Demand uncertainty and lot sizing in manufacturing systems: the effects of forecasting errors and mis-specification

This paper proposes a methodology for examining the effect of demand uncertainty and forecast error on lot sizing methods, unit costs and customer service levels in MRP type manufacturing systems. A number of cost structures were considered which depend on the expected time between orders. A simple two-level MRP system where the product is manufactured for stock was then simulated. Stochastic demand for the final product was generated by two commonly occurring processes and with different variances. Various lot sizing rules were then used to determine the amount of product made and the amount of materials bought in. The results confirm earlier research that the behaviour of lot sizing rules is quite different when there is uncertainty in demand compared to the situation of perfect foresight of demand. The best lot sizing rules for the deterministic situation are the worst whenever there is uncertainty in demand. In addition the choice of lot sizing rule between ‘good’ rules such as the EOQ turns out to be relatively less important in reducing unit cost compared to improving forecasting accuracy whatever the cost structure. The effect of demand uncertainty on unit cost for a given service level increases exponentially as the uncertainty in the demand data increases. The paper also shows how the value of improved forecasting can be analysed by examining the effects of different sizes of forecast error in addition to demand uncertainty. In those manufacturing problems with high forecast error variance, improved forecast accuracy should lead to substantial percentage improvements in unit costs

The state of macroeconomic forecasting

Macroeconomic forecasts are used extensively in industry and government The historical accuracy of US and UK forecasts are examined in the light of different approaches to evaluating macro forecasts. Issues discussed include the comparative accuracy of macroeconometric models compared to their time series alternatives, whether the forecasting record has improved over time, the rationality of macroeconomic forecasts and how a forecasting service should be chosen. The role of judgement in producing the forecasts is also considered where the evidence unequivocally favors such interventions. Finally the use of macroeconomic forecasts and their effectiveness is discussed. The conclusion drawn is that researchers have paid too little attention to the issue of improving the forecasting accuracy record. Areas where improvements would be particularly valuable are highlighted.

Household technology acceptance heterogeneity in computer adoption

Technology policy analysis and implementation relies on knowledge and understanding of the "adoption gap" in information technologies among different groups of consumers. Factors that explain the residential "digital divide" also need to be identified and quantified. Through the application of survey data we provide an enhanced understanding of the key factors involved in the choice of residential computer adoption. These choices are analysed using a discrete choice model that reveals that sociodemographic factors strongly influence the adoption of the residential computer. Moreover, we apply the basic findings of the Technology Adoption Model (TAM) into the discrete choice framework heteroscedastically to deepen our understanding of why some households choose not to have computers; above and beyond what may be explained by socio-demography alone. Generally, we find that computer adoption is sensitive to household digital division measures and that the model fit improves with the heteroscedastic addition of the TAM factors. These findings are important for market planners and policymakers who wish to understand and quantify the impact of these factors on the digital divide across different household types, as defined by the TAM model

Effective forecasting for supply-chain planning: an empirical evaluation and strategies for improvement

Demand forecasting is a crucial aspect of the planning process in supply-chain companies. The most common approach to forecasting demand in these companies involves the use of a simple univariate statistical method to produce a forecast and the subsequent judgmental adjustment of this by the company's demand planners to take into account market intelligence relating to any exceptional circumstances expected over the planning horizon. Based on four company case studies, which included collecting more than 12,000 forecasts and outcomes, this paper examines: i) the extent to which the judgmental adjustments led to improvements in accuracy, ii) the extent to which the adjustments were biased and inefficient, iii) the circumstances where adjustments were detrimental or beneficial, and iv) methods that could lead to greater levels of accuracy. It was found that the judgmentally adjusted forecasts were both biased and inefficient. In particular, market intelligence that was expected to have a positive impact on demand was used far less effectively than intelligence suggesting a negative impact. The paper goes on to propose a set of improvements that could be applied to the forecasting processes in the companies and to the forecasting software that is used in these processes

Non-Linear Identification of Judgmental Forecasts Effects at SKU-Level

Prediction of demand is a key component within supply chain management. Im- proved accuracy in forecasts affects directly all levels of the supply chain, reduc- ing stock costs and increasing customer satisfaction. In many application areas, demand prediction relies on statistical software which provides an initial forecast subsequently modified by the expert’s judgment. This paper outlines a new method- ology based on State Dependent Parameter (SDP) estimation techniques to identify the non-linear behaviour of such managerial adjustments. This non-parametric SDP estimate is used as a guideline to propose a non-linear model that corrects the bias introduced by the managerial adjustments. One-step-ahead forecasts of SKU sales sampled monthly from a manufacturing company are utilized to test the proposed methodology. The results indicate that adjustments introduce a non-linear pattern undermining accuracy. This understanding can be used to enhance the design of the Forecasting Support System in order to help forecasters towards more efficient judgmental adjustments

