluation models: a Chance Constrained Programming (CCP)  Dror Parnes Submitted on January 06, 2014 ~ 4 ~   model, a Data Envelopment Analysis (DEA) model, and a Multi-Objective Programming (MOP) model. The authors then form a hypothetical supply chain consisting of three levels and use simulated data with representative distributions to contrast and discuss the advantages and disadvantages of these three types of SCR evaluation schemes. Neiger, Rotaru, and Churilov (2009)  present a novel value-focused process engineering methodology for process-based SCR identification with the overall intention to increase value to the individual supply chain members as well as to the entire supply chain. Oke and Gopalakrishnan (2009) categorize SCR into inherent high-frequency risks and disruption infrequent risks. While the high-likelihood risks usually exhibit low impact on supply chains, the low-likelihood risks typically convey high impact on supply networks. The authors further examine both generic and specific mitigation strategies for dealing with these exposures. Sodhi and Tang (2009) explain that companies normally manage their SCR either with the strategic long-term or the tactical medium-term strategies. The authors then employ a linear programming model of deterministic supply chain planning that considers demand uncertainty and cash flows within a medium term. Trkman and McCormack (2009) present a new conceptual approach to identify and predict supply chain disruptions based on prior classification of problematic suppliers with respect to their attributes, performances, and supply chain characteristics, which are further modified by environmental factors. Yu, Zeng, and Zhao (2009) evaluate the impacts of supply disruption risks on the choice  between the famous single and dual sourcing methods in a two-stage supply chain with a non-stationary and price-sensitive demand. Tang and Musa (2011) survey the literature on SCR management and identify three main disruption flows in supply networks, namely material (products or components), cash (financial arrangements), and information (transparency across subcontractors). The authors state that quantitative models in this field are relatively scarce while information flow risk has received inadequate attention thus far. Thun and Hoenig (2011) review 67 manufacturing plants in the German automotive industry and further test the impact of SCR management on corporate performance while dividing the sample into two groups: the firms that take reactive steps and those that undertake preventative measures. The authors report that the group using reactive SCR management has higher average value in terms of disruptions resilience, whereas the group pursuing preventative SCR management has better values concerning production flexibility. By reviewing these prior studies as well as examining past instances we learn that SCR typically surface within two dimensions: (1) the presence of few compulsory subcontractors that cannot deliver on time necessary components to the main production line, while (2) any delay in the detection of these supply chain problems could add further assembly or delivery costs. In light of these two aspects, we direct our model hereafter to account for the failure likelihoods of limited mandatory suppliers and the time-delay of fault recognition by the chief manufacturer. 3.   A Model with Few Compulsory Subcontractors We now turn to develop an explicit SCR model that accounts for a chief manufacturer, which relies on three subcontractors structured in a two-layer formation, while fault recognition by the chief manufacturer could be delayed, depending on the nature of disruption and the degree of information flow between all the system’s producers. In particular, we assume that a chief manufacturer   outsources the production of two components vital to its lead product among three subcontractors. While supplier    is a sole authorized subcontractor that is exclusively responsible to assemble one of the critical components, suppliers   and   share production capacity within the second crucial module. Nevertheless, subcontractors   and  , which operate at a separate production layer than supplier   , are not necessarily autonomous. Without loss of generality, we assume that a production  www.todayscience.org/jfe Journal of Finance & Economics Vol. 2, Issue 2, 2014   ~ 5 ~ failure by any one of these two suppliers could trigger an immediate shift in the Production Failure Rate (PFR) of the other. This realistic presumption considers that upon a recognition of a  production failure of either subcontractors   or  , more production responsibilities will be instantaneously assigned to the remaining supplier, hence the residual subcontractor within this manufacturing layer would have to increase its production speed to be able to service the present customers’ demand. This enhanced production rate would conceivably intensify the respective PFR. We therefore denote    the initial PFR of subcontractor  ,    the srcinal PFR of subcontractor  ,    the new PFR of supplier   upon a prior failure of subcontractor  ,    the new PFR of supplier   upon a prior failure of subcontractor  , and    the independent PFR of the sole supplier   . Alternatively, we can assert that    =       and    =      , where    ≥  1  and    ≥  1  are two constants corresponding to the suppliers’ shifts from normal production speeds to enhanced  production rates. In addition, we assume that the chief manufacturer   intermittently monitors the entire  production line including the outsourced assembly of parts that are manufactured by the three subcontractors   ,  , and  . Thus, to minimize its response time, the chief manufacturer aspires to instantly detect any production disruption at its suppliers. However, we realistically presume that this coverage is imperfect hence fault recognition by the chief manufacturer could be delayed due to unintentional temporary problems in the information flow between the three suppliers and the main contractor, or because of intentional actions taken by the subcontractors to mask these infrequent  production interruptions. We therefore designate    the Fault Recognition Rate (FRR) by the chief manufacturer. All rates thus far can also be interpreted as probabilities per time unit. For the purpose of model tractability we also consider that throughout the production horizon all the relevant parameters,   ,   ,   ,   ,   , and    are constants. This presumption allows us to allocate average quantities or expected value to the above PFR and FRR and at the same time to further calibrate the model with every consequential change to these rates. In addition, we assume that all time intervals between production failures as well as fault recognitions are exponentially distributed. Not only that the Exponential distribution serves in most related disciplines a parallel  purpose, but this specific selection conveys an added value in our context. The Exponential distribution is a continuous dissemination, thus it allows us to yield reduced-form solutions, which are further programmable. This apparent advantage is highly beneficial when modeling complex  production systems. 1  The supply chain described thus far exhibits several risks, as follows. Whenever sole supplier    fails to provide its designated parts, the chief manufacturer cannot complete the production of its lead product. Alternatively, when both subcontractors   and   fail to supply their nominated components, the entire production line comes to a halt as well. Nevertheless, the production process is considered fully operational if sole supplier    is working effectively and at least one of the subcontractors   or   is functional, despite having a higher PFR upon a prior failure of the complement supplier within the same production layer. Clearly, if all three subcontractors are  perfectly operative, the entire production runs smoothly. We can further describe this specific supply chain network with a set of three sufficient and necessary conditions. The production line labeled thus far is fully functional at time   hence no apparent production disruption occurs until time   if and only if any one of the following three mutually exclusive States of Nature (  ) happens: 1   The Weibull distribution is another popular allocation when modeling failure rates. Although a deployment of the Weibull distribution requests a few more underlying assumptions for justifying the shape and the scale  parameters, with relatively little effort, the current scheme can utilize this substitute dissemination when needed.  
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