Priority Rules for Multi‐Task Due‐Date Scheduling under Varying Processing Costs

We study the scheduling of multiple tasks under varying processing costs and derive a priority rule for optimal scheduling policies. Each task has a due date, and a non‐completion penalty cost is incurred if the task is not completely processed before its due date. We assume that the task arrival process is stochastic and the processing rate is capacitated. Our work is motivated by both traditional and emerging application domains, such as construction industry and freelance consulting industry. We establish the optimality of Shorter Slack time and Longer remaining Processing time (SSLP) principle that determines the priority among active tasks. Based on the derived structural properties, we also propose an effective cost‐balancing heuristic policy and demonstrate the efficacy of the proposed policy through extensive numerical experiments. We believe our results provide operators/managers valuable insights on how to devise effective service scheduling policies under varying costs.     

AN EXPERIMENTAL EVALUATION OF THE RELATIONSHIP OF UNCERTAINTY IN THE ENVIRONMENT TO INFORMATION USED BY DECISION MAKERS*

This paper discusses the relationship between uncertainty in a decision making environment and the desired characteristics of information used for decision making. The work is aimed at providing a better understanding of the variables that affect the design of Management Information Systems. An experiment was conducted using an inventory simulator. The subjects could decide on inventory control variables and the amount and type of information to be used in monitoring system performance. The demand for inventory was an external random variable under the control of the experimenter. The experiment investigated how different demand variances affected the decision information used, the decisions made, and the resulting decision effectiveness (cost.) It was found that decision review frequency was not affected by demand variability. However, age and degree of summary of information used were greatly affected. Subjects exposed to high variability used data with a shorter history and a higher level of detail than those exposed to low variability. It was also found that the number of reports used increased from the low to middle variance group, then decreased at very high variance. Subjects tended to “give-up” on their information system at high variance, and they relied on additional safety stock to prevent frequent stockouts. Finally, the correlation between the information used and decision effectiveness as measured by cost was low. This result was contrary to the subjects' beliefs that more and better information produced “better” decisions. It indicates that although variability may strongly affect preferences for different types of information, the information used may not in turn affect decision performance.     

The Effectiveness of Graphical Presentation for Information Extraction: A Cumulative Experimental Approach*

Three laboratory experiments were conducted to assess the relative strengths and weaknesses of bar, symbol, and line graphs for performing a variety of elementary information extraction tasks using two dependent variables, time and accuracy. The findings indicate that the degree of support provided by a particular graph format for a particular data extraction task depends on the matching of format and task in terms of their anchoring characteristics. Anchoring, in this context, refers to the phenomenon that specific and diverse parts of a graph are segmented by the reader to act as salient and relevant cues, or anchors, when different classes of information are to be extracted from the graph. A data extraction task has high x-value (y-value) anchoring if the x-axis (y-axis) component is represented in the question as either a given value or an unknown value. Conversely, a task has low x-value (y-value) anchoring if the x-axis (y-axis) component is not represented in the question as either a given value or as an unknown value. Data extraction accuracy was not significantly affected by presentation format. Bars provided the best time performance for data extraction tasks having high anchoring on both axes but were not appropriate for tasks having low anchoring on either the y axis or both the x and y axes. Line graphs tended to be worse in terms of time performance for tasks having high anchoring on both axes although they were as fast or better than other representations for tasks having low anchoring on both axes. Symbol plots appeared to possess anchoring characteristics associated with both bars and line graphs. Symbols (as with bars) tended to produce a time performance superior to that of line graphs for tasks having high anchoring on both axes; and (as with line graphs) symbols allowed faster results than bar graphs for tasks having low anchoring on either the y axis or both the x and y axes.     

Mechanisms to Induce Buyer Forecasting: Do Suppliers Always Benefit from Better Forecasting?

