Shailesh Kulkarni’s research while affiliated with University of North Texas and other places

This research examines maintenance outsourcing in the presence of cyber-attacks. The research findings contribute to the literature on maintenance outsourcing by considering how cyber-attacks affect channel coordination and, specifically, cost subsidization. The Internet of Things assumes the risk that a “smart” or connected manufacturing system could become the target of a cyber-attack. Manufacturers have to face the difficult decision of adopting increasingly costly security technologies or having the manufacturing system remain vulnerable to cyber threats. This study develops a model addressing this dilemma by providing insight into the effects of cost subsidization and tools that manufacturers may use to evaluate the impact that installation of a security system has on a manufacturing system’s profit. The research also analyzes the effect of a cyber-attack on the system when using an age replacement policy. Under such a system, repairs may cost more than replacement which suggests that new components are required immediately after failure. For “smart” manufacturing systems that use either maintenance outsourcing or an age replacement policy, the study provides optimal solutions for firms to maximize the manufacturing system’s profit with consideration of the firms’ physical equipment failure and cyber-attack rates. The findings help determine the value of a security system by providing guidelines that address the effects of the cost, failure rate, and “successful” cyber-attack rate parameters.

Purpose Operations managers are subjected to various cognitive biases, which may lead them to make less optimal decisions as suggested by the normative models. In their seminal work, Tversky and Kahneman introduced three heuristics based on which people make decisions: representativeness, availability, and anchoring. This paper aims to investigate the six cognitive biases resulting from the use of the representativeness heuristic, namely, insensitivity to prior probability of outcomes, insensitivity to sample size, misconception of chance, insensitivity to predictability, the illusion of validity, and misconception of regression. Specifically, the paper examines how cognitive reflection and training affect these six cognitive biases in the operations management context. Methods For each cognitive bias, a scenario related to operations management was developed. The participants of the experimental study are asked to select among three responses, where one response is correct and the other two are biased. A total of 315 students from the University of North Texas participated in this study and 302 valid responses were used in the analysis. Results The results show that in all six scenarios, >50% of the respondents make biased decisions. However, using simple training, the bias is significantly reduced. Regarding the relationship between cognitive biases and cognitive reflection, the results partially support the hypothesis that people with high cognitive reflection ability tend to make less biased decisions. Regarding the effect of training on making biased decisions, the results show that making people aware of the existence of cognitive biases helps them partially to avoid making biased decisions. Conclusion Overall, our study demonstrates the value of training in helping operations managers make less biased decisions. Our discussion section offers some related guidelines for creating a professional environment where the effect of the representativeness heuristic is minimized.

In this paper, we analyse the due-date-to-promise problem for a supplier who supplies parts to his customer. The parts are produced on an unreliable machine with constant production rate. The machine is leased from a contractor who is also entrusted with its maintenance. The supplier's decision is the optimal due date to promise to his customer, taking into account the holding and tardiness costs on the customer side, as well as the transfer payment to the contractor for the lease and maintenance of the equipment. The contractor in turn has to decide on the frequency of pre-ventive maintenance. Both maximize their own profits. For this finite horizon optimization problem, we employ a Non-Renewal-Theory based approach from the literature to derive the performance characteristics in the transient regime - first for a general setting involving general distributions and then, for a special case. We then use Game Theory to analyse the underlying two-member game for both the non-cooperative and cooperative cases. A numerical example drawn from the literature is used to illustrate the special case. We show that for the special case, the game has a unique Nash equilibrium. Another significant result is that the Nash solution dominates the Stackelberg solution when the supplier is the leader. The supplier is worse off when the contractor is the leader. The example further shows that the supplier and the contractor stand to gain under cooperation which is well documented in the literature. We point to relevant literature for strategies to enforce this cooperation.