SIMPLE MODELS FOR MONITORING NEW PRODUCT PERFORMANCE

Several studies have shown that consumers trying a new brand of frequently purchased consumer goods behave differently from repeat buyers. The “customer mix” for a new brand changes over time. Early in a new brand's life, most of the buyers are triers, while later in its life, a significant portion of the buyers are repeaters. The study presented here tests the hypothesis that an aggregate sales forecast, “averaging” the behaviors of triers and repeaters, would be outperformed by a model that treats them separately. A new product model, giving separate consideration to triers and repeaters, is developed. This model is tested on predictive ability against a widely used single-equation aggregate model. Consumer panel purchase diaries and data on advertising in measured media, covering a period of 42 months following introduction of a major brand of cold tablets, serve as the proving grounds. Data for the early part of this period serve as the data base for both models, while data for the rest of the period serve to test the predictive ability of both models. The disaggregate model is shown to perform significantly better than the aggregate model in terms of predictive accuracy. It also offers several other advantages in use as a decision aid, both for GO-NO decisions and for marketing mix decisions. Finally, several problems in the implementation of the proposed model and implications for research strategy are discussed.     

A NEW CLASS OF MODELS FOR THE DISCRIMINANT PROBLEM

Discriminant analysis is relevant to business decision making in a variety of contexts, such as when one decides to make or buy a specified component, fund a venture project, or hire a particular person. Potential applications in artificial intelligence, particularly in the area of pattern recognition, have further underscored the importance of the field. A recent innovation in discriminant analysis is provided by special linear programming (LP) models, which offer attractive alternatives to classical statistical approaches. The scope of application in which discriminant analysis can be advantageously employed is broadened by the flexibility to tailor parameters in the LP approaches to reflect diverse goals and by the power to explore the sensitivity of these parameters. In spite of the promise of the LP formulations, however, limitations to their effectiveness have been uncovered in certain settings. A recent advance involving a normalization construct removes some of the limitations but entails solving the LP model twice (to allow for different signs of a normalization constant) and does not yield equivalent solutions for different rotations of the problem data. This paper introduces a new model and a new class of normalizations that remedy both remaining limitations, making it possible to take advantage of the modeling capabilities of the LP formulations without the attendant shortcomings encountered by earlier investigations. Our development shows by empirical testing and illustrative analysis that the quality of solutions from LP discriminant approaches is more favorable (relative to the classical model) than previously supposed.     

A BAYESIAN APPROACH TO DISCRIMINATION AMONG ECONOMIC MODELS*

Frequently, alternative theories are proposed as relevant models for an economic process. Which alternative is a relatively better representation is a question of fact, but empirical methods of resolving the issue have not been compelling. This study developes a technique for discrimination from Bayesian statistical theory. This technique can analyze an arbitrary number of alternatives simultaneously, and each alternative can be characterized by distribution theory appropriate to its structure. An example, the aggregate consumption function, is considered, and each alternative is characterized by the Normal regression process. The results may only be regarded as suggestive, as the parameterization of the structure may be incomplete on that assumption.     

NEW PRODUCT ANNOUNCEMENTS AND STOCK PRICES

The final test of any management decision is its effect on the value of the firm's securities. This paper examines the subsequent impact of the publication of new product announcements on stock returns. A distinguishing feature of this presentation is the integration of decision making in the marketing and finance functional areas.     

A NOTE ON INTEGRATING INVENTORY AND PRODUCT SALES-MIX MODELS

In a recent issue of Decision Sciences, Raiborn and Harris [2] discussed the integration of inventory and product sales-mix models. While they formulated a model which reflected some of the interactions between product sales-mix and inventory decisions, the purpose of this note is to point out deficiencies in their decision rule analysis for solving such integrated models.     

MANAGING PRODUCT RETURNS FOR REMANUFACTURING

Firms are often encouraged to offer environmentally friendly products as a demonstration of corporate citizenship. However, this may prove to be an unrealistic expectation since a rational firm will only engage in profitable ventures; those that increase shareholder wealth. We develop a framework for analyzing the profitability of reuse activities and show how the management of product returns influences operational requirements. We show that the acquisition of used products may be used as the control lever for the management and profitability of reuse activities. These activities, termed product acquisition management, affect several important business decisions. First, if a firm is to pursue reuse activities, these reuse activities must be value‐creating. Second, if a firm is to compete by offering remanufactured products, then we show how product returns management influences the overall profitability of such activities via a trial and error EVA approach. Third, we show how operational issues are strongly affected by the approach used to manage product returns. There is a need for future research specifying the mathematical relationship between acquisition price and the nominal quality of the returned product.     

