Nagraj Balakrishnan

Environmental considerations on the optimal product mix

Abstract Several types of regulations limit the amount of different emissions that a firm may create from its production processes. Depending on the emission, these regulations could include threshold values, penalties and taxes, and/or emission allowances that can be traded. However, many firms try to comply with these regulations without a systematic plan, often leading not only to emission violations and high penalties, but also to high costs. In this paper, we present two mathematical models that can be used by firms to determine their optimal product mix and production quantities in the presence of several different types of environmental constraints, in addition to typical production constraints. Both models are comprehensive and incorporate several diverse production and environmental issues. The first model, which assumes that each product has just one operating procedure, is a linear program while the second model, which assumes that the firm has the option of producing each product using more than one operating procedure, is a mixed integer linear program. The solutions of both models identify the products that the firm should produce along with their production quantities. These models can be used by firms to quickly analyze several “what if” scenarios such as the impact of changes in emission threshold values, emission taxes, trading allowances, and trading transaction costs.

Early/tardy scheduling with sequence dependent setups on uniform parallel machines

Abstract We consider the problem of scheduling N jobs on M parallel machines that operate at different speeds (known as uniform parallel machines), to minimize the sum of earliness and tardiness costs. Jobs are assumed to arrive in a dynamic albeit deterministic manner, and have nonidentical due dates. Violations of due dates result in earliness or tardiness penalties that may be different for different jobs. Setup times are job-sequence dependent and may be different on different machines based on the characteristics of the machines. For this problem, we present a mixed integer formulation that has substantially fewer zero–one variables than typical formulations for scheduling problems of this type. We present our computational experience in using this model to solve small sized problems, and discuss solution approaches for solving larger problems. Scope and purpose In JIT environments, firms face the need to complete jobs as close to their due dates as possible. Failure to do so would result in earliness and/or tardiness costs and the optimum schedule would seek to minimize functions of these costs. In many real-world situations, the problem is greatly complicated by the presence of disparate issues such as: (i) uniform parallel machines that are capable of processing these jobs at different speeds; (ii) sequence-dependent setup times; (iii) distinct job due dates; (iv) distinct job ready dates; and (v) distinct earliness and/or tardiness costs for each job. For this complex problem, we present a compact mathematical model and describe our computational experience in using this model to solve small sized problems.

Modeling the relationship between corporate strategy and wealth creation using neural networks

In this paper, we hypothesize that there is a non-linear relationship between corporate strategy, short-run financial variables, and wealth creation measured as market value added (MVA), and use neural networking to model this relationship. The neural network model accurately categorized over 90% in the training set and nearly 93% of firms in the holdout test sample. Additional analysis revealed that strategy variables were particularly effective predictors of an upward trend in wealth creation whereas short-run financial variables were more effective in predicting a downward trend, or wealth destruction. Neural networks outperformed discriminant analysis in predictive ability in all analyses, suggesting the presence of non-linear effects. This research represents a first attempt to use neural networking to model the relationship between corporate strategy and wealth creation.

Business Process Outsourcing: an event study on the nature of processes and firm valuation

Business Process Outsourcing (BPO) is a phenomenon that is rapidly increasing in both incidence and importance. This study empirically examines the value proposition of BPO with respect to the nature of the processes being outsourced. Using the event study methodology, we employ the value chain (VC) position and existing ownership of a business process as our primary independent variables, and the stock abnormal return in response to the BPO announcement as the dependent performance variable in our research model. The study was conducted on 298 BPO announcements from 1998 to 2005. Results support the argument that outsourcing is valuable for both primary and supportive business processes. However, we found that BPO announcements on primary processes yield higher abnormal returns than supportive processes. Although existing process ownership was not found to be a powerful differentiator for BPO performance, its interaction with VC position provides important insights into the timing of outsourcing. The evidence suggests that internal cultivation of processes is important for BPO success, particularly when BPO is applied to primary processes.

Robustness of capacity rationing policies

A recent paper discusses a capacity rationing policy that allows make-to-order manufacturing firms encountering expected total demand in excess of available capacity to discriminate between two classes of products, one yielding a higher profit contribution per unit of capacity allocated to it than the other. The result is a selective rejection of orders for the class with lower unit contribution, yielding an increase in total profit when compared to a base case that implements no capacity rationing. Implementation of the policy requires forecasts of demand parameters for both product classes. In this paper we test the sensitivity of the capacity rationing policy to forecast errors in these parameters. The results indicate that, on average, the rationing policy is quite robust in improving profit even when actual demands are approximately twenty percent different from forecast values.

