Measuring unemployment

The forecasts and assumptions of government and business regarding availability of labour are based in part on official statistics has been a matter of considerable debate in recent years. In this paper the author presents his views on the interpretation of the relevant U.K. statistics.     

OPTIMAL PLANNING: THE USE OF SALES FORECASTS

Budgets are established and decisions in a firm are made on the basis of the sales forecast. In the majority of firms this forecast is a single value estimate of sales. The decisions made on the basis of the single value estimate frequently fall short of optimal strategies because of costs that occur when actual sales differ from forecast sales. These costs, measured in terms of opportunity losses, can be minimized when considered in conjunction with a probability distribution of sales. Three methods for determining the probability distribution of sales are considered in this paper. These are regression analysis Schlaifer's approach for establishing a subjective distribution, and Bayesian regression. The use of the distribution in conjunction with linear and quadratic loss functions is illustrated.     

AN ANALYSIS OF THE USEFULNESS OF DISAGGREGATED ACCOUNTING DATA FOR FORECASTS OF CORPORATE PERFORMANCE*

The purpose of this paper is to derive the conditions under which disaggregated accounting data contribute to more accurate forecasts of corporate performance. A comparison formula is derived and applied to actual data. The results obtained indicate that disaggregated data do not necessarily produce better forecasts of corporate performance than do aggregated data. The paper concludes with implications of the results to some reporting issues.     

A Note on the Aggregation of Event Probabilities

In this article, multiple forecasts given as probabilities of events are aggregated using two assumptions: calibration and conditional independence. The forecasts are treated as data and the aggregation is based on Bayes's theorem. A measure of discrimination is given and the behavior of the aggregated posterior probability is examined as the number of forecasters grows without bound. The work is motivated by recent research efforts employing large numbers of individual forecasts.     

Marketing Category Forecasting: An Alternative of BVAR‐Artificial Neural Networks*

Analyzing scanner data in brand management activities presents unique difficulties due to the vast quantity of the data. Time series methods that are able to handle the volume effectively often are inappropriate due to the violation of many statistical assumptions in the data characteristics. We examine scanner data sets for three brand categories and examine properties associated with many time series forecasting methods. Many violations are found with respect to linearity, normality, autocorrelation, and heteroscedasticity. With this in mind we compare the forecasting ability of neural networks that require no assumptions to two of the more robust time series techniques. Neural networks provide similar forecasts to Bayesian vector autoregression (BVAR), and both outperform generalized autoregressive conditional herteroscedasticty (GARCH) models.     

Feeling at Risk Matters: Water Managers and the Decision to Use Forecasts

Experts contend that weather and climate forecasts could have an important role in risk management strategies for community water systems. Yet, most water managers make minimal use of these forecasts. This research explores the determinants of the use of weather and climate forecasts by surveying managers of community water systems in two eastern American states (South Carolina and the Susquehanna River Basin of Pennsylvania). Assessments of the reliability of weather and climate forecasts are not driving their use as water managers who find forecasts reliable are no more likely to use them than are managers who find them unreliable. Although larger systems and those depending on surface water are more likely to use forecasts for some (but not all) purposes, the strongest determinant of forecast use is risk perceptions. Water managers who expect to face problems from weather events in the next decade are much more likely to use forecasts than are water managers who expect few problems. Their expectations of future problems are closely linked with past experience: water managers who have had problems with specific types of weather events (e.g., flood emergencies) in the last 5 years are likely to expect to experience problems in the next decade. Feeling at risk, regardless of the specific source of that weather-related risk, stimulates a decision to use weather and climate forecasts.     

Quantifying Potential Health Impacts of Cadmium in Cigarettes on Smoker Risk of Lung Cancer: A Portfolio-of-Mechanisms Approach

