Series of Free Webinars
Pragmatic insight on forecasting during the global pandemic
Speakers: Sarah Darin and Erik Subatis
The abstract: Just as the COVID-19 pandemic has disrupted all our lives, it has had a major impact on virtually all businesses. While some businesses have seen demand surge, others have seen it plummet. While some businesses continue to observe a disruptive influence on demand, others have seen demand stabilize to a new normal. Many companies continue to struggle with stockouts and longer lead times.
This webinar will provide pragmatic insight into how to use Forecast Pro’s methods and techniques to create accurate forecasts during the global pandemic. Drawing upon their extensive expertise in forecasting approaches that have been successfully used during this and other business disruptions, Sarah and Erik will provide an overview of how Forecast Pro can be used to account for the impact of Covid-19 in statistical models and how to efficiently integrate judgmental overrides in large scale forecasting projects. Erik and Sarah will profile several business categories, review the methods that are best suited to each group and demonstrate these approaches in Forecast Pro TRAC using real-world examples
Sarah Darin is a Vice President at Business Forecast Systems. Sarah received her undergraduate degree in Applied Mathematics from Harvard University and has a M.S. in Statistics from the University of Chicago, where she was a PhD candidate (ABD) and served as a Lecturer.
Erik Subatis is Sales Director at Business Forecast Systems. Erik started at BFS as a product specialist and has been Sales Director for the past 8 years. Erik has a B.A in Writing from Ithaca College and expects to earn a Master’s in Software Engineering from Harvard Extension School next year.
To Infinity and Beyond: Forecasting with Dynamic Models
Speaker: Ivan Svetunkov
The abstract: Dynamic model is the model that represents the behaviour of an object over time. The classical examples of dynamic models include Exponential Smoothing, ARIMA, time varying parameters regression and some other models. The three mentioned above are considered to be different classes of models in the academic literature. They have some intersections, but are not necessarily directly connected. Over the last few years, I have been working on theoretical development of state space model that unite these approaches in one unified framework and on implementing this model in an R function. This has resulted in the appearance of the so called “Augmented Dynamic Adaptive Model” – ADAM. In this presentation, I will explain the basics of ADAM and show on several case studies, what problems can be solved with it.
Bio: Ivan is a Lecturer of Marketing Analytics at Lancaster University, UK and a director of Marketing of CMAF. He has PhD in Management Science from Lancaster University. His main area of interest is developing statistical methods for forecasting. He is a creator and maintainer of several forecasting- and analytics-related R packages.
SPC for Autocorrelated Data Using Automated Time Series Forecasting
Speaker: John Noguera
The abstract: Statistical process control for autocorrelated processes have been addressed using the EWMA (Exponentially Weighted Moving Average) one-step-ahead forecast or simple ARIMA (Auto-Regressive Integrated Moving Average) models. The time series model forecasts the motion in the mean and an Individuals control chart is plotted of the residuals to detect assignable causes. Failure to account for the autocorrelation will produce limits that are too narrow resulting in excessive false alarms, or limits that are too wide resulting in misses. The challenge with this approach is that if there is seasonality or negative autocorrelation in the data, the user needs an advanced level of knowledge in forecasting methods to pick the correct model. In this session, we will review simple exponential smoothing / EWMA and then introduce recent developments in time series forecasting that use automatic model selection to accurately pick the time series model that produces a minimum forecast error.
Bio: John Noguera is Co-founder and Chief Technology Officer of SigmaXL, Inc., a leading provider of user-friendly Excel add-ins for Lean Six Sigma tools, statistical & graphical analysis and Monte Carlo simulation. He leads the development of SigmaXL and DiscoverSim with a passion for ease-of-use, practical & powerful features, and statistical accuracy. John is a certified Six Sigma master black belt and was an instructor at Motorola University. He has authored conference papers on Statistical Process Control and Six-Sigma Quality and is a contributing author in the Encyclopedia of Statistics in Quality and Reliability (Wiley).
