Professor Disney’s research interests involve the application of control theory and statistical techniques to operations management and supply chain scenarios to investigate their dynamic, stochastic, and economic performance. Stephen has a particular interest in the bullwhip effect, forecasting, and inventory management. Stephen has advised several of the world’s largest corporations on the bullwhip effect and his research has influenced the material flow of at least 1 in every 7 pounds of UK retail sales. He has worked with many companies in the UK, US, and Europe and on supply chains that operate globally.
Professor Disney currently a Professor of Operations Management at the University of Exeter Business School. He is the Head of the Science, Innovation, Technology and Entrepreneurship Department within the Business School. Previously he worked at Cardiff Business School. He has extensive experience of teaching in-class, on-line, and on-site to Undergraduate, Postgraduate, and Executive audiences. He recently spent 12 months on Research Leave at the University of California, Los Angeles. Professor Disney has previously held visiting positions at the Chinese University of Hong Kong and at Boston University.
PhD entitled "The Production and Inventory Control Problem in Vendor Managed Inventory Supply Chains", 2001
MSc in Systems Engineering, 1995
BSc in Manufacturing Systems and Manufacturing Management, 1994
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This visual workbook shows the practical lean manager how to solve the bullwhip problem.
I recently finished a 2-year project that improved the forcasting and production planning processes within Yeo Valley, one of the largest organic dairy producers in the UK. Atfer dynamically mapping the values streams, we changed and improved the forecasting and production planning process for over 400 products across two whole factories. The project led to a significant increasing in customer service levels, a reduction of finished goods inventories, fresher product being sold to the consumer, and a much more stable production plan with fewer urgent production orders. You can read more about this project here.
I am currently teaching the following Undergraduate courses at the University of Exeter:
In the past I have taught the following courses at Cardiff University:
I have also taught the following courses at Boston University, USA:
I also deliver exec-ed training, including:
I have recently developed a 1-2 day course for Exec-Ed delivery entitled “Setting the cadence of your pacemaker”. The course shows you how to use dynamic value stream mapping to solve the bullwhip problem. Topics covered include: replenishment strategy selection, forecasting, designing replenishment decisions, detailed scheduling, and supplier MRP. If you are interested in this type of training please contact me.
Click here to be re-directed to a web-based Shiny App designed to support a paper on SpeedFactories that I am currently writing.
Click here to be re-directed to a web-based Shiny App that is able to make decisions under uncertainty based on the criteria Maximax, Maximin, Laplace, Minimax Regret, Hurwitz and the Maximum likelyhood criteria. This Shiny App is also able to make risky decisions based on the Expected Monetray Value and Expected Opportunity Loss, as well to calculate the Expected Profit from a Perfect Predictor and the Expected Value of Perfect Information.
Click here to be re-directed to a web-based Shiny App that explores the Economic Order Quantity decision. This simple Shiny App was developed as part of a Shiny Hack Day I ran on May 15, 2019 in Cardiff Business School.
Click here to be re-directed to a web-based Shiny App that explores the Standard Normal Distribution. This simple Shiny App is useful for determining safety stocks, fill rates and the like in many operations management settings.
Many of the mathematical functions required in operations management (OM) scenarios are not available in Microsoft Excel. To address this issue, I have created an Add-in that adds some OM functionality to Excel. The Add-in can be downloaded here.
To install this Add-in:
When you have done this, the following functions should now be available in Excel:
Gives the inverse of the standard normal distribution function evaluated at x. This is a useful function when determining safety stock levels when net stock levels are normally distributed. This function has also been incorporated into my Shiny App for the standard normal distribution that is available here.
Gives the real solutions to the Lambert W function on the principle branch (when mode = 1) and the alternative branch (when mode = -1), evaluated at z. This function is useful for identifying stability boundaries (see Warburton et al. (2004)) and bullwhip expressions (see Warburton and Disney (2007)) in continuous time systems and also for identifying the Net Present Value of the cash flows in the EOQ problem (see Disney and Warburton (2013).
Calculates a critical bullwhip condition for ARMA(p,q) demand in the Order-Up-To policy with a lead-time of k periods. If CBk is positive bullwhip is generated. If CBk is negative bullwhip is avoided. This criteria even works with non-stationary demand. The maximum allowable k is set to 100 as otherwise it slows up the computer. More information can be found in Gaalman et al., (2018).
Calculates the Damped Trend forecast with a smoothing constant for the level of alpha, a smoothing constant for the trend of beta, a damping parameter of phi. More information can be found in Li and Disney, (2015).
Note: When phi = 1, Holts forecasts are generated. When beta = 0, exponential smoothing forecasts are generated.
Calculates the fill rate when demand and inventory is normally distributed. Both demand and inventory can be correlated, cross-correlated, and possibly negative in some periods which makes this calculation much more robust than many other other fillrate formulas. More information can be found in Disney et al., (2015).
Calculates the safety stock required to achive a target fill rate when demand and inventory is normally distributed. Both demand and inventory can be correlated, cross-correlated, and possibly negative in some periods which makes this calculation much more robust than many other other fill rate formulas. More information can be found in Disney et al., (2015).
I am keen to add more functionality to my Operations Analysis Add-in. If you have an idea of something useful to add, please contact me.