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

Judgement and supply chain dynamics

Forecasting demand at the individual stock-keeping-unit (SKU) level often necessitates the use of statistical methods, such as exponential smoothing. In some organizations, however, statistical forecasts will be subject to judgemental adjustments by managers. Although a number of empirical and ‘laboratory’ studies have been performed in this area, no formal OR modelling has been conducted to offer insights into the impact such adjustments may have on supply chain performance and the potential development of mitigation mechanisms. This is because of the associated dynamic complexity and the situation-specific nature of the problem at hand. In conjunction with appropriate stock control rules, demand forecasts help decide how much to order. It is a common practice that replenishment orders may also be subject to judgemental intervention, adding further to the dynamic system complexity and interdependence. The system dynamics (SD) modelling method can help advance knowledge in this area, where mathematical modelling cannot accommodate the associated complexity. This study, which constitutes part of a UK government funded (EPSRC) project, uses SD models to evaluate the effects of forecasting and ordering adjustments for a wide set of scenarios involving: three different inventory policies; seven different (combinations of) points of intervention; and four different (combinations of) types of judgmental intervention (optimistic and pessimistic). The results enable insights to be gained into the performance of the entire supply chain. An agenda for further research concludes the paper

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

Developing a Web-based tourism demand forecasting system

2008-2009 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Expert judgement in the Processes of Commercial Property Market Forecasting

In this paper we investigate the role of judgement in the formation of forecasts in commercial real estate markets. Based on interview surveys with the majority of forecast producers, we find that real estate forecasters are using a range of inputs and data sets to form models to predict an array of variables for a range of locations. The findings suggest that forecasts need to be acceptable to their users (and purchasers) and consequently forecasters generally have incentives to avoid presenting contentious or conspicuous forecasts. Where extreme forecasts are generated by a model, forecasters often engage in ‘self-censorship’ or are ‘censored’ following in-house consultation. It is concluded that the forecasting process is more complex than merely carrying out econometric modelling and that the impact of the influences within this process vary considerably across different organizational contexts.

The Production and Consumption of Commercial Real Estate Market Forecasts

Whilst the vast majority of the research on property market forecasting has concentrated on statistical methods of forecasting future rents, this report investigates the process of property market forecast production with particular reference to the level and effect of judgemental intervention in this process. Expectations of future investment performance at the levels of individual asset, sector, region, country and asset class are crucial to stock selection and tactical and strategic asset allocation decisions.  Given their centrality to investment performance, we focus on the process by which forecasts of rents and yields are generated and expectations formed.  A review of the wider literature on forecasting suggests that there are strong grounds to expect that forecast outcomes are not the result of purely mechanical calculations.Real Estate, Forecast, Real Estate Markets, Commercial Real Estate

Algorithm-Human-Algorithm: A New Classification Approach to Integrating Judgemental Adjustments

Modern-day firms face the predicament of blending the comparative advantages of their two core resources: machines and humans. When forecasting demand (e.g., for a product), extant literature documents that always permitting (or prohibiting) human revision of a machine forecast is beneficial if the humans\u27 private information role is larger (or smaller) than that of machine-accessible public information. We propose and design a complementary framework that shifts the focus to the regulation of each human revision; and, in doing so, adjusts for human vulnerability to systematic biases. To test our framework, we collaborate with a European retailer to compile a large dataset (~1.1 mn transactions) on machine-led demand forecasts and human revisions. In an out-of-sample analysis, our revision-level regulation approach picks the best of available forecasts in 14% more instances, compared to an always-permit or prohibit strategy at the product-store level. Our approach is theory-driven and easy to implement for practitioners