Improving organisational forecasts and decisions with hierarchical forecasting
Speaker: Nikos Kourentzes
The abstract: Companies have to produce forecasts for hundreds of up to several thousand products to support their operations. As different functions in an organization plan on different horizons and aggregation levels of the market, from individual products to complete markets, they invariably need different forecasts to support their activities. Traditionally, these forecasts are produced independently, considering each item separately from the rest, without capturing cross-effects between products or sharing information between functions effectively. This can lead to multiple different outlooks for the future, misaligned decision making, and eventually either increased costs or lost opportunities. At the crux of this forecasting challenge is the notion of coherence, where forecasts made at the disaggregated level, for example, for individual stock keeping units, must agree with the forecasts at brand, market, or even more aggregate levels. Although this is intuitively clear, both modelling challenges and organizational frictions can make achieving this difficult. In this talk, we will explore how hierarchical forecasting can help organisations produce coherent forecasts, providing a common view of the future from the disaggregate to the aggregate levels in an organization. Building on this, we will look at how innovations in hierarchical forecasting can help us not only achieve better forecast accuracy, but more efficiently use the available information in an organization, and eventually overcome organizational silos by relying on analytics. We will finish the talk with a practical roadmap to incorporating hierarchical forecasts in organisations, and an outlook of the future.
Bio: Nikolaos Kourentzes is a professor at Skövde University in Sweden, and a CMAF veteran.
A presentation by Simon Spavound from Peak
Speaker: Simon Spavound
The abstract: TBA
A presentation by Stephan Kolassa
Speaker: Stephan Kolassa, SAP
The abstract: TBA
Robert Fildes and Mike Gilliland presenting on the topic of Forecast Value Added
Oliver Schaer presenting on the topic of "What's New in Forecasting Software?"
John Boylan presenting on "Current Issues in Supply Chain Forecasting"
Mike Thomas presenting on "Resilient Forecasting with InstantML"
Gunter Fonteyne presenting on "Demand Planning and Forecasting is not only about the software!"
What do we need to know about Forecast Value Added?
Speaker: Robert Fildes
The abstract: Forecast value added is a phrase now very much part of the organisational understanding needed to improve forecasting practice and to select between alternative forecasting methods. One of its more important applications is to understand the improved accuracy achieved by the common practice in demand planning of adjusting a statistical forecasting method based on information gathered through the sales and operations planning process. Various past studies have analysed the results of this adjustment process. This presentation considers a range of data sources to provide insight into the circumstance where gains have been achieved. We identify the key questions facing any organisation where FVA of expert judgmental adjustment is part of the process. But be warned, the conclusions will not be unequivocal; FVA is a complex area and there remains a lot to be learnt and much to be done if organisations are going to develop effective demand planning processes.
The introduction to this talk was given by Mike Gilliland from SAS.
Bio: Robert Fildes is the Director of Centre for Marketing Analytics and Forecasting, Lancaster University Management School
Demand Planning and Forecasting is not only about the software!
Speaker: Gunter Fonteyne
The abstract: The forecasting process plays an important role in demand planning and marketing decisions. Although the process has evolved from Silo Thinking to Integrated Thinking, often they are not properly implemented. What about participation of senior management in the process, what about responsibility and accountability of the main stakeholders, what about the evaluation of your demand planning process, what about the process for continuous improvement?.. Forecasting is neither just a science, nor just an art, but a combination of both. Based on our experiences in Xeleos Consulting and Optimact, Demand Planning and Forecasting brings benefits to a company when the process is setup right, and when it is supported by an adequate strategy, organisational structure, and technological infrastructure.
Bio: Gunter is partner of Xeleos Consulting and Optimact. Gunter’s main field of interest is Supply Chain Management: Supply Chain Strategy, Supply Chain Planning and Business Process Management. He has been working on several projects to design and improve supply chain operations and supporting applications. Gunter has overall management and project/interim management skills combined with a strategic supply chain view. This view can be translated afterwards into a pragmatic approach to implement the solutions including the link to technology, operations, infrastructure and change management.
Resilient Forecasting with InstantML
Speaker: Mike Thomas
The abstract: When modelling time-series data there is often a tendency to focus on developing the ‘best’ model for a given situation without considering the inherent fragility of the model itself. In practice, significant forecasting errors typically come from structural changes or changes in the availability of data at the point of forecast. What if a competitors store opens up next door, or your model relied on a data point from t-x but t-x data does not arrive in time? Extended periods of sub-optimal or incomplete forecasts can follow before the next model is built, often with considerable investment in time and energy but the revised models are no less fragile.