An experimental comparison of capacity rationing models

In this paper, we evaluate two approaches to capacity rationing to manage demand in make-to-order manufacturing firms that face seasonal demand in excess of installed capacity. We assume that the firms produce two classes of products, one having a higher profit contribution per unit of capacity than the other. In the first approach, capacity is reserved for the higher profit class in a dynamic fashion using an expected value analysis each time an order is received for the lower profit class. In the second approach, the amount of capacity reserved for the higher profit class is set at the beginning of the planning horizon and never changed. The two approaches are evaluated relative to the case with no capacity rationing (i.e. orders are accepted as they arrive as long as capacity is available) for a wide array of experimental situations by varying the ratio of unit profit contributions for the two product classes, the ratio of total available capacity to total expected demand, the ratio of order arrival ra...

Applicant Evaluation in an Executive MBA Program.

Abstract The validity of models using only quantitative factors such as standardized test scores and undergraduate grade point averages to predict student performance is questionable for Executive Master of Business Administration (EMBA) programs, which are typically designed for older students. Hence, many EMBA administrators place greater weight on more subjective, qualitative factors characterizing work experience, motivation, and business success, even though they have incomplete evidence of their validity. Using three successive classes of EMBA students at a major private university in the United States, we developed models that combine traditional, quantitative factors with subjective factors to capture the relationship between those variables and EMBA student performance. Results indicate that the inclusion of qualitative factors enhances the ability of the models to predict student performance.

Opportunistic retooling of a flexible machine subject to failure

A set of jobs can be processed without interruption by a flexible machine only if the set of tools required by all jobs can be loaded in the tool magazine. However, in practice the total number of tools required by a job set would exceed the tool magazine capacity. In such situations, the job set has to be carefully partitioned at the start of the production run such that each partition can be processed without interruption. During the production run, if there are unscheduled machine downtimes due to machine failure, this provides an additional opportunity to optimally retool the magazine for a smaller job set consisting of just the unprocessed jobs. In this paper, we study job sequencing rules that allow us to minimize the total expected cost of machine down time due to machine failures and magazine retooling, assuming a dynamic re-sequencing of the unprocessed jobs after each machine failure. Using these rules, we develop a branch-and-bound heuristic that allows us to solve problems of reasonable size.

Reacting in real-time to production contingencies in a capacitated flexible cell

We present a PC-based procedure for the real-time revision of production and inventory schedules in a capacitated flexible manufacturing cell, so as to counter the impact of production contingencies (defined as unanticipated disturbances that force a revision in the schedule). In capacity constrained production systems, a reallocation of capacity among the various manufactured items coupled with a backordering of the end-item must occur to counter such contingencies. However, by taking advantage of existing and planned inventories in the current schedule generated using Materials Requirement Planning (MRP), it is possible to delay the onset of these end-item backorders, and provide the scheduler with additional time to explore other corrective measures such as using overtime and sub-contracting, before resorting to backordering. The objectives in our problem are to delay the end-item backorders as far as possible, minimize the magnitude of these backorders, and maximize the savings in inventory costs that may result from these backorders. The algorithm is capable of handling problems of large size and is computationally efficient. The implementation of the algorithm is extremely simple and requires only three items of data input in an open format as prompted by the system.

Firm valuation effects of the decision to adopt relationally governed business process outsourcing arrangements

Business process outsourcing (BPO) has recently grown in incidence and importance. However, academic research on this phenomenon is sparse. Further, studies on outsourcing have primarily used a transaction cost economics (TCE) lens, largely neglecting other key theoretical explanations of the outsourcing decision and performance. While TCE provides a useful framework, it underemphasises hybrid governance structures that reflect relationships that fall between markets and hierarchies. We examine the decision to adopt relationally governed BPO arrangements and the impact on firm value. We recognise ‘discriminatory alignment’ aspects of governance and argue that the nature of the process itself will influence the value that can be garnered through relational governance. Using secondary data on 298 BPO announcements, we test the proposed model and confirm that a higher level of relational governance adoption strongly enhances firm valuation. This positive valuation impact of relational governance adoption reaches an even higher level in situations of primary processes and processes that have had a presence in the outsourcing organisation. The results emphasise the importance of considering all processes for outsourcing, the critical consideration of relational governance and the importance of planning governance structures that are aligned with the nature and experience with the process being outsourced.