The waves of disruption just keep coming. Retailers have learned to make rapid pivots to catch and surf the big ones – but how are they doing with AI? NRF this year explored what it means to ride the AI wave in retail. We invited experts from Microsoft, Sitoo and
Tropical Storm Ana, which formed in May this year, officially made 2021 the seventh consecutive year that a storm formed before the season's designated start. Since then we have seen a number of storms, representative of an increase of severe weather over the past few years, especially as we remember
Hippocrates once said, “Let food be thy medicine.” For food vendors, this advice could be communicated another way: Stock more fresh fruit, protein and vegetables. The periphery of the grocery store has led in the expansion of revenues – namely within produce, but also spanning seafood, bakery products and ready-to-eat
Through the M4 and M5 competitions, we've seen the promising performance of machine learning approaches in generating forecasts. The SAS whitepaper "Assisted Demand Planning Using Machine Learning for CPG and Retail" describes a role for ML in augmenting the demand planning by guiding the review and override of statistical forecasts.
On Friday Nov 27, 2:00pm GMT (9:00am EST in the US), Robert Fildes is presenting his latest research in the webinar "What do we need to know about Forecast Value Added?" This is part of the Lancaster University Centre for Marketing Analytics and Forecasting's "CMAF Friday Forecasting Talks." Here is
Welcome to the first of a 3-part series by guest bloggers Jessica Curtis and Andrea Moore: FORECASTING IS UBIQUITOUS Forecasting is core to many different business decisions across virtually every industry. Whether you’re a retailer planning a compelling assortment of SKUs or improving labor planning for distribution centers and stores, or a consumer product goods
Tomorrow we begin a three-part series on how to accelerate open source forecasting with SAS, by guest bloggers (and my colleagues) Jessica Curtis and Andrea Moore. As the popularity of open source forecasting has expanded, so has the ability of SAS to take advantage of open source capabilities. Over the
At SAS, we believe analytics is the force that drives change across organizations. Today, as change has been further accelerated, digital transformation is happening faster than anyone planned. Amid these advances, the use of analytics has become even more crucial, especially as a role in mission-critical applications. In 2020, even
Recently we announced a new strategic partnership with Microsoft to further shape the future of AI and analytics in the cloud. This commitment will make it easy for SAS customers to move their analytics workloads to the cloud. And it will introduce SAS technologies to millions of Azure customers through
Forecasting with SAS®: Special Collection SAS Press has added to its selection of free downloadable eBooks with the new Forecasting with SAS®: Special Collection. From the description: Want to get the most insight out of your data and improve the quality of your forecasts? SAS offers many different solutions to
Few of us expected to experience problems with retailers during the lockdown. Most people have, after all, been happily buying online for years. Why would anything change just because some physical shops closed for a while, or some people could not go out? However, the challenges went a bit deeper
The CEO's themselves sponsor transformation projects, especially in the telecom branch because of the costs and level of influence required.
Nancy Rausch shares 4 examples of the role data management techniques play in responding to COVID-19.
Forecasting During Chaos The Institute of Business Forecasting has produced an 80-minute virtual town hall on "Forecasting & Planning During the Chaos of a Global Pandemic." The on-demand video recording is available now and well worth a look. There is much solid practical guidance from an experienced panel: Eric Wilson,
In a recent video blog, I discuss forecast accuracy as a parameter for measuring the ability to forecast and plan demand. I further argue for the use of causal data as a key input to understanding historical demand and forecasting/planning future demand. Forecast accuracy is often claimed NOT to be
Artificial Intelligence for Forecasting Can artificial intelligence augment and amplify our forecasting efforts? Will AI impact our forecasting roles and processes? Does AI deliver the automation and forecast accuracy we've been pursuing? These are the sorts of questions to be addressed by a stellar panel of world-class experts at the
As you will have read in my last blog, businesses are demanding better outcomes, and through IoT initiatives big data is only getting bigger. This presents a clear opportunity for organisations to start thinking seriously about how to leverage analytics with their other investments. Demands on supply chains have also
How do you explain flat-line forecasts to senior management? Or, do you just make manual overrides to adjust the forecast? When there is no detectable trend or seasonality associated with your demand history, or something has disrupted the trend and/or seasonality, simple time series methods (i.e. naïve and simple
Analytics has an important role to play in supporting the police to keep our communities safe, and I believe the benefits can be far-reaching. In my previous three posts, I discussed the role analytics can play in policing, reviewed the required data and highlighted a police force that is currently
Como consumidor dou por garantido o facto de que a minha marca de “sumo de cevada” preferida se encontra, constantemente, na prateleira intermédia do último corredor do supermercado local, seja qual for o dia da semana. Esqueço-me, por vezes, que para que tal seja possível foi necessário que há seis
Artificial intelligence is the attention-grabbing, overhyped, shiny object that every organization is searching to make use of. Yes, it is overhyped, but it’s also very real and very powerful. “We do not want to add to the hype. We do not want to add to the confusion. We want to
What if you could automatically detect supply chain anomalies as they happen, or even predict them in advance? You'd be able to take timely corrective action and help maximize revenue, margins, customer satisfaction and shareholder value. There's no question: Supply chain planning and execution is complex. From design and sourcing, to
Smart retailers know that omnichannel customer experience isn't just about marketing anymore. It’s about bridging all your digital and physical channels to recognize customers wherever they are, collecting data and understanding the retail customer’s purchasing journey. By taking customer data, product data, and supply chain data - and applying predictive and prescriptive
With all the technology advancements and innovative trends driving Industry 4.0 right now, you might expect geeky topics like the Internet of Things (IoT) or artificial intelligence (AI) to be the hottest topics of discussion among industry leaders. Instead, many leaders are still more focused on workplace culture. And here’s
IBF Free Webinar The Institute of Business Forecasting is offering a free webinar on March 29, 2018: Analytically Speaking: Transforming Forecasting & Demand Planning in a New Era The webinar will be delivered by Chad Schumacher, Senior Director of Global Analytics at Kellogg's, and you can register here. From the
Please join me and my colleague Charlie Chase, for the IBF's Predictive Business Analytics Forecasting & Planning Conference in New Orleans (April 23-25). Charlie and I will be staffing the SAS booth, and available to answer your SAS forecasting questions, or provide a software demonstration. Also, fill out a short
“Quick response forecasting (QRF) techniques are forecasting processes that can incorporate information quickly enough to act upon by agile supply chains” explained Dr. Larry Lapide, in a recent Journal of Business Forecasting column. The concept of QRF is based on updating demand forecasts to reflect real and rapid changes in demand, both
Analytics-driven forecasting means more than measuring trend and seasonality. It includes all categories of methods (e.g. exponential smoothing, dynamic regression, ARIMA, ARIMA(X), unobserved component models, and more), including artificial intelligence, but not necessarily deep learning algorithms. That said, deep learning algorithms like neural networks can also be used for demand forecasting,
Are you caught up in the machine learning forecasting frenzy? Is it reality or more hype? There's been a lot of hype about using machine learning for forecasting. And rightfully so, given the advancements in data collection, storage, and processing along with technology improvements, such as super computers and more powerful
The Foresight Practitioner Conference returns to Raleigh, NC (November 15-16), with a theme of "Recoupling Forecasting with Inventory Control and Supply Planning." This event is produced jointly by Foresight and the North Carolina State University Institute for Advanced Analytics, and deals with an important topic. Too often we consider forecasting