JOINT PRODUCT COST ALLOCATION IN THE CONTEXT OF COST-PLUS PRICING DETERMINATIONS WITH NON-UNIFORM MARKUPS

Companies that use cost-based pricing sometimes manufacture products that involve joint cost allocation. A problem with this is that, while product prices are a function of the full cost, joint cost allocation methods using net realizable values depend on the product prices. This paper demonstrates that when all costs and production quantities are known or can be budgeted, it is possible to simultaneously determine unique product prices (having non-uniform markup rates) and cost allocations using the net realizable value method.     

NEW PRODUCT SCREENING VIA THE INTENTION-TO-BUY SCALE*

Data obtained from the intention-to-buy scale often are used for early product screening. The authors discuss current procedures to evaluate these data and indicate the minor rote that risk assessment has played. Evaluation rules that incorporate the risk factor are presented, and their usefulness for product selection is discussed.     

SUBJECTIVE EVALUATIONS FOR PRODUCT DESIGN DECISIONS

This paper presents a simple methodology to enable a firm to incorporate consumers' subjective evaluations early into the concept screening stage of a new product introduction process. The methodology involves eliciting ranked preference judgments toward a set of alternative product concepts, each described as a profile of attributes and derived from an interval-scaled utility score using Thurstone's comparative judgment model. The derived utility scores are then decomposed into simple effects attributable to each of the attributes, thus yielding functional representations of consumer preferences. Mathematical models are developed to utilize the functions to determine the best concept. Empirical illustrations are provided for application of the methodology.     

MODELS FOR OPTIMAL LEAD TIME REDUCTION

Most inventory and production planning models in the academic literature treat lead times either as constants or random variables with known distributions outside of management control. However, a number of recent articles in the popular press have argued that reducing lead times is a dominant issue in manufacturing strategy. The benefits of reducing customer lead times that are frequently cited include increased customer demand, improved quality, reduced unit cost, lower carrying cost, shorter forecast horizon, less safety stock inventory, and better market position. Although the costs of reducing lead times in the long term may be relatively insignificant compared with the benefits, in the short term these costs can have a significant impact on the profitability of a firm. This article develops a conceptual framework within which the costs and benefits of lead time reduction can be compared. Mathematical models for optimal lead time reduction are developed within this framework. The solutions to these models provide methods for calculating optimal lead times, which can be applied in practice. Sensitivity analysis of the optimal solutions provides insight into the structure of these solutions.     

LIABILITY AS A CATALYST FOR PRODUCT STEWARDSHIP

Product stewardship is the set of practices related to reducing risks from chemical and process hazards in a company's supply chain. This paper develops an economic framework for evaluating supply chain liability as a driver for adopting product stewardship. Companies that outsource production may face residual liability for damages from use of their products, when liabilities are large enough to exceed supply chain partners' assets. The resulting potential liabilities can be mitigated through product stewardship. This paper shows that extended supply chain liabilities provide incentives for investing in reducing environmental hazards throughout the supply chain.     

ADVERTISING DECISION RULES FOR MARKET SHARE MODELS

This paper develops rules for setting advertising budgets when long term effects of advertising and competitive promotional spending are considered in a market share model. In both deterministic and probabilistic cases, a firm's optimal advertising budget is shown to be relatively insensitive to the level of competitive advertising. The key determinant of the advertising level is long run expected profit, and the budgeting rule developed is to spend a constant percentage of long run profit on advertising. Reasons for this result are explored.     

INTEGRATED COMPONENT AND SUPPLIER SELECTION FOR A PRODUCT FAMILY

This paper examines the reduction in complexity of a product family through product design. By leveraging the commonalities among products in a family, the decision support methodology presented in the paper helps choose components and suppliers that minimize the sum of design, procurement, and usage costs. The problem of integrated component and supplier selection is conceptualized and formulated as an integer-programming model. Analysis of the model yields two properties, complete and continuous replacement, which form the basis of a heuristic procedure. Computational tests show that the heuristic provides results close to the optimal solution and can be used for selecting components and suppliers. Application of the model to an industrial problem is discussed.     

