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
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
There's been a lot of hype regarding using machine learning (ML) for demand forecasting, and rightfully so, given the advancements in data collection, storage, and processing along with improvements in technology. There's no reason why machine learning can't be utilized as another forecasting method among the collection of forecasting methods
En este artículo, quiero darles a conocer algunos tópicos importantes al entrar en el mundo de la planeación de la demanda, tanto desde el punto de vista de quien requiere planearla, como desde los que tomarán el liderazgo para implementar este tipo de proyecto. Este es un tema que es
When it comes to forecasting new product launches, executives say that it's a frustrating, almost futile, effort. The reason? Minimal data, limited analytic capabilities and a general uncertainty surrounding a new product launch. Not to mention the ever-changing marketplace. Nevertheless, companies cannot disregard the need for a new product forecast
This Is the third and final installment of a series of posts discussing promising use cases in retail and the benefits of adopting IoT technologies in 2019. What will be the ground-breaking new application of IoT and analytics that drives an epiphany and spurs widespread adoption? In previous posts, I discussed
El popular dicho español ‘El que mucho abarca poco aprieta’ ha cobrado gran relevancia en la era del análisis de datos. Cuando los data scientists, data analysts, y analistas en general realizan modelos para predecir comportamientos, tendencias, patrones, etc. se enfrentan ante el desafío de abarcar lo suficiente para apretar
In the oil industry you can make or lose money based on how good your forecasts are, so I’ve pulled together six papers that discuss different ways in which you can leverage analytics to optimize your output and more accurately predict your production performance. Written by employees at oil and
As a resident of Northern California, I was interested in learning more about the causes of wildfires. My area has recently experienced large fires that caused many residents to evacuate their homes and some who have even lost their lives. Last October there were more than 170 fires that burned
If you think machine learning will replace demand planners, then don’t read this post. If you think machine learning will automate and unleash the power of insights allowing demand planners to drive more value and growth, then this article is a must read.
This post is an introduction to SAS Visual Forecasting 8.2. We'll build a Visual Forecasting (VF) Pipeline, which is a process flow diagram whose nodes represent tasks in the VF Process. The objective is to show how to perform the full analytics life cycle with large volumes of data: from accessing data and assigning variable roles accurately, to building forecasting models, to select a champion model and overriding the system generated forecast.
There is general agreement that the electricity and energy market is changing in a wide variety of ways. There is, however, less agreement on the likely effects of these changes. This makes it extremely hard for energy companies to know precisely where to target investment, but there are ways of
Wherever there is uncertainty there has got to be judgment, and wherever there is judgment there is an opportunity for human fallibility. Donald Redelmeirer, physician-researcher Over the holidays, I read a fascinating book titled The Undoing Project: A Friendship That Changed Our Mind by Michael Lewis (W.W. Norton & Company,
Just last week, Walmart announced that they'll be testing inventory management robots. These robots will cruise store aisles, scanning shelves to identify out-of-stock products and other issues. According Reuters, Walmart is testing these camera-equipped robots in a handful of stores, but plans to expand the test to 50 stores. We
Let me start by posing a question: "Are you forecasting at the edge to anticipate what consumers want or need before they know it?" Not just forecasting based on past demand behavior, but using real-time information as it is streaming in from connected devices on the Internet of Things (IoT).
When I talk to companies on how to improve their forecasting process they often share their difficulties with handling demand from potential large orders, where they are in a tender situation. Further, demand originating from tenders is increasing. Tender demand have characteristics which makes it difficult to handle in traditional
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 U.S. Marshals Service is the federal agency known for bringing wanted fugitives to justice. Often, the Marshals Service gets attention for these arrests, but once the publicity has died down they face a basic challenge --- where to put the individuals in their custody. The agency uses data to
Welcome back to the market driven journey in supply chain management! The three earlier articles of this short series contained: The typical road to market driven forecasting – where companies go through three different levels of maturity before moving to being market driven Market driven forecasting – and the changes
Intuitively being demand driven indicates having a pull-based manufacturing system customer orders trigger sourcing and production activities. However – it is rarely the case, that this is possible – due to the long lead time this would cause. This article will point out the requirements to a market driven supply
The term market driven – or demand driven as some prefer to call it – refers to a situation in which the supply chain responds to the actual requirements of the market. It has been the utopia of supply chain planning for years, and the number of business strategies and
In seinem Buch „Competing on Analytics“ benennt Tom Davenport die Analytik als Grundlage nachhaltiger Wettbewerbsvorteile. Der Grund dafür ist der prädiktive Ansatz. Heutzutage ist es nicht mehr möglich, ein Unternehmen alleine mit Blick in den Rückspiegel zum Erfolg zu führen. Und Analytik erlaubt den dringend erforderlichen Blick in die Zukunft.
Machine learning is taking a significant role in many big data initiatives today. Large retailers and consumer packaged goods (CPG) companies are using machine learning combined with predictive analytics to help them enhance consumer engagement and create more accurate demand forecasts as they expand into new sales channels like the
"Correlation does not imply causation.” Does that bring back memories from your college statistics class? If you cringe when you hear those words, don’t worry. This phrase is still relevant today, but is now more approachable and easier to understand. Here at SAS, we use SAS® Visual Analytics to make
It was John Allen Paulos who said, “Data, data everywhere, but not a thought to think.” That rings true more than ever before. Companies are struggling with the deluge of data coming at them from multiple channels. But traditional data channels are just the beginning. Companies also are facing an
Händler und Handel haben heutzutage Zugang zu einer enormen Menge an Daten – und damit die Grundlage für eine personalisierte Ansprache, die Kunden inzwischen erwarten. Richtig eingesetzt, kann Analytics der Schlüssel für alle möglichen Geschäftsvorteile sein – sei es, dass es darum geht, ein besseres Online-Erlebnis für den Kunden zu
It is said that everything is big in Texas, and that includes big data. During my recent trip to Austin I had the privilege of being a judge in the final round of the Texata Big Data World Championship, a fantastic example of big data competitions. It felt fitting that
Although the title of this blog posting has all the ingredients to attract the eyes of an analyst, the content is targeted for all personalities of a digital marketing organization. Before we jump into the marketing analytic use case regarding forecasting, scenario analysis, and goal-seeking for digital analytics, let's spend some time
Langsam könnte man meinen, das Christkind sei der verbeamtete Chef des statistischen Himmelsamtes. Heute geht es an die Analyse von Zeitreihen zur Vorhersage (Forecasting) der Wunschzettelmassen. Die Kindergesellschaft ist eine Konsum- und Wegwerfgesellschaft. Das merkt das Christkind nicht erst seit 15 Jahren! Da wird gewünscht und ausgepackt was das Zeug
The Rule of Three is a writing principle that suggests that things that come in threes are inherently funnier, more satisfying, or more effective than other numbers of things – Wikipedia. 3 Ps of success, Blind Mice, Little Pigs, Stooges, Musketeers, The Matrix, The Lord of the Rings, rings, pairs