Our research into best practices in forecasting is well under way, and is already providing some new insights in Supply Chain Forecasting, Demand Planning and Forecasting practices in Vendor Managed Inventory (VMI) and Collaborative Planning Forecasting and Replenishment (CPFR).
However, we are still seeking additional participants and further responses for the survey.
By taking part you will receive a copy of the findings, with valuable insights into:
· Forecasting best practices in Supply Chain Forecasting (overall and by industry)
· Benchmarks in Forecasting accuracy (overall and by industry)
The survey will take between 20-30 minutes depending on your forecasting setup; it is possible to pause and complete it at a later point. To start taking the survey visit:
The research is not for commercial purposes and all responses will be treated in the strictest confidence. Further information about the study is given below. If you have any additional questions please do not hesitate to contact me at: firstname.lastname@example.org
PhD Candidate in Management Science
About the organisers
The study forms part of my PhD at Lancaster University under Dr Sven Crone and Professor Robert Fildes. The Lancaster Centre for Forecasting hosts a number of different research interests, past and current research grants, applied research projects with companies and PhD students in the area of Forecasting for Supply Chain Management. For a detailed description visit our website: www.lums.lancs.ac.uk/forecasting
About the research
The increased volatility and competitiveness of today’s market has led firms to engage in practices such as VMI, CPFR, S&OP and information-sharing in order to improve forecast accuracy. However, knowing how and when to collaborate with downstream partners remains a challenge. We aim to address some of the open questions in this area, initially through a web-based survey of forecasters & demand planners.
To be more specific, we are focusing on the following research questions: (a) What types of information are available to forecasters? (b) How are they using this information, if at all, in their forecasting process? Through statistical methods and/or judgement?, and (c) How do forecasters cope with different data conditions and forecasting requirements of different customers?
In a later phase, we look to test alternative methods of using downstream data in the statistical forecasting process and compare accuracy against established benchmark methods. Further details about the study are available on request from Matt Weller (see above for contact details).