CMAF Friday Forecasting Talks

Series of Free Webinars

Upcoming webinars
14th May, 2-3pm (GMT+1)

Forecasting Software Trends for the Next Decade

Speaker: Michele Trovero

The abstract: A well-designed forecasting support system, in the form of a commercial package or based on open source, is essential to enable analysts to generate reliable and robust forecasts in a production environment. In recent years, the availability of options for forecasting support systems has been steadily growing and expanding. There are two main drivers of this growth: the evolution of technology common to much of the software industry, and the evolution of algorithms and methodologies specific to forecasting. This talk explores these two elements of change and tries to extrapolate some common trends that will drive innovation and growth in forecasting software for the next few years.

Bio: Michele Trovero is currently leading the Forecasting R&D group at SAS. He has over 17 years of experience in the development of commercial forecasting software. His interests cover a wide variety of topics related to forecasting, time series analysis, and machine learning. He has presented at several international conferences on these topics. He received a PhD in statistics from the University of North Carolina in Chapel Hill and has a degree in economics from Bocconi University in Milan, Italy. When he’s not thinking about forecasting or driving his children to activities, you can find Michele running or riding his bikes.

28th May, 2-3pm (GMT+1)

How AIML Forecasting is not Changing Industry Demand Planning

Speaker: Sven Crone

The abstract: Artificial Intelligence and Machine Learning (AI/ML) reign high on the Gartner hype cycle, promising new business models, and capturing the imagination of many industry executives well outside the digital industries of Facebook, Google and Uber. However, despite dozens of Proof-of-Concept studies in demand planning, AI/ML is struggling to prove significant increases in planning accuracy. One limiting factor, we will argue, is that the underlying data for demand planning is fundamentally limited. After a very brief introduction into AI/ML we will explore the fundamental difference in data between image/voice/video recognition and typical pharmaceutical demand planning, where we data size and data labels are often very sparse. As this limits the benefit of the AI/ML algorithms of Google & co, we will show a case study where we have enhanced limited datasets in demand panning and thus showed promise in using AI/ML for Janssen (a Johnson & Johnson company). We will close by showing more favourable data conditions and successful AIML forecasts in container shipping for Hapag-Lloyd, forecasting consumer demand for FMCG manufacturer Beiersdorf, and beverage forecasting for beer Manufacturer Anheuser-Busch InBev, which have been forecasting with AI for up to 10 years.

Bio: Sven is an Assistant Professor in Management Science at Lancaster University, UK, where his 15+ years in research on forecasting algorithms and applications of AI/ML led to 80+ publications and ranks him in the top 30 in Europe (citations by Google Scholar). Sven regularly provides in-house training courses on forecasting and analytics for companies, and is a regular conference chair and keynote speaker at international conferences in forecasting, analytics, and S&OP/IBP. He is also CEO and founder of iqast.de, a university spin-off pioneering AIML algorithms in software that work as an add-on to the dated forecasting engines of legacy systems including SAP APO/SCM, IBP, ER

Our webinars will be useful for

Practitioners

Demand planners and forecasters

Academics

Lecturers and students

Software vendors

Forecasting and planning support systems developers

What you would get

Learn something new

Find out the recent developments in forecasting

Socialise

Meet practitioners and academics working in the area

Ask questions

Find answers to your forecasting and demand planning related questions

Previous webinars
30th Apr 2021

Generation of Time Series Scenarios: How to Do It and Make it Pay

Speaker: Thomas Willemain

The abstract: Time series scenarios are artificial data series intended as inputs to decision processes. Those processes may involve forecasting, system design, or operator training. We outline criteria for evaluating scenario generators, explain how to create scenarios using nonparametric bootstrapping, and show how to exploit scenarios at the intersection of forecasting and inventory optimization.

Bio: Dr. Tom Willemain is Professor Emeritus of Industrial and Systems Engineering at Rensselaer Polytechnic Institute, Cofounder and Senior Vice President of Smart Software, Inc., and former intelligence officer. He holds degrees from Princeton University and Massachusetts Institute of Technology.

16th Apr 2021

Forecasting Through Adaptive Combinations of Large Model Pools

Speaker: Nicos Pavlidis

The abstract: Model combination (averaging) methods have gained substantial attention in the time series forecasting literature due to their success in numerous and diverse applications. In this work we focus on methods capable of accommodating changes over time in both model parameters as well as in the optimal model combination. This problem is particularly relevant as a growing empirical literature provides strong evidence in favour of structural change in numerous application areas. Ignoring the time-varying nature of real-world applications can have detrimental consequences for inference and forecasting. The approach we discuss relies on Dynamic Model Averaging (DMA): an adaptive methodology that is computationally efficient and allows the user to consider a very large pool of candidate models. We discuss modifications of the DMA methodology and in particular the consideration of methods from the machine learning literature for the model averaging step. We apply our proposed approach on time-series from house market prices and compare its performance to state-of-the-art methods for point and density forecast.