This article introduces an approach to estimating the uncertain potential effects on lung cancer risk of removing a particular constituent, cadmium (Cd), from cigarette smoke, given the useful but incomplete scientific information available about its modes of action. The approach considers normal cell proliferation; DNA repair inhibition in normal cells affected by initiating events; proliferation, promotion, and progression of initiated cells; and death or sparing of initiated and malignant cells as they are further transformed to become fully tumorigenic. Rather than estimating unmeasured model parameters by curve fitting to epidemiological or animal experimental tumor data, we attempt rough estimates of parameters based on their biological interpretations and comparison to corresponding genetic polymorphism data. The resulting parameter estimates are admittedly uncertain and approximate, but they suggest a portfolio approach to estimating impacts of removing Cd that gives usefully robust conclusions. This approach views Cd as creating a portfolio of uncertain health impacts that can be expressed as biologically independent relative risk factors having clear mechanistic interpretations. Because Cd can act through many distinct biological mechanisms, it appears likely (subjective probability greater than 40%) that removing Cd from cigarette smoke would reduce smoker risks of lung cancer by at least 10%, although it is possible (consistent with what is known) that the true effect could be much larger or smaller. Conservative estimates and assumptions made in this calculation suggest that the true impact could be greater for some smokers. This conclusion appears to be robust to many scientific uncertainties about Cd and smoking effects.     

A Methodology to Select a Wire Insulation for Use in Habitable Spacecraft

This paper investigates electrical overheating events aboard a habitable spacecraft. The wire insulation involved in these failures plays a major role in the entire event scenario from threat development to detection and damage assessment. Ideally, if models of wire overheating events in microgravity existed, the various wire insulations under consideration could be quantitatively compared. However, these models do not exist. In this paper, a methodology is developed that can be used to select a wire insulation that is best suited for use in a habitable spacecraft. The results of this study show that, based upon the Analytic Hierarchy Process and simplifying assumptions, the criteria selected, and data used in the analysis, Tefzel is better than Teflon for use in a habitable spacecraft.     

USING SPECTRAL ANALYSIS FOR FORECAST MODEL SELECTION

Spectral analysis can be used to determine the general class of forecasting models which should be considered for purposes of generating forecasts of a marketing time series. Power spectral estimates and cross-spectral estimates can aid in selecting the most appropriate model when several variations of the predetermined class are under consideration.     

Reconciling Uncertain Costs and Benefits in Bayes Nets for Invasive Species Management

Bayes nets are used increasingly to characterize environmental systems and formalize probabilistic reasoning to support decision making. These networks treat probabilities as exact quantities. Sensitivity analysis can be used to evaluate the importance of assumptions and parameter estimates. Here, we outline an application of info‐gap theory to Bayes nets that evaluates the sensitivity of decisions to possibly large errors in the underlying probability estimates and utilities. We apply it to an example of management and eradication of Red Imported Fire Ants in Southern Queensland, Australia and show how changes in management decisions can be justified when uncertainty is considered.     

ON THE IMPLICATIONS OF SPECIFICATION UNCERTAINTY IN FORECASTING*

Forecasters typically select a statistical forecasting model from among a set of alternative models. Subsequently, forecasts are generated with the chosen model and reported to management (forecast consumers) as if specification uncertainty did not exist (i.e., as if the chosen model were the “true” model of the forecast variable). In this note, a well-known Bayesian model-comparison procedure is used to illustrate some of the ambiguities and distortions of forecasts that do not reflect specification uncertainty. It is shown that a single selected forecasting model (however chosen) will generally misstate measures of forecast risk and lead to point and interval forecasts that are misplaced from a decision-theoretic point of view.     

Are environmental scanning units effective?

Many authorities have urged companies to set up environmental scanning to assist corporate planning. Some advocates have recommended a unit at corporate level. This would give breadth of view and penetration into the future. It would arm decision makers with accurate forecasts. The information would be broad in scope and future directed. It could provide also assumptions for long-range planning. The Fahey and King study produced a model of corporate scanning types. The data showed that environmental information was built into the plan. Though the political environment was important, scanning was inadequate. The best location for scanning was not at corporate level and most firms used irregular methods. The Thomas study concluded that effective environmental scanning was permanent and multi level and that 'best practice' was continuous scanning. In 1978 the sample organizations were revisited. Five of the twelve have not changed their practice. The factors which encouraged a continuous model were the attitudes of academics and business media, demonstrated success of the units, the right kind of personnel. Contrary influences were changes in top management, decentralization moves, resource cuts, defining the environment and its significance, the availability of scanning competent personnel, surprise itself, and the availability of alternatives e.g. external forecasts.     

