Subhashish Samaddar

An analysis of interorganizational resource sharing decisions in collaborative knowledge creation

Abstract Collaborative relationships between organizations for knowledge creation have received considerable attention in recent literature. For any collaborative endeavor to succeed, adequate allocation and sharing of resources is important. We explored the conditions of resource sharing in which organizations are motivated to collaborate for knowledge creation, as well as the conditions required for the collaboration to continue. Using the game theoretic framework, we modeled the collaboration for knowledge creation as a Stackelberg leader–follower game. Specifically, we modeled two scenarios, the first in which organizations expend current efforts only, and the second in which organizations have made relevant prior efforts. The equilibrium values of current efforts in knowledge creation, the leader organization’s resource participation rate, and the total expected system gain in both of these scenarios were determined. Both within scenario and cross-scenario analyses were conducted. It is important to maintain an optimal ratio between the leader’s and follower’s marginal gains for the formation and continuation of the collaboration.

Inter-organizational information sharing: The role of supply network configuration and partner goal congruence

This paper presents a theoretical framework to investigate the relationships between the design of a supply network (SN) and inter-organizational information sharing (IIS). We distinguish between four different types of inter-organizational information sharing. These concepts are developed using a two-dimensional classification scheme consisting of varying levels of the volume of information shared and the strategic importance of this information in an organizational context. Theoretical arguments and analysis of secondary data are used to develop propositions regarding the association between SN configurations and IIS types, and the role of coordination structure in such associations.

How to anticipate the Internet's global diffusion

T racking the Internet’s global diffusion is a daunting but increasingly important task, especially for network capacity planners [8]. A 1997 report found that the Internet consists of more than 16 million registered host computers. Conceived initially as a demonstration project for the U.S. government, the Internet today aggregates traffic from a vastly wider set of constituencies. And commercial use now accounts for 58% of Internet traffic, far exceeding the network’s original purposes in research and education [2]. But consumers of Internet-related services are regularly frustrated by slow response time, inaccessible online services, and breakdowns leading to services being unavailable. These problems can follow inadequate capacity planning caused by ignorance of the Internet growth process. For example, planning for the number of hosts that can be supported has to account for the network’s potential future use. Rapid growth could lead to the saturation of address spaces for new hosts much more quickly than expected. Developing models that explain the growth process is critical for policy formulation, capacity planning, and introducing new network hardware and software. Growth projections inform providers of Internet products and services of the potential consumer base. Many other businesses, including training and educational enterprises, also need to reorient their strategic plans to account for Internet diffusion. Understanding Internet growth patterns involves assessing alternative models for the Internet diffusion process. The internal influence model is one class of models used to study the diffusion phenomenon. These models are informed by the diffusion of innovation and various economic theories and assume that nonadopters of an innovation are increasingly likely to imitate adopters over time. Empirical studies have used these models to investigate the diffusion of such communication systems as residential telephones [1], telecommunication sysHow to Anticipate the Internet’s Global Diffusion

Improving Revenue Management Decision Making for Airlines by Evaluating Analyst‐Adjusted Passenger Demand Forecasts*

To maximize revenue, airline revenue management analysts (RMAs) attempt to protect the right number of seats for late-booking, high-revenue-generating passengers from low-valued leisure passengers. Simulation results in the past showed that a major airline can generate approximately

Multi-Organizational Networks: Three Antecedents of Knowledge Transfer

500 million per year through efficient RM operations. Accurate passenger demand forecasts are required, because reduced forecast error significantly improves revenue. RMAs often adjust the system forecasts to improve revenue opportunity. Analysis of system forecast performance and analyst adjustment is complex, because one must account for all unseen demands throughout the life of a flight. This article proposes a method to account for unseen demand and evaluate forecast performance (adjusted or unadjusted) through a forecast monitoring system. Initial results from one major airlines origin-destination market data justify the value of RMA forecasting adjustments.

