AMELIA CARR, Bowling Green State University
Email: ascarr@bgsu.edu
SENTHIL KUMAR MUTHUSAMY, Bowling Green State University - Department of Management
Email: smuthu@cba.bgsu.edu

This paper is a study of the coordination capability between operations and other functional areas within the firm. The paper examines a number of relationships with respect to cross-functional coordination and performance. Using a random sample of 231 firms, five hypotheses are tested. Structural equation modelling is used to test the relationships depicted in the research model. The results indicate that firms can benefit from the cross-functional coordination between operations, marketing, engineering and purchasing.

SURESH SETHI, University of Texas at Dallas - School of Management
Email: sethi@utdallas.edu
DENNY YEH, Fu Jen Catholic University
Email: yeh@im.fju.edu.tw
RONG ZHANG, Chongqing University - College of Economics and Business Administration
Email: zhangrong@cqu.edu.cn
ANDREW JARDINE, affiliation not provided to SSRN
Email: jardine@mie.utoronto.ca

This paper considers a problem of optimal preventive maintenance and replacement schedule of equipment devoted to extracting resources from known deposits. Typical examples are oil drills, mine shovels, etc. At most one replacement of the existing machinery by a new one is allowed. The problem is formulated as an optimal control problem subject to the state constraint that the remaining deposit at any given time is nonnegative. We show that the optimal preventive maintenance, production rates, and the replacement and salvage times of the existing machinery and the new one, if required, can be obtained by solving sequentially a series of free-end-point optimal control problems. Moreover, an algorithm based on this result is developed and used to solve two illustrative examples.

SERGIO CHAYET, Washington University, Olin Business School
Email: chayet@wustl.edu
WALLACE J. HOPP, affiliation not provided to SSRN
Email: hopp@northwestern.edu

We develop a risk-sensitive strategic facility sizing model that makes use of readily obtainable data and addresses both capacity and responsiveness considerations. We focus on facilities whose original size cannot be adjusted over time and limits the total production equipment they can hold, which is added sequentially during a finite planning horizon. The model is parsimonious by design for compatibility with the nature of available data during early planning stages. We model demand via a univariate random variable with arbitrary forecast profiles for equipment expansion, and assume the supporting equipment additions are continuous and decided ex-post. Under constant absolute risk aversion, operating profits are the closed-form solution to a nontrivial linear program, thus characterizing the sizing decision via a single first-order condition. This solution has several desired features, including the optimal facility size being eventually decreasing in forecast uncertainty and decreasing in risk aversion, as well as being generally robust to demand forecast uncertainty and cost errors. We provide structural results and show that ignoring risk considerations can lead to poor facility sizing decisions that deteriorate with increased forecast uncertainty. Existing models ignore risk considerations and assume the facility size can be adjusted over time, effectively shortening the planning horizon. Our main contribution is in addressing the problem that arises when that assumption is relaxed and, as a result, risk sensitivity and the challenges introduced by longer planning horizons and higher uncertainty must be considered. Finally, we derive accurate spreadsheet-implementable approximations to the optimal solution, which make this model a practical capacity planning tool.

ALAIN BENSOUSSAN, University of Texas at Dallas - School of Management
Email: alain.bensoussan@utdallas.edu
METIN CAKANYILDIRIM, University of Texas at Dallas - School of Management
Email: metin@utdallas.edu
SURESH SETHI, University of Texas at Dallas - School of Management
Email: sethi@utdallas.edu

Information delays exist when the most recent inventory information available to the Inventory Manager (IM) is dated. Such situations arise when it takes a while to process the demand data, count the inventory, and pass the results to the IM. We show that the optimal total inventory-related cost decreases when the length of the information delay decreases. The amount of the decrease is an important datum for an IM interested in considering whether or not to invest in reducing the delay. The investment is required to finance design and acquisition of an information (collection and dissemination) system that can reduce the information delay. Such systems include phone calls, business meetings, and the use of information collection mechanisms such as radio frequency identification tags.