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

Genetic and environmental influences on food preferences in adolescence

Background: Food preferences vary substantially among adults and children. Twin studies have established that genes and aspects of the shared family environment both play important roles in shaping children's food preferences. The transition from childhood to adulthood is characterized by large gains in independence, but the relative influences of genes and the environment on food preferences in late adolescence are unknown. Objective: The aim of this study was to quantify the contribution of genetic and environmental influences on food preferences in older adolescents. Design: Participants were 2865 twins aged 18-19 y from the TEDS (Twins Early Development Study), a large population-based cohort of British twins born during 1994-1996. Food preferences were measured by using a self-report questionnaire of 62 individual foods. Food items were categorized into 6 food groups (fruit, vegetables, meat or fish, dairy, starch foods, and snacks) by using factor analysis. Maximum likelihood structural equation modeling established genetic and environmental contributions to variations in preferences for each food group. Results: Genetic factors influenced a significant and substantial proportion of the variation in preference scores of all 6 food groups: vegetables (0.54; 95% CI: 0.47, 0.59), fruit (0.49; 95% CI: 0.43, 0.55), starchy foods (0.32; 95% CI: 0.24, 0.39), meat or fish (0.44; 95% CI: 0.38, 0.51), dairy (0.44; 95% CI: 0.37, 0.50), and snacks (0.43; 95% CI: 0.36, 0.49). Aspects of the environment that are not shared by 2 twins in a family explained all of the remaining variance in food preferences. Conclusions: Food preferences had a moderate genetic basis in late adolescence, in keeping with findings in children. However, by this older age, the influence of the shared family environment had disappeared, and only aspects of the environment unique to each individual twin influenced food preferences. This finding suggests that shared environmental experiences that influence food preferences in childhood may not have effects that persist into adulthood

The Social context of motorcycle riding and the key determinants influencing rider behavior: A qualitative investigation

Objective: Given the increasing popularity of motorcycle riding and heightened risk of injury or death associated with being a rider, this study explored rider behaviour as a determinant of rider safety and, in particular, key beliefs and motivations which influence such behaviour. To enhance the effectiveness of future education and training interventions, it is important to understand riders’ own views about what influences how they ride. Specifically, this study sought to identify key determinants of riders’ behaviour in relation to the social context of riding including social and identity-related influences relating to the group (group norms and group identity) as well as the self (moral/personal norm and self-identity). ----- ----- Method: Qualitative research was undertaken via group discussions with motorcycle riders (n = 41). Results: The findings revealed that those in the group with which one rides represent an important source of social influence. Also, the motorcyclist (group) identity was associated with a range of beliefs, expectations, and behaviours considered to be normative. Exploration of the construct of personal norm revealed that riders were most cognizant of the “wrong things to do” when riding; among those issues raised was the importance of protective clothing (albeit for the protection of others and, in particular, pillion passengers). Finally, self-identity as a motorcyclist appeared to be important to a rider’s self-concept and was likely to influence their on-road behaviour. ----- ----- Conclusion: Overall, the insight provided by the current study may facilitate the development of interventions including rider training as well as public education and mass media messages. The findings suggest that these interventions should incorporate factors associated with the social nature of riding in order to best align it with some of the key beliefs and motivations underpinning riders’ on-road behaviours