http://directory.umm.ac.id/Journals/Journal%20of%20Operations%20Management/Vol19.Issue2.Feb2001/245.pdf

Arcs of integration: an international study of supply chain strategies

Over the past fifteen years, a number of studies have examined the determinants of firm R&D spending. These studies, however, almost invariably focus on the role of firm or external ownership characteristics in predicting R&D spending while overlooking the attributes of the top managers involved in allocating corporate resources. In this study, we change that focus by empirically examining how R&D spending as compared to industry competitors varies at firms based on the characteristics of their CEOs. Using a sample of publicly traded firms, we find that CEO characteristics explain a significant proportion of the sample variance in firm R&D spending even when corporate strategy, ownership structure, and other firm-level attributes are controlled. In terms of individual CEO characteristics, we find that R&D spending is greater at firms where CEOs are younger, have greater wealth invested in firm stock and signifacant career experience in marketing and/or engineering/R&D. In contrast to existing theory, we find that the amount of a CEO's formal education had no significant association with R&D spending once a CEO has attained a college degree. However, significant R&D spending increases are found at firms where CEOs have advanced science-related degrees. From subgroup analyses, we further find that CEO effects on relative R&D spending increase with longer CEO tenure implying that CEOs, over time, may mold R&D spending to suit their own preferences. From these results, we make implications for both research on determinants of R&D spending and managerial practice.

CEO Characteristics and Firm R&D Spending

This paper describes an adaptive control approach to improve urban mobility and relieve congestion. The basic idea consists in monitoring and controlling aggregate vehicular accumulations at the neighborhood level. To do this, physical models of the gridlock phenomenon are presented both for single neighborhoods and for systems of inter-connected neighborhoods. The models are dynamic, aggregate and only require observable inputs. The latter can be obtained in real-time if the neighborhoods are properly instrumented. Therefore, the models can be used for adaptive control. Experiments should determine accuracy. Pareto-efficient strategies are shown to exist for the single-neighborhood case, and optimality principles are introduced for multi-neighborhood systems. The principles can be used without knowing the origin–destination table or the precise system dynamics.

Urban Gridlock: Macroscopic Modeling and Mitigation Approaches

A firm's organizational and technological boundaries are two important demarcation lines when sourcing for technology. Based on this theoretical lens, four possible combinations of exploration and exploitation emerge. Applying an ambidexterity perspective to a firm's technology sourcing strategy, we hypothesize that a curvilinear relationship exists between a firm's technology sourcing mix and its performance. We further introduce a contingency element by proposing that a firm's absorptive capacity exerts a positive moderating effect on this relationship. We empirically test these hypotheses on a random, multi-industry sample of U.S. manufacturing companies. We find support for the notion that the relationship between technology sourcing mix and firm performance is an inverted U-shape. Moreover, higher levels of absorptive capacity allow a firm to more fully capture the benefits resulting from ambidexterity in technology sourcing.

https://www.scheller.gatech.edu/directory/faculty/rothaermel/pubs/09_orgsci.pdf

Ambidexterity in Technology Sourcing: The Moderating Role of Absorptive Capacity

Much of our understanding of human thinking is based on probabilistic models. This innovative book by Jerome R. Busemeyer and Peter D. Bruza argues that, actually, the underlying mathematical structures from quantum theory provide a much better account of human thinking than traditional models. They introduce the foundations for modelling probabilistic-dynamic systems using two aspects of quantum theory. The first, 'contextuality', is a way to understand interference effects found with inferences and decisions under conditions of uncertainty. The second, 'quantum entanglement', allows cognitive phenomena to be modeled in non-reductionist ways. Employing these principles drawn from quantum theory allows us to view human cognition and decision in a totally new light. Introducing the basic principles in an easy-to-follow way, this book does not assume a physics background or a quantum brain and comes complete with a tutorial and fully worked-out applications in important areas of cognition and decision.

/pdf/quantum-models-of-cognition-and-decision-hejlh57vgk.pdf

Quantum Models of Cognition and Decision

R&D intensity and manufacturing performance were evaluated in this study of 600 durable goods firms in 20 countries. In a path-analytic model, R&D intensity was significantly associated with improvements in market share (R2 = 34%), controlling for firm size, previous market share, and regardless of industry or region of the world. Market share increases were also significantly correlated with improvements in manufacturing agility (R2 = 4%). Agility improvement was significantly correlated with R&D intensity, and computerization in manufacturing, controlling for firm size and region, and did exhibit industry effects, with electronic equipment firms elevated on this measure. Computerization exhibited regional (not industry) differences, with South American firms depressed on this measure. The role of computerization in manufacturing and agility in firm innovativeness are discussed.