Internet Usage and Online Shopping Experience as Predictors of Consumers’per Preferences to Shop Online Across Product Categories

This paper studies how adoption and usage behaviour of the Internet and online shopping respectively influence the preference to use electronic commerce to purchase different types of products. We empirically model the preference for electronic commerce when consumers have to buy different types of products and thus face different types of risks (Cox and Rich, 1964). Unlike previous research, we find that consumers who have previously shopped online display stronger preferences to buy products on the Internet irrespective of the perceived level of product specific risks of online shopping. This paper provides an interesting and novel insight into how both adoption and usage of electronic commerce impact on the attitude and risk perception of buying less predictable (more risky) products on the Internet

Selective Use of Pericardial Window and Drainage as Sole Treatment for Hemopericardium from Penetrating Chest Trauma

Background Penetrating cardiac injuries (PCIs) are highly lethal, and a sternotomy is considered mandatory for suspected PCI. Recent literature suggests pericardial window (PCW) may be sufficient for superficial cardiac injuries to drain hemopericardium and assess for continued bleeding and instability. This study objective is to review patients with PCI managed with sternotomy and PCW and compare outcomes. Methods All patients with penetrating chest trauma from 2000 to 2016 requiring PCW or sternotomy were reviewed. Data were collected for patients who had PCW for hemopericardium managed with only pericardial drain, or underwent sternotomy for cardiac injuries grade 1–3 according to the American Association for the Surgery of Trauma (AAST) Cardiac Organ Injury Scale (OIS). The PCW+drain group was compared with the Sternotomy group using Fisher’s exact and Wilcoxon rank-sum test with P\u3c0.05 considered statistically significant. Results Sternotomy was performed in 57 patients for suspected PCI, including 7 with AAST OIS grade 1–3 injuries (Sternotomy group). Four patients had pericardial injuries, three had partial thickness cardiac injuries, two of which were suture-repaired. Average blood drained was 285mL (100–500 mL). PCW was performed in 37 patients, and 21 had hemopericardium; 16 patients proceeded to sternotomy and 5 were treated with pericardial drainage (PCW+drain group). All PCW+drain patients had suction evacuation of hemopericardium, pericardial lavage, and verified bleeding cessation, followed by pericardial drain placement and admission to intensive care unit (ICU). Average blood drained was 240mL (40–600 mL), and pericardial drains were removed on postoperative day 3.6 (2–5). There was no significant difference in demographics, injury mechanism, Revised Trauma Score exploratory laparotomies, hospital or ICU length of stay, or ventilator days. No in-hospital mortality occurred in either group. Conclusions Hemodynamically stable patients with penetrating chest trauma and hemopericardium may be safely managed with PCW, lavage and drainage with documented cessation of bleeding, and postoperative ICU monitoring. Level of evidence Therapeutic study, level IV

A New Bootstrapped Hybrid Artificial Neural Network Approach for Time Series Forecasting

In this study, a new bootstrapped hybrid artificial neural network is proposed for forecasting. This new neural network provides input significance, linearity and nonlinearity hypothesis tests in a unique network structure via a residual bootstrap approach. The network has three parts: linear, non-linear and a combination with associated weights and biases. These weights are used to test the input significance, linearity and nonlinearity hypotheses with this new method providing empirical distributions for forecasts and weights. The proposed method employs a bagging approach to obtain forecasts. It is then applied to real-time series including the M4 Competition data set and stock exchange time series where its performance is compared with appropriate benchmark methods including other popular neural networks. The proposed method results are less affected than other neural networks by initial random weights, which means that the results of the proposed method are more stable and precise. The new method provides improvements in forecasting accuracy over the established benchmarks

Forecasting retailer product sales in the presence of structural change

Grocery retailers need accurate sales forecasts at the Stock Keeping Unit (SKU) level to effectively manage their inventory. Previous studies have proposed forecasting methods which incorporate the effect of various marketing activities including prices and promotions. However, their methods have overlooked that the effects of the marketing activities on product sales may change over time. Therefore, these methods may be subject to the structural change problem and generate biased and less accurate forecasts. In this study, we propose more effective methods to forecast retailer product sales which take into account the problem of structural change. Based on data from a well-known US retailer, we show that our methods outperform conventional forecasting methods that ignore the possibility of such changes