Srikant M. Datar

Sensitivity, Precision, And Linear Aggregation Of Signals For Performance Evaluation

Several accounting and other signals are generally available for the construction of a managerial performance evaluation measure on which an optimal compensation contract is based. The demand for aggregation in evaluating managerial performance arises because reporting all the basic transactions and other nonfinancial information about performance is costly and impracticable (see Ashton [1982], Casey [1978], and Holmstrom and Milgrom [1987]). We identify necessary and sufficient conditions on the joint density function of the signals under which linear aggregation, a simple and commonly employed way to construct a performance evaluation measure, is optimal. This characterization suggests that the linear form of aggregation is optimal for a large class of situations. Focusing on performance measures that are linear aggregates enables us to determine the relative weights on the individual signals in the optimal linear aggregate, since these weights are invariant for all realizations of the signals. We interpret these weights in terms of statistical characteristics (sensitivity and precision) of the joint distribution of the signals.

The role of audits and audit quality in valuing new issues

Abstract This paper provides a model in which audited reports are valuable to entrepreneurs who have private information and seek to share risks with investors. A distinctive feature of the model is that the choice of auditor and the resulting audited report provide partial information about the entrepreneurs private information, and he resolves all remaining investor uncertainty by signalling with retained ownership. The value of an audit is increasing in audit quality and the firm-specific risk faced by the entrepreneur and is a nondecreasing function of the entrepreneurs expectations about the future value of the firm.

Software complexity and maintenance costs

While the link between the difficulty in understanding computer software and the cost of maintaining it is appealing, prior empirical evidence linking software complexity to software maintenance costs is relatively weak [21]. Many of the attempts to link software complexity to maintainability are based on experiments involving small pieces of code, or are based on analysis of software written by students. Such evidence is valuable, but several researchers have noted that such results must be applied cautiously to the large-scale commercial application systems that account for most software maintenance expenditures [13,17]

Relevant costs, congestion and stochasticity in production environments

Abstract Conventional management accounting principles used to evaluate relevant costs have been developed under the assumption of deterministic manufacturing settings. Manufacturing operations, however, are complex and stochastic. In this paper we examine the impact of stochasticity in the production process on relevant costs based on a dynamic assessment of capacity constraints. We develop a model to analyze the behavior of relevant costs with respect to changes in the expected duration and variability in set-ups and processing. An implication of this analysis is that for profit maximization capacity will exceed expected demand if production rates or demand are stochastic.

Software complexity and maintainability

This paper examines the relationships between software complexity and software maintainability in commercial software environments. Models are proposed for estimating the economic impacts of software complexity and for identifying the factors which affect a system’s complexity. Empirical work currently under way has shown these models to be implementable.

Measurement of Productivity Improvements: An Empirical Analysis

In this paper, we test for productivity gains resulting from the introduction of a productivity-based incentive program in a large manufacturing plant of a Fortune 500 corporation. We develop a methodology based on a stochastic nonparametric frontier estimation technique to evaluate productivity in the postincentive plan period relative to the pre-incentive plan period. We also test for productivity gains using stochastic parametric frontier approaches. The results of both the nonparametric and parametric stochastic frontier analyses indicate that the incentive program has a positive effect on indirect labor, manufacturing services, and materials productivity and relatively little effect on direct labor productivity.

Enamored with Scale: Scaling with Limited Impact in the Microfinance Industry

In many ways, the microcredit industry is a scaling success story. With fewer than 10 million clients a decade ago, the industry now serves approximately 150 million clients and continues to grow, even through the economic downturn. In part, this growth is made possible by the very nature of microcredit—it is a service that clients both need and are able to purchase, enabling donated funds to be recycled many times. In addition, the industry as a whole has served a critical role in catalyzing growth. Cooperative efforts among industry stakeholder groups have led to the development of information exchanges, rating systems, and research efforts that have rapidly improved efficiency and professionalization of microfinance. Nonetheless, mission drift has become a real concern, and the industry has reached a point where additional scale does not guarantee increased social impact. Some markets are saturated and hyper-competitive, while enormous demand remains unfulfilled in difficult-to-serve markets and among clients whose credit needs fall beyond the narrow range of products currently offered. Continued questions about microcredit’s poverty-reducing potential continue to plague the industry, and high rates of client defection and over-indebtedness provide cause for alarm. Without significant changes in microfinance institution (MFI) management and business models required to serve client needs, further growth may harm the very clients the industry seeks to serve.

Optimal Incentive Schemes in Bottleneck Constrained Production Environments

This paper analyzes the design of incentives and control measures in a manufacturing setting. In the environment we consider, the output of one operation serves as the input for a second operation, where there is a bottleneck; inputs must be processed at both workstations to produce the final output. Thus, high production volume at the first operation does not translate into high benefits to the owner unless the output is subsequently processed at the second workstation. This is a familiar context in manufacturing management (Lawrence and Buss [1992]). Critics such as Goldratt [1990] argue that in these situations, typical cost accounting efficiency measurements motivate the nonbottleneck operation to produce output beyond the available capacity of the bottleneck resource, leading to waste and excess work-in-process inventories. However, workers at the nonbottleneck operation can be induced not to overproduce if restricting production is desired by the owner. We characterize management accounting practices and incentive arrangements that balance output production at nonbottleneck operations with the input demands of the bottleneck facility.

Tradeoffs within costing systems between incentives and measurement objectives

Explains that a common problem of cost control at design stage is the firm’s (manager’s) desire for the lowest cost compatible with supporting innovation and the designer’s preference for the optimal design, which may be unnecessarily sophisticated. Develops a mathematical model to represent this situation, pointing out that the manager is usually unaware of the design alternatives unless they are revealed by the designer, but can use budgetary limits and “load” costs onto certain cost drivers (e.g. number of parts) to influence the designer’s choice and align his/her interests with those of the firm. Suggests that the difference between actual and “loaded” costs is a function of the non‐cost benefits from design choice (e.g. competitive edge) and the degree of information asymmetry between manager and designer. Considers the implications for costing activities and the limitations of the model.

Design Thinking and Innovative Problem Solving

In 2010, professors Srikant M. Datar and David A. Garvin and research associate Patrick G. Cullen published Rethinking the MBA: Business Education at a Crossroads. Rethinking the MBA took stock of business education in the US in a way that had not been done since the Carnegie Corporation and Ford Foundation each published reports on the subject in 1959. The research revealed an educational establishment disconnected in significant ways from the businesses and organizations in which its graduates traditionally go to work. Through interviews with numerous high-level executives and corporate recruiters and deans from highly ranked business schools in the US and Europe, a picture developed of MBAs whose skills were unequal to the most pressing needs of 21st century employers. Tasked with executing on known strategy or operations, MBAs performed very well. But when faced with “unstructured problems, ambiguous data, rapidly changing environments, and information overload” (Datar, Garvin and Cullen, 2010)—the new normal in an increasingly complicated and global world—MBAs faltered. Hence the question, Can anyone, including MBAs and executives with superb analytical skills, learn to think more innovatively and, if so, how might we go about developing these skills? Executives and managers could then train their associates in these skills.