R&D and Global Manufacturing Performance

This paper explores some of the traffic phenomena that arise when drivers have to navigate a network in which queues back up past diverge intersections. If a diverge provides two alternative routes to the same destination and the shorter route has a bottleneck that generates a queue, one would expect that queue to stabilize at an equilibrium level where the travel time on both routes is roughly equal. If the capacity of the alternative route is unlimited then this network can accommodate any demand level. However, if the bottleneck is so close to the upstream end of the link that the equilibrium queue cannot be contained in the link, then the trip time on the queued route cannot grow to match that on the alternate route. This means that the alternative route can never be attractive, even if the queue spills back past the diverge, and that drivers approaching the diverge will act as if the alternative route did not exist. As a result, a steady flow into the system greater than the capacity of the bottleneck will cause a queue to grow all the way back to the origin (blocking it). The final result is an "oversaturated static state" where the queue regulates the input flow into the system. Curiously, if the bottleneck capacity of this network is reduced below a critical level (or is eliminated altogether) then the alternative route becomes attractive again and the system cannot reach the saturation point. This phenomenon is explored in the paper, where it is also shown that: i) a network can become permanently oversaturated/undersaturated as a result of a temporary increase/decrease in link capacity, ii) even under the most favorable assumptions, and in contrast to the equivalent assignment problem with point queues, a network can be stable both in an oversaturated and an under-saturated state, and iii) temporary endogenous disturbances can permanently reverse the saturation state of a network. These findings suggest that in certain situations the time-dependent traffic assignment problem with physical queues is chaotic in nature and that (as in weather forecasting) it may be impossible to obtain input data with the required accuracy to make reliable predictions of cumulative output flows.

Queue Spillovers in Transportation Networks with a Route Choice

We study a production-inventory system with multiple unreliable supply sources. Through inspection and rework, the system can improve the quality of the units received from the supply sources. There are two interleaved decisions: the replenishment quantities from the sources and the inspection-rework quantities among the units received. We show the optimal solution to the replenishment decision can be efficiently derived from a greedy algorithm, and inspection-rework is optimally applied to a single source identified by the algorithm. Furthermore, in the case of linear cost functions, it is optimal to place orders from two supply sources, i.e., dual sourcing. The results extend to the infinite-horizon case, where an order-up-to policy is optimal. The model also readily adapts to situations in which the supply imperfection takes the form of a reduced delivery quantity (yield loss).

Optimal Replenishment and Rework with Multiple Unreliable Supply Sources

A key central tenet of decision theory is that decomposing an uncertain event into sub-events should not change the overall probability assigned to that uncertain event. As we show, both quantum physics and behavioral decision theory appear to systematically violate this principle in very similar ways. These results suggest that the structuring phase of decision analysis-which specifies how various events are decomposed-helps shape the subjective probabilities which will ultimately be assigned to those events.

https://pubsonline.informs.org/doi/pdf/10.1287/opre.46.6.923

Quantum Mechanical and Human Violations of Compound Probability Principles: Toward a Generalized Heisenberg Uncertainty Principle

A batch of products is to be supplied to customers with warranty. The units in the batch are either defective or nondefective, with different lifetime distributions. The defect rate-the proportion of defects in the batch-is itself a random variable, known only in terms of its distribution. We develop a sequential quality control procedure that exploits the knowledge of the defect distribution gained through inspection, and strikes an optimal balance between the inspection repair cost and the warranty cost. We identify a simple threshold policy, and we prove its optimality for a very general class of warranty cost functions without imposing any restrictions on the type of distributions involved. The key to optimality is that the warranty cost, as a function of the number of inspected units and the conditional defect index, satisfies a so-called K-submodularity property, which is a strengthening of the usual notion of submodularity.

Jinfa Chen

Papers

R&D and Global Manufacturing Performance

Queue Spillovers in Transportation Networks with a Route Choice

Optimal Replenishment and Rework with Multiple Unreliable Supply Sources

Quantum Mechanical and Human Violations of Compound Probability Principles: Toward a Generalized Heisenberg Uncertainty Principle

Quality Control for Products Supplied with Warranty