Model accuracy is important, so for a forecasting system to be resilient and manageable at scale the models must capture new information from the data as soon as possible, whilst also providing the transparency and granularity required to enable users to understand the specific impact of these changes in real time.
This webinar will explain why Tangent Works focussed on automating the creation of time-series machine learning models and the implications that their InstantML technology has in enabling resilient forecasting philosophies.
Bio: Mike is a Director of Tangent Works UK. The Tangent Information Modeller by Tangent Works builds machine learning models with a single pass of the data in just a few moments. Since studying Physics at The University of Manchester, Mike has worked with various global technology companies before returning to The Alliance Manchester Business School to complete his MBA.
What's New in Forecasting Software
Speaker: Oliver Schaer
The abstract: Companies looking to obtain a new software solution often feel like in a candy store: There are lots of options, but the forecasting needs may well be quite specific, and the budget is typically constrained. In addition to new startups that supply very specific machine learning (ML) and artificial intelligence (AI) algorithms, established business intelligence software companies are adding more and more predictive time-series analysis tools to their product lines increasing the number of software solutions available. As a result, it is difficult to choose the most appropriate product to meet prospective users’ needs. This talk highlights insights from the latest biennial ORMS-Today forecasting software survey, led by the Centre for Marketing Analytics and Forecasting, on recent developments and future trends in this industry sector – supporting practitioners making their forecasting software purchase decision.
Bio: Oliver Schaer is a post-doctoral researcher at the University of Virginia Darden School of Business. His research interests are in the application of predictive analytics for the areas of business operations and marketing. More specifically, he is currently working on new product forecasting and incorporating user-generated content into demand forecasting models to improve decision-making. He is a visiting research fellow of the Centre for Marketing Analytics and Forecasting at Lancaster University.
Current Issues in Supply Chain Forecasting
Speaker: John E. Boylan
Abstract: Forecasting demand has, if anything, become even more challenging over recent years, as product lives have shortened, distribution channels have become more varied, and inventory reviews have become more frequent. This webinar will address a number of key issues that should be addressed in today’s forecasting and inventory management systems. We shall look at data requirements and issues arising from inventory record inaccuracy. Then, we move on to performance metrics and ensuring that you have the right forecast accuracy measures in place for your organization. Advances in forecasting approaches will be reviewed, focusing particularly on methods that are suitable for short data histories. We also discuss appropriate responses to sudden shocks in demand histories, such as the shift in demand at the start of the Covid-19 pandemic. These shocks call for the effective combination of judgement and statistical evidence.
Bio: John is Professor of Business Analytics at Lancaster University. His research interests are focused on supply-chain forecasting and include intermittence, seasonality and information sharing. He is an Editor-in-Chief of the Journal of the Operational Research Society and President of the International Society for Inventory Research.
Demand planners and forecasters
Lecturers and students
Forecasting and planning support systems developers
Learn something new
Find out the recent developments in forecasting
Meet practitioners and academics working in the area
Find answers to your forecasting and demand planning related questions
Our Centre, founded in 1990, continues to lead the field in applied forecasting and marketing analytics. Our research, executive support and training helps practitioners and academics working in the retail, manufacturing, telecommunications and software sectors. We develop new methods to tackle a range of problems facing those using predictive analytics, including demand planning and marketing modelling. Our work results in substantial cost reductions and service level improvements for a range of private and public organisations.
Specifically, we get involved with projects for multinational companies and we advise companies on effective procedures for forecasting and inventory management. We also recommend and design the best software solutions for the operational side of businesses. In addition to this we design and deliver courses to meet specific group needs, using interactive material that gives attendees hands-on experience in producing effective forecasts. We are the only centre in the UK to offer a Certificate of Forecasting issued by the International Institute of Forecasters (IIF).
Throughout the year we host a range of events, including courses and guest speakers, it is a great way to tap into our expertise. And our list of publications demonstrates the scope of our work.
Yes, any participant will have a chance to type in their questions, which will then be asked after the presentation.