Use and misuse of information in supply chain forecasting of promotion events

Demand forecasting is a critical component of sales and operations planning (S&OP) and is pivotal in supporting inventory and production planning in supply chains. Because of their relative infrequency the effects of sales promotions can be particularly difficult to forecast - yet these are events where production and inventory planners need clear guidance on the probable uplifts in demand. A widely-documented practice involves judgmentally adjusting a baseline statistical forecast on receipt of shared information from sales, marketing and logistics. However, much of this information will either have no predictive value in estimating demand uplift resulting from the promotion or its predictive diagnosticity will be unknown. Theoretical arguments on ‘system neglect’ and ‘base rate discounting’ suggest that the provision of information with no or unknown diagnosticity would lead to the forecasters being distracted from the underlying base-rate uplift with deleterious effects on forecast accuracy. This study investigates this possibility when forecasters made judgmental adjustments to forecasts via a forecasting support system (FSS) in advance of forthcoming sales promotions. In experiments forecasters were provided with the mean rate of sales uplift achieved through promotions (the base rate), and a baseline statistical forecast, together with both quantitative and qualitative information relating to a range of products that were due to be promoted. The results revealed that forecasters were distracted from the base rate, misinterpreting the diverse information available to them, and this led to underestimates of the uplift achieved by the promotions. By extending earlier findings from field observation to a representative experimental setting, these findings have important implications for the quality of inventory decisions, for the design of organizational S&OP processes, and for the implementation of the FSSs that such processes rely on

The Influence of Managerial Forces and Users’ Judgements on Forecasting in International Manufacturers: a Grounded Study

Despite the improvements in mathematical forecasting techniques, the increase in forecasting accuracy is not yet significant. Previous research discussed various forecasting issues and techniques without paying attention to users’ forces and behaviours that influence the construction of forecasts. This research investigates this gap through examining the managerial forces that influence the judgements of different users and constructors of forecasts in international pharmaceutical companies. A qualitative research applying Grounded Theory methodology is used to explore the concealed forces in forecasting processes by interviewing different constructors and users of forecasts in international contexts. Using the Coding Matrices, the research identifies the forces which induce users’ judgements, and consequently lead to conflicts. The research adds value by providing assessment criteria of forecasting management in future research

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

Differences in forecasting approaches between product firms and product-service systems (PSS)

This paper examines the forecasting implications for Product-Service Systems (PSS) applications in manufacturing firms. The approach taken is to identify the scope of operations for PSS applications by identifying all the activities associated with the total cost of ownership (TCO). The paper then develops a revenue model for manufacturing firms providing PSS applications. The revenue model identifies three generic revenue streams that provide the basis for discussion on the differences in forecasting approaches between product firms and Product-Service Systems (PSS) in manufacturing firms. The forecasting approaches are different due to the nature of customer involvement in the service aspect of PSS applications. This necessitates an understanding of the customer service experience and the factors affecting this such as the service profit chain which links profitability, customer loyalty and service value to employee satisfaction, capability and productivity. The forecasting approaches identified raises forecasting challenges for each of the three generic revenue sources. These challenges vary from the difficulty in obtaining the service user’s viewpoint through to difficulties in determining market acceptance of PSS applications

Forecasting of commercial sales with large scale Gaussian Processes

This paper argues that there has not been enough discussion in the field of applications of Gaussian Process for the fast moving consumer goods industry. Yet, this technique can be important as it e.g., can provide automatic feature relevance determination and the posterior mean can unlock insights on the data. Significant challenges are the large size and high dimensionality of commercial data at a point of sale. The study reviews approaches in the Gaussian Processes modeling for large data sets, evaluates their performance on commercial sales and shows value of this type of models as a decision-making tool for management.Comment: 1o pages, 5 figure

Strategic Planning And Forecasting Fundamentals

Individuals and organizations have operated for hundreds of years by planning and forecasting in an intuitive manner. It was not until the 1950s that formal approaches became popular. Since then, such approaches have been used by business, government, and nonprofit organizations. Advocates of formal approaches (for example, Steiner, 1979) claim that an organization can improve its effectiveness if it can forecast its environment, anticipate problems, and develop plans to respond to those problems. However, informal planning and forecasting are expensive activities; this raises questions about their superiority over informal planning and forecasting. Furthermore, critics of the formal approach claim that it introduces rigidity and hampers creativity. These critics include many observers with practical experience (for example, Wrapp, 1967).planning, forecasting, strategic

Considerations on economic forecasting: method developed in the bulletin of EU and US inflation and macroeconomic analysis

This article presents economic forecasting as an activity acquiring full significance when it is involved in a decision-making process. The activity requires a sequence of functions consisting of gathering and organising data, the construction of econometric models and ongoing forecast evaluations to maintain a continuous process involving correction, perfecting and enlarging the data set and the econometric models used, systematically improving forecasting accuracy. With this approach, economic forecasting is an activity based on econometric models and statistical methods, applied economic research with all its general problems. One of these is related to economic data. The widespread belief that if economic information is published, it is valid fo

Inventory drivers in a pharmaceutical supply chain

In recent years, inventory reduction has been a key objective of pharmaceutical companies, especially within cost optimization initiatives. Pharmaceutical supply chains are characterized by volatile and unpredictable demands –especially in emergent markets-, high service levels, and complex, perishable finished-good portfolios, which makes keeping reasonable amounts of stock a true challenge. However, a one-way strategy towards zero-inventory is in reality inapplicable, due to the strategic nature and importance of the products being commercialised. Therefore, pharmaceutical supply chains are in need of new inventory strategies in order to remain competitive. Finished-goods inventory management in the pharmaceutical industry is closely related to the manufacturing systems and supply chain configurations that companies adopt. The factors considered in inventory management policies, however, do not always cover the full supply chain spectrum in which companies operate. This paper works under the pre-assumption that, in fact, there is a complex relationship between the inventory configurations that companies adopt and the factors behind them. The intention of this paper is to understand the factors driving high finished-goods inventory levels in pharmaceutical supply chains and assist supply chain managers in determining which of them can be influenced in order to reduce inventories to an optimal degree. Reasons for reducing inventory levels are found in high inventory holding and scrap related costs; in addition to lost sales for not being able to serve the customers with the adequate shelf life requirements. The thesis conducts a single case study research in a multi-national pharmaceutical company, which is used to examine typical inventory configurations and the factors affecting these configurations. This paper presents a framework that can assist supply chain managers in determining the most important inventory drivers in pharmaceutical supply chains. The findings in this study suggest that while external and downstream supply chain factors are recognized as being critical to pursue inventory optimization initiatives, pharmaceutical companies are oriented towards optimizing production processes and meeting regulatory requirements while still complying with high service levels, being internal factors the ones prevailing when making inventory management decisions. Furthermore, this paper investigates, through predictive modelling techniques, how various intrinsic and extrinsic factors influence the inventory configurations of the case study company. The study shows that inventory configurations are relatively unstable over time, especially in configurations that present high safety stock levels; and that production features and product characteristics are important explanatory factors behind high inventory levels. Regulatory requirements also play an important role in explaining the high strategic inventory levels that pharmaceutical companies hold