A TIME-SERIES APPROACH FOR CONSTRUCTING EXPECTATIONS MODELS*

This article presents an efficient way of dealing with adaptive expectations models—a way that makes use of all the information available in the data. The procedure is based on multiple-input transfer functions (MITFs): by calculating lead and lag cross correlations between innovations associated with the variables in the model, it is possible to determine which periods have the greatest effects on the dependent variable. If information about k periods ahead is required, fitted values for the expectation variables are used to generate k-period-ahead forecasts. These in turn can be used in the estimation of the transfer function equation, which not only contains the usual lagged variables but also allows for incorporation of lead-fitted values for the expectation variables. The MITF identification and estimation procedures used are based on the corner method. The method is contrasted with the Almon distributed-lag approach using a model relating stock market prices to interest rates and expected corporate profits.     

FAIR PRICING

This paper explores the consequences of supposing that consumers see a firm as fair if they cannot reject the hypothesis that the firm is somewhat benevolent towards them. When consumers can reject this hypothesis, some become angry, which is costly to the firm. The desire to appear benevolent can lead firms to adopt third‐degree price discrimination based on the income of different consumer classes while foreswearing third‐degree price discrimination based on differences in the elasticity of demand. It can also explain why prices seem to be more responsive to changes in factor costs than to changes in demand that have the same effect on marginal cost. Lastly, if consumers experience regret or disappointment when faced by increased prices, the model can explain why prices can be more rigid in response to disasters that increase demand dramatically than they are when there is a less substantial increase in demand.     

EVALUATING MULTIPLE CRITERIA—MODELS FOR TWO-CRITERIA SITUATIONS

A general procedure is given for evaluating various alternatives (e.g., investments, projects, systems, etc.) involving two criteria of evaluation. The method involves the detailed questioning of decision makers as to preferences between various combinations of pre-selected sets of alternatives under various conditions. From the responses to such questions, a mathematical function which can be applied repeatedly for implementing choices between other alternatives is established. An example of the application of the procedure is given, along with discussion of certain ramifications of the example. In addition to the specific application, potential extensions of the technique are discussed briefly.     

A CASE IN ON-LINE DECISION ANALYSIS FOR PRODUCT PLANNING1

This report presents an application of quantitative decision-analytic techniques to consider an opportunity for capital investment in a new product. Decision analysis is a well-established technique for evaluating major decisions in which substantial resources and time are available for the analysis and the problem is definitely formulated at the outset. This case study, however, illustrates how decision-analytic techniques can be used on-line to improve decision making in a situation in which time and resources are limited, and the manager's perception of the problem changes in response to feedback from the analysis. The basic decision problem was whether or not the AIL division of Cutler-Hammer, Incorporated should purchase a six-month option on a flight-safety system patent. The president of AIL had only a few weeks in which to make a decision. The analytic approach focused on a probabilistic discounted earnings model. This model was refined over a two-week period through a sequence of iterations which incorporated the results of both direct and indirect assessments of probabilities and values. The analysis affected the company's decision process by providing a vehicle for structuring the ongoing communication between AIL's president and his advisors, and by focusing their attention on the most important issues. In the end, the analysis facilitated a unanimous decision from the decision-making group not to purchase the option, but to consider participating in the license as a subcontractor at a later date. This strategy had not been considered at the outset.     

AN EMPIRICAL COMPARISON OF TWO MODELS FOR PREDICTING PREFERENCES FOR STANDARD EMPLOYMENT OFFERS

There are two major types of job-choice models in the current literature. One may be called an ideal job model, and the other is primarily a linear utility model. This study attempts to compare the predictive capacity of the two types of models. The ideal job model implemented in the study performs better than a linear utility model in predicting preferences among a set of standard job offers.     

RETURN ON INVESTMENT AS A CRITERION FOR INVENTORY MODELS

In the past most inventory formulations have utilized cost minimization or profit maximization as an optimizing criterion. When viewed from the standpoint of the owner or investor, maximizing the return on investment (ROI) is an appropriate criterion for many types of inventories. This paper proposes ROI as a criterion for inventory models and derives optimal reorder rules for some common assumptions. An economic order quantity that differs greatly from the traditional formulas is discussed. The paper also enumerates the conditions under which ROI is an appropriate criterion and contrasts it to the traditional cost minimization and profit maximization criteria.