Knowledge that Transforms

We use a real‐options approach to analyze investments in process improvement. We develop a simple, stochastic model of a firm making investment decisions in process improvement. Our analysis offers several interesting insights into investments in process improvement. First, early investment in process improvement results in valuable knowledge, which helps increase the value of the option to invest in process improvement in future periods. This may motivate a firm to invest in process improvements as early as possible. Second, it may be optimal for a firm to stop investing when such investments do not create enough value in the later stages of the investment horizon. Third, although one would expect the state of a firm's process relative to that of other firms to impact a firm's decision to invest in process improvement, this study finds that the impetus is conditional and identifies these conditions. Finally, in such an environment, the delay of investment in process improvement incurs an opportunity cost for a firm, and we show that the traditional net present value rule must incorporate this opportunity cost and the knowledge‐induced change in future option values to lead to a correct investment decision.

We develop a general model for software development process and propose a policy to manage system coordination using system fault reports (e.g., interface inconsistencies, parameter mismatches, etc.). These reports are used to determine the timing of coordination activities that remove faults. We show that under an optimal policy, coordination should be performed only if a “threshold” fault count has been exceeded. We apply the policy to software development processes and compare the management of those projects under different development conditions. A series of numerical experiments are conducted to demonstrate how the fault threshold policy needs to be adjusted to changes in system complexity, team skill, development environment, and project schedule. Moreover, we compare the optimal fault threshold policy to an optimal release‐based policy. The release‐based policy does not take into account fault data and is easier to administer. The comparisons help to define the range of project parameters for which observing fault data can provide significant benefits for managing a software project.

Should service capacities (such as agent groups in call centers) be pooled or not? This paper will show that there is no single answer. For the simple but generic situation of two (strictly pooled or unpooled) server groups, it will provide (1) insights and approximate formulae, (2) numerical support, and (3) general conclusions for the waiting‐time effect of pooling. For a single call type, this effect is clearly positive, as represented by a pooling factor. With multiple job types, however, the effect is determined by both a pooling and a mix factor. Due to the mix factor, this effect might even be negative. In this case, it is also numerically illustrated that an improvement of both the unpooled and the strictly pooled scenario can be achieved by simple overflow or threshold scenarios. The results are of both practical and theoretical interest: practical for awareness of this negative effect, the numerical orders, and practical scenarios in call centers, and theoretical for further research in more complex situations.

We examine optimal control decisions regarding pricing, network size, and hiring strategy in the context of open source software development. Opening the source code to a software product often implies that consumers would not pay for the software product itself. However, revenues may be generated from complementary products. A software firm may be willing to open the source code to its software if it stands to build a network for its complementary products. The rapid network growth is doubly crucial in open source development, in which the users of the firm's products are also contributors of code that translates to future quality improvements. To determine whether or not to open the source, a software firm must jointly optimize prices for its various products while simultaneously managing its product quality, network size, and employment strategy. Whether or not potential gains in product quality, network size, and labor savings are sufficient to justify opening the source code depends on product and demand characteristics of both the software and the complementary product, as well as on the cost and productivity of in‐house developers relative to open source contributors. This paper investigates these crucial elements to allow firms to reach the optimal decision in choosing between the open and closed source models.

The process of introducing new and phasing out old products is called product rollover. This paper considers a periodic‐review inventory system consisting of a manufacturer and a retailer, where the manufacturer introduces new and improved products over an infinite planning horizon using the solo‐roll strategy. We consider two scenarios: (1) the manufacturer does not share the upstream information about new‐product introduction with the retailer and (2) the manufacturer shares the information. For each scenario, we first derive the decentralized ordering policy and the system‐optimal ordering policy with given cost parameters. We then devise an optimal supply chain contract that coordinates the inventory system. We demonstrate that when the inventory system is coordinated, information sharing improves the performance of both supply chain entities. However, this may not be true if the inventory system is not coordinated. We also show that under the optimal contract, the manufacturer has no incentive to mislead the retailer about new‐product information in the information‐sharing model. When demand variability increases, information sharing adds more benefits to the coordinated supply chain. Our research provides insights about coordinating product, financial, and information flows in supply chains with product rollover.