Make Your Business Quantum-Ready Today

Quantum computing is a different way of computation that merges quantum mechanics with computer science and information theory, with a large enough payoff for managers to feel compelled to invest. At the same time, it is also uncertain when – if at all -- quantum computing will become widely available for mainstream business applications. Thus, managers will find themselves torn between 1) waiting for the technology to mature and risking competitors taking the lead and 2) investing now but with no sight of returns on this investment. This article provides a way out: Executives can invest today in quantum-inspired computing that emulates quantum computing on existing specialized digital computers. Any investment today will carry over to (true) quantum computing when it becomes available, and there are computational gains to be had right away for certain architectures and specific business applications. To get quantum ready, first, managers can use the information on providers and use cases in the existing computing ecosystem. Second, they should take stock of their companies’ needs for advanced computing to gauge the fit of quantum applications with their business requirements. Finally, they should try out pilot studies with quantum-inspired computing

Near optimality guarantees for data-driven newsvendor with temporally dependent demand: A Monte Carlo approach

We consider a newsvendor problem with stationary and temporally dependent demand in the absence of complete information about the demand process. The objective is to compute a probabilistic guarantee such that the expected cost of an inventory-target estimate is arbitrarily close to the expected cost of the optimal critical-fractile solution. We do this by sampling dependent uniform random variates matching the underlying dependence structure of the demand process - rather than sampling the actual demand which requires the specification of a marginal distribution function - and by approximating a lower bound on the probability of the so-called near optimality. Our analysis sheds light on the role of temporal dependence in the resulting probabilistic guarantee, which has been only investigated for independent and identically distributed demand in the inventory management literature. © 2013 IEEE

A simulation-based support tool for data-driven decision making: Operational testing for dependence modeling

Dependencies occur naturally between input processes of many manufacturing and service applications. When the dependence parameters are known with certainty, the failure to factor the dependencies into decisions is well known to waste significant resources in system management. Our focus is on the case of unknown dependence parameters that must be estimated from finite amounts of historical input data. In this case, the estimates of the unknown dependence parameters are random variables and simulations are designed to account for the dependence parameter uncertainty to better support the data-driven decision making. The premise of our paper is that there are certain cases in which the assumption of an independent input process to minimize the expected cost of input parameter uncertainty becomes preferable to accounting for the dependence parameter uncertainty in the simulation. Therefore, a fundamental question to answer before capturing the dependence parameter uncertainty in a stochastic system simulation is whether there is sufficient statistical evidence to represent the dependence, despite the uncertainty around its estimate, in the presence of limited data. We seek an answer for this question within a data-driven inventory-management context by considering an intermittent demand process with correlated demand size and number of interdemand periods. We propose two new finite-sample hypothesis tests to serve as the decision support tools determining when to ignore the correlation and when to account for the correlation together with the uncertainty around its estimate. We show that a statistical test accounting for the expected cost of correlation parameter uncertainty tends to reject the independence assumption less frequently than a statistical test which only considers the sampling distribution of the correlation-parameter estimator. The use of these tests is illustrated with examples and insights are provided into operational testing for dependence modeling. © 2014 IEEE

Properties of throughput in Kanban lines

Properties of throughput in Kanban line

Analysis of a Kanban Controlled Manufacturing System: Iteration Large-Step Primal-Dual Affine Algorithm for Linear Programming

This paper published in "Technometrics" 33 (1991), 393-40

Controlling serial production lines w/yield losses using kanbans

Controlling serial production lines w/yield losses using kanban

Heuristics for Kanban allocation in a multi-stage stochastic environment

Heuristics for Kanban allocation in a multi-stage stochastic environmen

Structural properties of a Kanban controlled serial manufacturing system

Structural properties of a Kanban controlled serial manufacturing syste

Structural properties of a Kanban controlled serial manufacturing system

Structural properties of a Kanban controlled serial manufacturing syste

Kanbans, generalized semi-Markov processes and anti-matroids

This paper published in "Multiple Comparisons in Biostatistics: Current Research in the Topics of Dunnett, C. W." (Ed. Fred M. Hoppe) Marcel Dekker, NY (Chap. 18, p. 315-330