In this study, we consider a supplier's contract offerings to a buyer who may obtain improved forecasts for her demand over time. We investigate how the supplier can take advantage of the buyer's better forecasts and what kind of contracts he should offer to the buyer in order to maximize his profits. We model a natural forecast evolution where the buyer can obtain a more accurate forecast closer to the selling season. We assume there is information asymmetry between the buyer and the supplier at all times in that the buyer understands her demand better than the supplier. Three types of contracts that the supplier can offer are considered: (1) one where a contract is offered before the buyer has a chance to obtain improved forecasts, (2) one where a contract is offered after the buyer has obtained improved forecasts, and (3) a contingent (dynamic) contract which offers an initial contract to the buyer before she obtains improved forecasts, followed by a later contract (contingent on the initial contract) offered after improved forecasts have been obtained. We consider two scenarios: (1) where the supplier is certain that the buyer can obtain more accurate forecasts over time, and (2) where the supplier is uncertain about the buyer's forecasting capability (or forecasting cost). In the first scenario, we show that among the three types of contracts, the contingent contract is always the most profitable for the supplier. Furthermore, using the contingent contract, the supplier always benefits from higher accuracy of the buyer's demand forecasts. In the second scenario, we explicitly model the supplier's level of certainty about the buyer's capability of obtaining better forecasts, and explore how the supplier can design contracts to induce the buyer to obtain better forecasts when she is capable.     

The Effects of Process and Outcome Similarity on Users' Evaluations of Decision Aids*

Decision aids (DA) used in online shopping contexts have been shown to improve users' product choices. Given that previous research (e.g., Byrne & Griffitt, 1973 ) has demonstrated the positive effects of perceived similarity on an individual's evaluation of others, this study investigates the effects of users' perceived similarity with a DA on their evaluations of that DA. More specifically, we investigate the effect of users' perceptions of the similarity between their own decision process and that followed by the DA to arrive at a recommendation (decision process similarity), as well as the similarity between the recommendations made by the DA and users' initial choices (outcome similarity), on their evaluations of the DA's usefulness and trustworthiness. The results of this study show that perceived process similarity exerts positive and significant effects on users' perceptions of the DA's usefulness and trustworthiness. However, the effects of perceived outcome similarity on trust are completely mediated by perceived process similarity. It is also observed that the level of the user's domain knowledge moderates the effects of perceived decision process similarity on both perceived usefulness and trustworthiness. These results have implications for DA design. It is important that designers consider the process by which users make decisions for themselves and align the DA's decision process with those of the user's, especially for the novice user. The full mediation of the effect of outcome similarity on trust by process similarity highlights how a similar decision process can mitigate some of the negative effects of outcome dissimilarity.     

Dynamic Customer Acquisition and Retention Management

In consulting, finance, and other service industries, customers represent a revenue stream, and must be acquired and retained over time. In this paper, we study the resource allocation problem of a profit maximizing service firm that dynamically allocates its resources toward acquiring new clients and retaining unsatisfied existing ones. The interaction between acquisition and retention in our model is reflected in the cash constraint on total expected spending on acquisition and retention in each period. We formulate this problem as a dynamic program in which the firm makes decisions in both acquisition and retention after observing the current size of its customer base and receiving information about customers in danger of attrition, and we characterize the structure of the optimal acquisition and retention strategy. We show that when the firm's customer base size is relatively low, the firm should spend heavily on acquisition and try to retain every unhappy customer. However, as its customer base grows, the firm should gradually shift its emphasis from acquisition to retention, and it should also aim to strike a balance between acquisition and retention while spending its available resources. Finally, when the customer base is large enough, it may be optimal for the firm to begin spending less in both acquisition and retention. We also extend our analysis to situations where acquisition or retention success rate, as a function of resources allocation, is uncertain and show that the optimal acquisition and retention policy can be surprisingly complex. However, we develop an effective heuristic for that case. This paper aims to provide service managers some analytical principles and effective guidelines on resource allocation between these two significant activities based on their firm's customer base size.