ManMohan S. Sodhi

An Analysis of Sources of Risk in the Consumer Electronics Industry

The consumer electronics industry is a

Applications and Opportunities for Operations Research in Internet-Enabled Supply Chains and Electronic Marketplaces

240 billion global industry with a small number of highly competitive global players. We describe many of the risks associated with any global supply chain in this industry. As illustration, we also list steps that Samsung Electronics and its subsidiary, Samsung Electronics UK, have taken to mitigate these risks. Our description of the risks and illustration of mitigation efforts provides the backdrop to identify areas of future research.

Quantifying the effect of batch size and order errors on the bullwhip effect using simulation

The Internet is creating opportunities to apply optimization. Firms can use operations research (OR) to improve their supply-chain performance within the enterprise whether their supply chains are being Internet-enabled or not. They can increase benefits by using OR to improve planning and execution in Internet-enabled supply chains with an expanded physical scope that includes vendors and customers and with an expanded functional scope that includes product design, marketing, and customer-relationship management.

The OR/MS Ecosystem: Strengths, Weaknesses, Opportunities, and Threats

The bullwhip effect is the observed amplification in order-size variance for upstream nodes in a supply chain. Lee et al. (1997, Management Science, 43, pp. 546–558) identified four causes for a single-product demand: (1) nodes updating their forecasts independently; (2) order batching; (3) price fluctuations; and (4) rationing. Chen et al. (2000, Management Science, 46, 436–443) provided a lower bound of the impact of the first cause. We contribute by quantifying the impact of the other three causes individually through simulation. We require any order to be an integer multiple of the batch size and posit price fluctuations and rationing as causing random i.i.d. errors or deviations from the optimal order size. We find with high R2 that the increase in order variance over a “core” order variance (when none of the four causes is present) is directly proportional to the square of the batch size and to the variance of the order deviations.

A Flexible, Fast, and Optimal Modeling Approach Applied to Crew Rostering at London Underground

This paper is dedicated to Arthur Geoffrion, who serves as role model of a great researcher, educator, and practitioner. We believe that research, teaching, and practice are becoming increasingly disengaged from one another in the OR/MS ecosystem. This ecosystem comprises researchers, educators, and practitioners in its core along with end users, universities, and funding agencies. Continuing disengagement will result in OR/MS occupying only niche areas and disappearing as a distinct field even though its tools would live on. To understand the reasons for this disengagement better and to engender discussion among academics and practitioners on how to counter it, we present the ecosystems strengths, weaknesses, opportunities, and threats. Incorporated in this paper are insights from a cluster of sessions at the 2006 INFORMS meeting in Pittsburgh (“Where Do We Want to Go in OR/MS?”) and from the literature.

ASP, The Art and Science of Practice: Skills Employers Want from Operations Research Graduates

We present a general modeling approach to crew rostering and its application to computer-assisted generation of rotation-based rosters (or rotas) at the London Underground. Our goals were flexibility, speed, and optimality, and our approach is unique in that it achieves all three. Flexibility was important because requirements at the Underground are evolving and because specialized approaches in the literature did not meet our flexibility-implied need to use standard solvers. We decompose crew rostering into stages that can each be solved with a standard commercial MILP solver. Using a 167 MHz Sun UltraSparc 1 and CPLEX 4.0 MILP solver, we obtained high-quality rosters in runtimes ranging from a few seconds to a few minutes within 2% of optimality. Input data were takes from different depots with crew sizes ranging from 30–150 drivers, i.e., with number of duties ranging from about 200–1000. Using an argument based on decomposition and aggregation, we prove the optimality of our approach for the overall crew rostering problem.

ASP, The Art and Science of Practice: What Employers Demand from Applicants for MBA-Level Supply Chain Jobs and the Coverage of Supply Chain Topics in MBA Courses

We analyzed the text of more than 1,000 ads for operations research (OR) jobs. Our objective was to help industry employers benchmark the skills they are seeking in OR graduates with those that other employers are seeking. Educators can also compare their offerings against the skills industry employers seek. We found that employers of OR graduates consistently require modeling, statistics, programming, and general analytical skills in an operations management context as their primary requirements regardless of sector, function within company, and even degree type. These employers also require communication, leadership, project management, spreadsheet and database, and team skills in that order.

Designing Student Groupwork in Management Education: Widening the Palette of Options

We analyzed the text of 704 online advertisements of supply chain management jobs for MBA graduates. The content analysis of these job advertisements provided us with a list of supply chain topics, such as inventory management and supply management, and general skills, such as communication and leadership; it also showed the proportion of advertisements requesting these skills. We measured the relative coverage of the same supply chain topics in MBA-level supply chain electives and operations management core courses in 21 of the top 50 business schools in the United States by analyzing the course descriptions and the cases used in these courses. This enabled us to compare the relative importance of supply chain topics to employers on the “demand” side with the relative importance of supply chain electives in MBA curricula on the “supply” side in these schools. Our analysis indicated that the supply usually matches demand; however, there may be an undersupply of practice- or process-oriented topics, such as forecasting, procurement, supplier and vendor management, and contracts and negotiation. In addition, there may be an oversupply of conceptual and strategy-oriented topics, such as product design, supply chain design, and emerging information technology and management information.

Determining Supply Requirement in the Sales-and-Operations-Planning (S&OP) Process Under Demand Uncertainty: A Stochastic Programming Formulation and a Spreadsheet Implementation

The authors describe innovation in practice through the unusual deployment of teams in a master’s in management core course. Two parallel uses of teams were made, both drawing on the jigsaw team method, in one case with the team supporting individual work. The experiences support the idea of widening the palette of types of groupwork used in management education, as well as the need for this to be addressed by faculty teams and individual academics.

Managing Supply Chain Disruptions via Time-Based Risk Management

We show how to extend the demand-planning stage of the sales-and-operations-planning (S&OP) process with a spreadsheet implementation of a stochastic programming model that determines the supply requirement while optimally trading off risks of unmet demand, excess inventory, and inadequate liquidity in the presence of demand uncertainty. We first present the model that minimizes the weighted sum of respective conditional value-at-risk (cVaR) metrics over demand scenarios in the form of a binomial tree. The output of this model is the supply requirement to be used in the supply-planning stage of the S&OP process. Next we show how row-and-column aggregation of the model reduces its size from exponential (2T) in the number of time periods T in the planning horizon to merely square (T2). Finally, we demonstrate the tractability of this aggregated model in an Excel spreadsheet implementation with a numerical example with 26 time periods.