A demand-driven approach for a multi-agent system in Supply Chain Management

This paper presents the architecture of a multi-agent decision support system for Supply Chain Management (SCM) which has been designed to compete in the TAC SCM game. The behaviour of the system is demand-driven and the agents plan, predict, and react dynamically to changes in the market. The main strength of the system lies in the ability of the Demand agent to predict customer winning bid prices - the highest prices the agent can offer customers and still obtain their orders. This paper investigates the effect of the ability to predict customer order prices on the overall performance of the system. Four strategies are proposed and compared for predicting such prices. The experimental results reveal which strategies are better and show that there is a correlation between the accuracy of the models' predictions and the overall system performance: the more accurate the prediction of customer order prices, the higher the profit. © 2010 Springer-Verlag Berlin Heidelberg

Demand Forecasting: Evidence-based Methods

We looked at evidence from comparative empirical studies to identify methods that can be useful for predicting demand in various situations and to warn against methods that should not be used. In general, use structured methods and avoid intuition, unstructured meetings, focus groups, and data mining. In situations where there are sufficient data, use quantitative methods including extrapolation, quantitative analogies, rule-based forecasting, and causal methods. Otherwise, use methods that structure judgement including surveys of intentions and expectations, judgmental bootstrapping, structured analogies, and simulated interaction. Managers' domain knowledge should be incorporated into statistical forecasts. Methods for combining forecasts, including Delphi and prediction markets, improve accuracy. We provide guidelines for the effective use of forecasts, including such procedures as scenarios. Few organizations use many of the methods described in this paper. Thus, there are opportunities to improve efficiency by adopting these forecasting practices.Accuracy, expertise, forecasting, judgement, marketing.

Review of Green Jobs

Reviews major studies on the potential for green job creation and summarizes their definitions, strategies, methodologies, key assumptions, and findings. Includes a list of additional reports, articles, and blog posts on green jobs

Program on stimulating operational private sector use of Earth observation satellite data

There are no author-identified significant results in this report

Feature-based intermittent demand forecast combinations:accuracy and inventory implications

Intermittent demand forecasting is a ubiquitous and challenging problem in production systems and supply chain management. In recent years, there has been a growing focus on developing forecasting approaches for intermittent demand from academic and practical perspectives. However, limited attention has been given to forecast combination methods, which have achieved competitive performance in forecasting fast-moving time series. The current study examines the empirical outcomes of some existing forecast combination methods and proposes a generalised feature-based framework for intermittent demand forecasting. The proposed framework has been shown to improve the accuracy of point and quantile forecasts based on two real data sets. Further, some analysis of features, forecasting pools and computational efficiency is also provided. The findings indicate the intelligibility and flexibility of the proposed approach in intermittent demand forecasting and offer insights regarding inventory decisions.<br/