Edward G. Anderson

The Nonstationary Staff-Planning Problem with Business Cycle and Learning Effects

Managing highly skilled employees is extremely complex because of the need to balance the costs and time lags associated with their training against the need to meet demand as quickly as possible. Unlike previous approaches to this problem in the staffing literature, this paper develops an optimal staffing policy at the strategic level to cope with nonstationary stochastic demand for a staff characterized by unproductive apprentice employees and fully productive experienced employees. The paper then explores the implications of this policy in different industries, using empirical data. Aside from the optimal policy, this papers primary results include: (1) demand volatility reduces average productivity, most especially under conditions of low (or slightly negative) growth and--nonintuitively-- low employee turnover or knowledge obsolescence rates; (2) there is a trade-off between meeting demand and high productivity; (3) firms withlonger business cycles should smooth their hiring and firing policies; and (4) firms in industries with longer training times should smooth their hiring and firing policies. The paper also explores the possible rewards from reducing training times and turnover rates. Finally, it discusses managerial implications and possible future directions in research.

A strategic supply chain simulation model

The authors describe a simulation game designed to quantify the benefits of an enterprise resource planning system coupled with the balanced scorecard framework in an extended enterprise. We present three scenarios of the same enterprise: a base case scenario with a non-integrated legacy system, a scenario with an integrated, enterprise resource planning system, and a scenario with an enterprise resource planning system using the balanced scorecard framework. Results from this game support our research and teaching activities on the benefits of systems integration, data and process standardization, visibility across the business enterprise, improved decision support functionality, and operationalizing strategy.

A Dynamic Model of Individual and Collective Learning Amid Disruption

Using the methodology of system dynamics, we model the effects of disruptive events on learning and productivity in organizations. We leverage the learning-by-doing and transactive memory system theories to model the underpinnings of learning processes at the collective and individual levels. We simulate the impact of disruptive events on organizational productivity and performance, such as employee turnover, technological innovation, reorganization, and extreme events such as natural disasters, which disrupt individual knowledge, collective knowledge, or both. Finally, we discuss implications of our findings for future research on organizational learning and productivity. One implication is that representing organizational learning by a single power-law learning curve or even by multiple noninteracting learning curves may be in many cases inadequate. Another is that disruptions to individual learning can be beneficial to organizations in the long run, whereas disruptions to collective learning are detrimental in the short and long run. We discuss the factors that might help organizations mitigate the negative effects of disruption so that learning can occur amid even the most disruptive events.

A simulation model to study the dynamics in a service-oriented supply chain

In this paper, we investigate the dynamic behavior of a simple service-oriented supply chain in the presence of non-stationary demand using simulation. The supply chain contains four stages in series. Each stage holds no finished goods inventory. Rather, the order backlog can only be managed by adjusting capacity. These conditions reflect the reality of many service (and custom manufacturing) supply chains. The simulation model is used to compare various capacity management strategies. Measures of performance include application completion rate, backlog levels, and total cumulative costs.

Business Cycles and Productivity in Capital Equipment Supply Chains

Cyclicality is a commonly observed phenomenon in market economies. Less well understood, however, is the amplification of cyclicality as one progresses up the supply chain from original equipment manufacturer (OEM) to first-, second-, and third-tier suppliers. Recent studies have focused on management techniques to minimize inventory costs when faced with amplification in product distribution chains (Baganha and Cohen 1996; Lee, Padmanabhan, and Whang 1997; Sterman 1989a).2 This paper instead examines long-term supplier productivity as influenced by amplification in capital goods supply chains.

The Effects of Outsourcing, Offshoring, and Distributed Product Development Organization on Coordinating the NPD Process

Outsourcing arrangements continue to evolve from peripheral activities, such as food services and back office transaction processing, to encompass more core activities such as new product development. Some arrangements maintain a clear lead organization while others, such as open source networks, are less centralized. We develop a framework identify the specific impact of different outsourcing arrangements on the search, selection, transformation, and coordination processes involved in new product development new product development.

Managing the impact of high market growth and learning on knowledge worker productivity and service quality

Abstract Many high technology firms in the information technology, engineering, and internet content industries experience exponential growth but are also dependent on knowledge workers requiring extensive periods of training or apprenticeship. Under these conditions productivity management becomes a critical yet dynamically complex issue. This paper uses control theory techniques to dynamically solve this staffing problem at a strategic level. The novelty of this papers formulation of the staffing problem lies in the use of time-discounting in combination with a special cost structure. The resulting optimal policy reflects the influence of both capacity shortfall and salary penalties. Under most real-world conditions, it will drive capacity and employment levels asymptotically towards a constant fractional shortfall. An illustration is presented using enterprise requirement planning (ERP) project implementers. One important managerial implication is that the interaction of extensive training requirements with market growth can cause firms to under-perform by delivering low levels of service at a high cost. Further this underperformance will escalate with increases in either market growth or training requirements. Finally, the paper puts this research into a framework with the experience curve and technology supply chain literatures to outline possible directions for future research.

Managing service supply relationships

Service supply relationship management is a systems approach that recognises the customer-supplier duality found in the delivery of services. For services, customers are suppliers of significant inputs (i.e., minds, bodies, belongings, and information) to the process. Implications for management arise from the fact that service supply relationships are inherently bi-directional. Three important features of service supply relationship management are bi-directional optimisation, management of productive capacity, and managing risk. Service supply relationship management incorporates customers into the knowledge management strategy of service companies to enhance the value and quality of the services provided.

A Simulation Exercise to Illustrate the Impact of an Enterprise System on a Service Supply Chain

In this paper, we present a computer-based simulation exercise designed to help students understand the impact of an enterprise system on business performance in a service supply chain. The particular service supply chain simulated in the exercise is a wireless telecommunications firm. In this exercise, students perform simulations to experience managing the supply chain of the telecommunications firm with and without an enterprise system. The simulator tracks their business performance. Then the results are used as the basis of discussion in a subsequent debriefing session. We describe the educational goals of the simulation exercise and how the exercise can be structured in order to achieve these goals. The latter is illustrated by the use of the simulation exercise in a masters level supply chain management course in the Red McCombs School of Business at the University of Texas at Austin. The simulator includes realistic details. In fact, it is based on the extensive consulting experiences of the first author with two North American telecommunications firms. We describe the simulator in detail under the various scenarios, explain how it was validated, and provide the simulator equations in system dynamics format in Appendix B.

An initial simulation model for aiding policy analysis in urban insurgencies

This paper aims to demonstrate the potential for using the system dynamics computer simulation methodology to gain insight into the evolution of insurgencies. In particular, it extends a prior system dynamics model of insurgencies containing the dynamic mechanisms of incident suppression, insurgent creation, and war weariness to also embrace the factors of unemployment, propaganda, finance, and weapons supply chains. Numerous policy simulation tests are then conducted using as a base case a calibration of the model to the Anglo-Irish War of 1916- 1921 to examine the effects of various policies aimed at suppressing insurgencies. The paper suggests that none of these policies - when implemented at achievable levels - will be nearly as successful in stopping an insurgency as a coordinated bundle of all of these policies together. The paper then concludes by proposing how a simulation model might be further developed to assist policy makers in managing current insurgencies throughout the world.