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Introduction
Published in Christine Owen, Ghosts in the Machine, 2017
Historically, the major eras in management-oriented approaches to work organisation have included ‘Taylorism’, emphasising the elimination of worker inefficiency. Taylorism, for example, provided early attempts to examine the structure of work organisation by detailing how the modification of rules, divisions of labour and hierarchies can enhance worker productivity. Taylorism led to a focus on work-tasks and structure and hierarchies of wage compensation. Although there is no one best way, most organisational theorists have shifted to a contingency approach (Van de Ven et al. 2013). Contingency theory suggests that the most appropriate set of structures and processes for an organisation will be contingent on finding the best fit with the environment. Organisational problems are characterised as a misfit between the organisation's structure and the environment within which it operates. Three dimensions have been identified as key features of organisational structure (Harper 2015) that enable or constrain fit with the environment.
Designing for Customer Value
Published in James William Martin, Operational Excellence, 2021
Projects have risks and issues that must be managed. A risk is an uncertainty associated with one or more key project deliverables. These include accurately capturing customer requirements, meeting the agreed-upon project schedule, meeting cost targets, successfully deploying the required technology, and creating the production process. In addition to risks, there are external factors that may impact a project. Some of these are associated with macroeconomic and microeconomic trends and competitive threats. Although project teams cannot eliminate these external factors, contingency plans can be created to mitigate their impact on a project.
Project risk management
Published in John M. Nicholas, Herman Steyn, Project Management for Engineering, Business and Technology, 2020
John M. Nicholas, Herman Steyn
Contingency planning implies anticipating risks that might arise and then preparing a course of action to cope with them. The initial project plan is followed, and throughout execution the risks are closely monitored. Should a risk materialize as indicated by a trigger symptom, the contingency plan is adopted. The contingency plan can be a post-hoc remedial action to compensate for a risk impact, an action undertaken in parallel with the original plan, or a preventive action initiated by a trigger symptom to mitigate the risk impact. Multiple contingency plans can be developed based upon “what-if” scenarios for multiple risks.
Deploying hybrid modelling to support the development of a digital twin for supply chain master planning under disruptions
Published in International Journal of Production Research, 2023
The third set of papers studied the service phase of the physical twin’s lifecycle. These studies investigated the role of SC digital twins in managing disruptions. SC Digital twins can simulate different scenarios to evaluate the impact of disruptions such as supplier delays, transportation disruptions, or changes in customer demand on SC performance. By modelling these disruptions, companies can assess the potential consequences and explore alternative courses of action. This helps in developing contingency plans and making proactive decisions to mitigate the impact of disruptions. Burgos and Ivanov (2021) used a SC digital twin to assess the impact of COVID-19 on food retail SCs. Park, Son, and Noh (2021) presented a digital twin-based SC control framework to minimise bullwhip effect and ripple effect in an automobile parts SC. Badakhshan and Ball (2023) presented a SC digital twin framework for inventory and cash management under physical and financial disruptions. Zdolsek Draksler, Cimperman, and Obrecht (2023) developed a SC digital twin to tackle a last mile delivery problem in the presence of transportation disruptions. The main shortcoming of these studies is that they used either simulation or coupled simulation with machine learning to manage disruptions. These studies cannot optimise SC performance in the presence of disruptions due to the nature of simulation and machine learning, which are primarily predictive rather than prescriptive.
Autonomous mobile robots in sterile instrument logistics: an evaluation of the material handling system for a strategic fit framework
Published in Production Planning & Control, 2023
Giuseppe Fragapane, Hans-Henrik Hvolby, Fabio Sgarbossa, Jan Ola Strandhagen
Contingency theory is a major theoretical lens used to view organisations and support organisations to see the relation between organisational characteristics and contingencies, such as the environment, size and strategy for reaching high performance (Donaldson 2001). This theory provides a substantial basis for investigating fit (Acur, Destan, and Boer 2012) because the concept of strategic fit builds on contingent views of strategy and resources (Venkatraman 1989). Strategic fit describes a situation in which elements both internal and external to the organisation are aligned (Scholz 1987), and this fit between a firm and its environment is crucial to yield desirable performance implications (Zott 2003; Fainshmidt et al. 2019). Therefore, strategic fit has been a powerful tool for managers to match the demand and supply characteristics on a strategic level (Fisher 1997; Christopher, Peck, and Towill 2006; Gligor, Esmark, and Holcomb 2015) because it helps reveal the ideal state towards which a logistics system should be continually directed (Zajac, Kraatz, and Bresser 2000). This concept can be used on a supply chain level (Cannas et al. 2020) and for areas within the supply chain, such as production (Buer et al. 2016) or, as in our study, transportation.
Business intelligence and analytics value creation in Industry 4.0: a multiple case study in manufacturing medium enterprises
Published in Production Planning & Control, 2020
Fanny-Eve Bordeleau, Elaine Mosconi, Luis Antonio de Santa-Eulalia
RBV does not account for the way resources are applied and assumes a perfect utilization (Melville, Kraemer, and Gurbaxani 2004). This limitation is often countered with the inclusion of contingency factors (Fink, Yogev, and Even 2017; Dubey, Gunasekaran, and Childe 2018). Contingency theory is built on the rejection of a unique best way to achieve organizational goals; an organization must adapt its strategy to contextual factors (Taylor and Taylor 2014). Performance is achieved by maintaining a fit between strategy and organizational context (Taylor and Taylor 2014). In information systems, having the right resources is not sufficient, since resources are easily imitable and mobile (Bharadwaj 2000). For resources to really lead to business value, a firm has to appropriately exploit these resources (Ross, Beath, and Goodhue 1996). Capabilities are ‘an organization’s ability to assemble, integrate, and deploy valued resources, usually in combination or “copresence”’ (Bharadwaj, 2000, p. 171). Big data analytics capabilities were linked to competitive advantage (Fosso-Wamba et al. 2017; Dubey, Gunasekaran, and Childe 2018). However, business value is not limited to competitive advantage. The resources and capabilities model can also explain operational process-level value often expressed as operational efficiency and effectiveness (Melville, Kraemer, and Gurbaxani 2004).