Resource sharing and scheduling for cyclic production in a computer-integrated manufacturing cell

Researchers have demonstrated that organizations operating within formal networks are more likely to experience knowledge transfer, and the associated benefits of knowledge transfer, than would organizations operating outside of a network. However, limited research attention has been given to how the established antecedents of knowledge transfer are affected by the different forms that multi-organizational networks can assume. Using two case studies, we develop six testable propositions regarding how three of the established antecedents of knowledge transfer —absorptive capacity, shared identity and causal ambiguity—would be affected by the different characteristics, which define multi-organizational network form. We discuss these propositions and raise issues of relevance for researchers and practitioners.

An experimental analysis of solution performance in a resource sharing and scheduling problem

Abstract This paper deals with the decision making for resource sharing and scheduling for a class of computer-integrated manufacturing cells. Each such cell is characterized by a set of resources and it can be used for the cyclic production of several products. The production (processing) of each product requires a set of operations with sequence constraints among them. Each operation can be performed using alternative modes. Each mode defines the subset of resources needed for the operation and the timing of their use. Resource setup times are sequence-dependent. The problem is to choose the best mode for each operation and, accordingly, to allocate and schedule resources in order to minimize make-span time. A general (feasible) mixed 0–1 LP model is proposed. A customized Branch and Bound type solution algorithm is presented, which fathoms only feasible solutions and finds all optimum solutions.

Controlling adverse effect on work in process inventory while reducing machine setup time

In this paper we study the performance of a branch and bound enumeration procedure in solving a comprehensive mixed-integer linear programming formulation of a resource-sharing and scheduling problem (RSSP). The formulation is monolithic and deterministic. Various independent factors of an RSSP generally influence the size of the problem. An experimental analysis is performed to examine how such factors influence performance. We found that the number of resources, the total number of modes allowed in the problem, and the average number of renewable resources used in the problem have significant influence on the computational time as well as other performance measures. In addition, some intermediate response variables are identified and additional insights regarding the influences of the independent factors on these variables are provided.

Designing Referral Network Structures and Decision Rules to Streamline Provision of Urgent Health and Human Services

Abstract Earlier research has found that the presence of setup time variance can cause an adverse effect on waiting time and inventory as one reduces setup time for a product on a single machine that processes a number of products in a cyclic production system [Sarkar, D., Zangwill, W.I., 1991. Variance effects in cyclic production systems. Management Science 37 (4) 444–453; Zangwill, W.I., 1987. From EOQ towards ZI. Management Science 33 (10) 1209–1223]. This finding validates what other researchers had believed from a rather anecdotal perspective: “variability reduction” is extremely important for improving overall effectiveness of a pull or JIT system [Schonberger, Richard J., 1982. Japanese Manufacturing Techniques: Nine Hidden Lessons in Simplicity. The Free Press, New York]. In this paper, we offer explicit mathematical equations that characterize the variance levels in order to offer exact conditions under which WIP improves or worsens when one reduces setup time. Specifically, we offer explicit conditions that find how much variance to reduce and when it is meaningful to reduce. Furthermore, it is found that variance reduction in any product setup, not necessarily the one whose setup time is being reduced, can be beneficial for the purpose. By using a queuing model of a two product cyclic production, this paper shows that in addition to variance, the amount of reduction in setup time also influences its effect on waiting time. The procedures developed in this research find (1) how much reduction in setup time is safe for a given situation, and (2) what to do if more reduction is desired.

The effect of setup time reduction on its variance

Thisstudyusesfullyfactorialcomputersimulationtoidentifyreferralnetworkattributes and referral decision rules that streamline the routing of people to urgent, limited services. As an example of a scenario, the model represents vaccine delivery in a city of 100,000 people during the first 30 days of a pandemic. By modeling patterns of communication among health care providers and daily routing of overflow clients to affiliated organizations, the simulations determine cumulative effects of referral network designs and decision rules on citywide delivery of available vaccines. Referral networks generally improve delivery rates when compared with random local search by clients. Increasing the health care organizations’ tendencies to form referral partnerships from zero to about four partners per organization sharply increases vaccine delivery under most conditions, but further increases in partnering yield little or no gain in system performance. When making referrals, probabilistic selection among partner organizations that have any capacity to deliver vaccines is more effective than selection of the highest-capacity partner, except when tendencies to form partnerships are very low. Implications for designing health and human service referral networks and helping practitioners optimize their use of the networks are discussed. Suggestions for using simulations to model comparable systems are provided.