Stochastic Optimal Control for Staffing and Backlog Policies in a Two‐Stage Customized Service Supply Chain

Anderson, Edward G.; Morrice, Douglas J.; Lundeen, Gary

doi:10.1111/j.1937-5956.2006.tb00244.x

Cited by 36 publications

(14 citation statements)

References 31 publications

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“…This finding is not obvious in the sense that in the available empirical This PPM problem cannot be solved in closed form (Anderson and Morrice, 2006;Browning and Yassine, 2008). Furthermore, the pipeline interactions are difficult to study with traditional statistical methods or to anticipate with thought processes.…”

Section: Discussionmentioning

confidence: 99%

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Enhancing New Product Development (NPD) Portfolio Performance by Shaping the Development Funnel

Figueiredo

Loiola²

2012

Journal of Technology Management & Innovation

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New product development (NPD) projects are typically managed through a series of screens, or gates, where ideas compete for resources. Ideas are carved into projects, and these projects are reviewed, and approved or terminated through the screening process so that only the best performing projects continue to subsequent stages of design, development and testing, and are released into the market place (Krishnan and Ulrich 2001;Terwiesch and Ulrich 2009). Most large innovative organizations deal with more than one NPD project at a time and typically engage in product pipeline management (PPM), where a set of active projects are evaluated together while they traverse through a sequence of such screens. Key decisions in a R&D pipeline are: screen thresholds, complexity of projects, resource allocation and capacity adjustment biases. We explore the impact of structural and behavioral aspects of these decisions through a simulation based analysis of a pharmaceutical dataset. Results establish concave relationships between value created at the end of pipeline and the resource allocation and complexity allocation biases, indicating optimizability and a limit for front loading practices.Keywords: product development, ideas, projects, product pipeline management, development funnel, stage/gate, screening. risk, and the resolution of uncertainty. Wheelwright and Clark (1992) describe typical decision levers in this setting: resource allocation (allocating workers), selection of task complexity (defining number, size and relations between tasks), capacity utilization (work intensity), and the level of threshold (minimum quality or expected value) for passing through a screen and the frequency of screening. Resource allocation dictates the types and amounts of resources available for executing tasks before a project, or a cohort of projects, goes through a screen. Complexity selection defines the nature of these tasks, and the amount of resources it takes to complete these tasks. Even though the level of complexity at each stage is predetermined to a certain degree by the existence of a minimum number of tasks to be performed, and their sequence, it is fair to assume that there is considerable freedom to managers while deciding project activities. For example, Thomke and Fujimoto (2000) and Khurana and Rosenthal (1998) recommend the front loading of activities in a project, i.e. the increase in complexity and activities early in the development process, as a way of reducing uncertainty and the amount of rework or new work to be done later.Capacity utilization affects the tradeoff between output quality (and thus the value created), and throughput. The total amount of resources available for allocation across stages is determined by a budgeting exercise (Chao et al., 2009) and is divided among the stages, so that each stage receives a fraction of the total. The selection of average complexity in any one stage, on the other hand, is not subject to such a global constraint. Product portfolio management deals with the p...

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Section: Discussionmentioning

confidence: 99%

“…The existence of optimal levels is also not obvious since the problem cannot be solved in closed form, i.e. it is an NP hard problem (Anderson and Morrice 2006;Browning and Yassine 2008). …”

Section: Discussionmentioning

confidence: 99%

Enhancing New Product Development (NPD) Portfolio Performance by Shaping the Development Funnel

Figueiredo

Loiola²

2012

Journal of Technology Management & Innovation

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“…Studies of forecast variability in services have also focused on inventory or scheduling implications, rather than impacts on costs. Examples have been seen in retail (Thomas, 1993;Cachon, 2001;Ramanathan, 2012), restaurant management (Kimes & Thompson, 2004), shift scheduling (Brusco & Jacobs, 2000), staffing call centers (Green, Kolesar, & Soares, 2003;Whitt, 2006), service supply chains (Anderson, Morrice, & Lundeen, 2006), risk management (Sodhi, 2005), general service workforce scheduling problems (Hur, Mabert, & Bretthauer, 2004), health care (Boyle et al, 2012), and utilities (Conerly, 1983).…”

Section: Impact Of Forecast Errorsmentioning

confidence: 99%

Impact of Bias Magnification on Supply Chain Costs: The Mitigating Role of Forecast Sharing

Sanders

Graman

2016

Decision Sciences

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The impact of forecast error magnification on supply chain cost has been well documented. Unlike past studies that measure forecast error in terms of forecast standard deviation, our study extends research to consider the impact of forecast bias, and the complex interaction between these variables. Simulating a two-stage supply chain using realistic cost data we test the impact of bias magnification comparing two scenarios: one with forecast sharing between retailer and supplier, and one without. We then corroborate findings via survey data. Results show magnification of forecast bias to have a considerably greater impact on supply chain cost than magnification of forecast standard deviation. Particularly damaging is high bias in the presence of high forecast standard deviation. Forecast sharing is found to mitigate the impact of forecast error, however, primarily at higher levels of forecast standard deviation. At low levels of forecast standard deviation the benefits are not significant suggesting that engaging in such mitigation strategies may be less effective when there is little opportunity for improvement in accuracy. Furthermore, forecast sharing is found to be much less effective against high levels of bias. This is an important finding as managers often deliberately bias their forecasts and underscores the importance of exercising caution even with forecast sharing, particularly for forecasts that have inherently large errors. The findings provide a deeper understanding of the impact of forecast errors, suggest limitations of forecast sharing, and offer implications for research and practice alike.

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“…Many of the current research are focused on different fields of the service sector, covering services like health care [4,13,14], telecom [IS], tourism [16,17], after-sale [18,19,20], oilfield [21], consulting [22], municipality [23], and e government [24]. Tn terms of education industry, a case study on language school concerning its service quality in the supply chain is conducted [2S].…”

Section: Service Supply Chainmentioning

confidence: 99%

Service Supply Chain: Configuration Structure and Operations Mechanism

Lin

Zhou

Rong

et al. 2012

Proceedings of 2012 IEEE International Conference on Service Operations and Logistics, and Informatics

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The main purpose of this research is to identify the configuration structure of a service supply chain from a view of modularity, to address the operations mechanism of the service supply chain from the perspective of service dominant logic. Literature review and case study are adopted in this research. Data are mainly collected through interview and secondary documents. The resultsshown that the service supply chain is configured with different types of module actor. A framework is proposed with dimensions of Configuration and Co-creation to explain the operations mechanism of a service supply chain.

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Stochastic Optimal Control for Staffing and Backlog Policies in a Two‐Stage Customized Service Supply Chain

Cited by 36 publications

References 31 publications

Enhancing New Product Development (NPD) Portfolio Performance by Shaping the Development Funnel

Enhancing New Product Development (NPD) Portfolio Performance by Shaping the Development Funnel

Impact of Bias Magnification on Supply Chain Costs: The Mitigating Role of Forecast Sharing

Service Supply Chain: Configuration Structure and Operations Mechanism

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