Bio: Nicos Pavlidis has studied economics in King's College, Cambridge University. He has an M.Sc. and Ph.D. in mathematics and computer science from the University of Patras, Greece, and has been a research associate at Imperial College London. Since 2010 he has been with the Department of Management Science, Lancaster University. His main research interests are classification and clustering in dynamic and information rich environments, as well as dimensionality reduction.

26th Mar 2021

Forecast Accuracy: Fanciful Aspiration or False Advertising?

Speaker: Stephan Kolassa

The abstract: How do we know our forecasts are good, or at least better than some other forecast? There are many forecast accuracy measures in use, some of which come with unexpected side effects. We will discuss accuracy measures for point forecasts, both for central tendencies and for quantiles as necessary for safety stock and capacity planning, and also touch on external industry forecast accuracy benchmarks and tests for statistical significance. If the participants find this interesting, we will organise a follow-up presentation, which will treat interval and density forecast evaluation as well as (as a bonus) the evaluation of predictive classifications. So, please, leave your feedback after the event.

Bio: Stephan Kolassa is a Data Science Expert at SAP Switzerland. His responsibilities include the algorithmic, statistical and forecasting aspects of SAP’s retail platform CARAB, from user research across prototyping to training. He also does some academic research on the side, serving as an Honorary Researcher at the Centre for Marketing Analytics and Forecasting at Lancaster University Management School, and as Associate Editor for Foresight: The International Journal of Applied Forecasting.

12th Mar 2021

Forecasting Retail Demand (including earthquakes and pandemics)

Speaker: Stephan Kolassa, SAP

Abstract: One point where we are all confronted with the need for good forecasts is when we go (or nowadays, click) shopping. Retailers need good forecasts to ensure their shelves are well stocked, but not overstocked. (They need forecasts for other uses, as well. More on this in the presentation.) We will go into key challenges in retail demand forecasting, how to address these, what we can learn from the recent M5 competition that used actual retail data, and why UK retailer My Local went out of business.

Bio: Stephan Kolassa is a Data Science Expert at SAP Switzerland. His responsibilities include the algorithmic, statistical and forecasting aspects of SAP’s retail platform CARAB, from user research across prototyping to training. He also does some academic research on the side, serving as an Honorary Researcher at the Centre for Marketing Analytics and Forecasting at Lancaster University Management School, and as Associate Editor for Foresight: The International Journal of Applied Forecasting.

26th Feb 2021

Accuracy, Explainability and Trust: the trinity of business forecasting?

Speaker: Simon Spavound

The abstract: Many businesses rely extensively on their forecasting systems in order to make decisions which concern the future. Given the importance of correct decisions to businesses, a great deal of emphasis is placed on the statistical accuracy of these systems, with little thought in the wider implementation challenges and overall utility within business processes. These issues can create larger barriers to implementation success and crucially user acceptance. With the advent of Machine Learning based forecasting approaches, and business decisions being made off the back of them, trust and the use of these systems by individuals and teams will become ever more important. This talk will discuss these issues, the wider issues of implementation and suggest some solutions to the problems.

Bio: Simon is Data Science Team Lead in Peak. He has PhD in Economics from Lancaster University and is an expert in forecasting in supply chain.

12th Feb 2021

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.

29th Jan 2021

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).

15th Jan 2021

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.

11th Dec 2020

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

Bios:

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.

27th Nov 2020

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

13th Nov 2020

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.

30th Oct 2020

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.

mike.thomas@tangent.works

www.tangent.works

16th Oct 2020

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.

2nd Oct 2020

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.

Video recordings of last 6 webinars

Thomas Willemain on "Generation of Time Series Scenarios: How to Do It and Make it Pay"

Nicos Pavlidis, "Forecasting Through Adaptive Combinations of Large Model Pools"

Stephan Kolassa, "Forecast Accuracy: Fanciful Aspiration or False Advertising?"

Stephan Kolassa, "Forecasting Retail Demand (including earthquakes and pandemics)"

Simon Spavound, "Accuracy, Explainability and Trust: the trinity of business forecasting?"

Nikos Kourentzes, "Improving organisational forecasts and decisions with hierarchical forecasting"

About us
Centre for Marketing Analytics and Forecasting, Lancaster University
Marketing Analytics
Supply Chain Forecasting
S&OP Process
Machine Learning
Software Development
Inventory Management

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.

Questions and Answers

Yes, we will record each webinar and make them available afterwards on our Youtube Channel.

Yes, any participant will have a chance to type in their questions, which will then be asked after the presentation.

We use MS Teams Live, so in order to attend you would need to either install it on a device of your choosing or use the web-based version.

So, do you want to attend?

Then press the button and select webinars that you want to attend

cmaf@lancaster.ac.uk
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