Risk‐Based Approach for Microbiological Food Safety Management in the Dairy Industry: The Case of Listeria monocytogenes in Soft Cheese Made from Pasteurized Milk

According to Codex Alimentarius Commission recommendations, management options applied at the process production level should be based on good hygiene practices, HACCP system, and new risk management metrics such as the food safety objective. To follow this last recommendation, the use of quantitative microbiological risk assessment is an appealing approach to link new risk‐based metrics to management options that may be applied by food operators. Through a specific case study, Listeria monocytogenes in soft cheese made from pasteurized milk, the objective of the present article is to practically show how quantitative risk assessment could be used to direct potential intervention strategies at different food processing steps. Based on many assumptions, the model developed estimates the risk of listeriosis at the moment of consumption taking into account the entire manufacturing process and potential sources of contamination. From pasteurization to consumption, the amplification of a primo‐contamination event of the milk, the fresh cheese or the process environment is simulated, over time, space, and between products, accounting for the impact of management options, such as hygienic operations and sampling plans. A sensitivity analysis of the model will help orientating data to be collected prioritarily for the improvement and the validation of the model. What‐if scenarios were simulated and allowed for the identification of major parameters contributing to the risk of listeriosis and the optimization of preventive and corrective measures.     

Restrictiveness and guidance in support systems

Restrictiveness and guidance have been proposed as methods for improving the performance of users of support systems. In many companies computerized support systems are used in demand forecasting enabling interventions based on management judgment to be applied to statistical forecasts. However, the resulting forecasts are often ‘sub-optimal’ because many judgmental adjustments are made when they are not required. An experiment was used to investigate whether restrictiveness or guidance in a support system leads to more effective use of judgment. Users received statistical forecasts of the demand for products that were subject to promotions. In the restrictiveness mode small judgmental adjustments to these forecasts were prohibited (research indicates that these waste effort and may damage accuracy). In the guidance mode users were advised to make adjustments in promotion periods, but not to adjust in non-promotion periods. A control group of users were not subject to restrictions and received no guidance. The results showed that neither restrictiveness nor guidance led to improvements in accuracy. While restrictiveness reduced unnecessary adjustments, it deterred desirable adjustments and also encouraged over-large adjustments so that accuracy was damaged. Guidance encouraged more desirable system use, but was often ignored. Surprisingly, users indicated it was less acceptable than restrictiveness.     

Application of Geostatistics to Risk Assessment

Geostatistics offers two fundamental contributions to environmental contaminant exposure assessment: (1) a group of methods to quantitatively describe the spatial distribution of a pollutant and (2) the ability to improve estimates of the exposure point concentration by exploiting the geospatial information present in the data. The second contribution is particularly valuable when exposure estimates must be derived from small data sets, which is often the case in environmental risk assessment. This article addresses two topics related to the use of geostatistics in human and ecological risk assessments performed at hazardous waste sites: (1) the importance of assessing model assumptions when using geostatistics and (2) the use of geostatistics to improve estimates of the exposure point concentration (EPC) in the limited data scenario. The latter topic is approached here by comparing design-based estimators that are familiar to environmental risk assessors (e.g., Land's method) with geostatistics, a model-based estimator. In this report, we summarize the basics of spatial weighting of sample data, kriging, and geostatistical simulation. We then explore the two topics identified above in a case study, using soil lead concentration data from a Superfund site (a skeet and trap range). We also describe several areas where research is needed to advance the use of geostatistics in environmental risk assessment.     

Managing Functional Biases in Organizational Forecasts: A Case Study of Consensus Forecasting in Supply Chain Planning

To date, little research has been done on managing the organizational and political dimensions of generating and improving forecasts in corporate settings. We examine the implementation of a supply chain planning process at a consumer electronics company, concentrating on the forecasting approach around which the process revolves. Our analysis focuses on the forecasting process and how it mediates and accommodates the functional biases that can impair the forecast accuracy. We categorize the sources of functional bias into intentional, driven by misalignment of incentives and the disposition of power within the organization, and unintentional, resulting from informational and procedural blind spots. We show that the forecasting process, together with the supporting mechanisms of information exchange and elicitation of assumptions, is capable of managing the potential political conflict and the informational and procedural shortcomings. We also show that the creation of an independent group responsible for managing the forecasting process, an approach that we distinguish from generating forecasts directly, can stabilize the political dimension sufficiently to enable process improvement to be steered. Finally, we find that while a coordination system—the relevant processes, roles and responsibilities, and structure—can be designed to address existing individual and functional biases in the organization, the new coordination system will in turn generate new individual and functional biases. The introduced framework of functional biases (whether those biases are intentional or not), the analysis of the political dimension of the forecasting process, and the idea of a coordination system are new constructs to better understand the interface between operations management and other functions.     

COMPARING FORECAST ACCURACY FOR EXPONENTIAL SMOOTHING MODELS OF EARNINGS-PER-SHARE DATA FOR FINANCIAL DECISION MAKING

This paper relates recent research in predicting accounting earnings per share (EPS) to an experiment comparing the performance of extrapolative forecasting models. The paper points out the usefulness of the results to decision-making processes such as those used in portfolio analysis or financial management. The statistical results of the experiment point to the usefulness of the Holt-Winter (HW) model in predicting EPS for a random sample of firms over a 20-year horizon. For short-term forecasting, the HW model provides relatively accurate forecasts in comparison to other methods used. HW is likely to be a costeffective alternative to more time-consuming and expensive techniques.     

A model for multinational corporate money management

A centralized multiple-period money management model is developed and applied to the case of a simulated US based MNC with subsidiaries in the United Kingdom, Canada and West Germany. Only the first of three period's decisions are intended for implementation in the moving horizon model. The inclusion of two future periods permits current actions to be executed with imperfect but valuable information contained in forecasts of future spot exchange rates produced by moving-horizon least-squares generators. For the set of assumptions invoked it is seen that the centralized model provides significantly better profit results than zero-net exposure approaches.     

CAPACITY AND PRODUCTION DECISIONS IN STOCHASTIC MANUFACTURING SYSTEMS: AN ASYMPTOTIC OPTIMAL HIERARCHICAL APPROACH

We present a new paradigm of hierarchical decision making in production planning and capacity expansion problems under uncertainty. We show that under reasonable assumptions, the strategic level management can base the capacity decision on aggregated information from the shopfloor, and the operational level management, given this decision, can derive a production plan for the system, without too large a loss in optimality when compared to simultaneous determination of optimal capacity and production decisions. The results are obtained via an asymptotic analysis of a manufacturing system with convex costs, constant demand, and with machines subject to random breakdown and repair. The decision variables are purchase time of a new machine at a given fixed cost and production plans before and after the purchase. The objective is to minimize the discounted costs of investment, production, inventories, and backlogs. If the rate of change in machine states such as up and down is assumed to be much larger than the rate of discounting costs, one obtains a simpler limiting problem in which the random capacity is replaced by its mean. We develop methods for constructing asymptotically optimal decisions for the original problem from the optimal decisions for the limiting problem. We obtain error estimates for these constructed decisions.     

Multicriteria Decision Analysis: A Comprehensive Decision Approach for Management of Contaminated Sediments

Contaminated sediments and other sites present a difficult challenge for environmental decisionmakers. They are typically slow to recover or attenuate naturally, may involve multiple regulatory agencies and stakeholder groups, and engender multiple toxicological and ecotoxicological risks. While environmental decision-making strategies over the last several decades have evolved into increasingly more sophisticated, information-intensive, and complex approaches, there remains considerable dissatisfaction among business, industry, and the public with existing management strategies. Consequently, contaminated sediments and materials are the subject of intense technology development, such as beneficial reuse or in situ treatment. However, current decision analysis approaches, such as comparative risk assessment, benefit-cost analysis, and life cycle assessment, do not offer a comprehensive approach for incorporating the varied types of information and multiple stakeholder and public views that must typically be brought to bear when new technologies are under consideration. Alternatively, multicriteria decision analysis (MCDA) offers a scientifically sound decision framework for management of contaminated materials or sites where stakeholder participation is of crucial concern and criteria such as economics, environmental impacts, safety, and risk cannot be easily condensed into simple monetary expressions. This article brings together a multidisciplinary review of existing decision-making approaches at regulatory agencies in the United States and Europe and synthesizes state-of-the-art research in MCDA methods applicable to the assessment of contaminated sediment management technologies. Additionally, it tests an MCDA approach for coupling expert judgment and stakeholder values in a hypothetical contaminated sediments management case study wherein MCDA is used as a tool for testing stakeholder responses to and improving expert assessment of innovative contaminated sediments technologies.