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12 August 2022

It is impossible to generate a perfect demand forecast. What makes it impossible is noise—seemingly random or arbitrary fluctuations in demand. Noise is not forecastable. Many times, when planners manually adjust the forecast, they are trying to predict the noise that[...]

Don’t Forecast Demand, Model Your Demand 14 May 2021

Don’t Forecast Demand, Model Your Demand

Many companies are looking to demand forecasting and modeling to improve their supply chain perforamnce without understanding the difference between these two practices. But they are actually two very different concepts. Let's break down the differences. What Is Demand Forecasting?[...]
Robots in the Building: The Positive Influence of AI for Supply Chain Planners 8 August 2019

Robots in the Building: The Positive Influence of AI for Supply Chain Planners

Have you looked at a job listing for a supply chain planner recently? The planner’s role has expanded to include a combination of technical skills and communications and interpersonal skills. A recent, typical job ad for a demand planner lists[...]
Quick Start Guide to Using Machine Learning for Demand Planning 25 April 2019

Quick Start Guide to Using Machine Learning for Demand Planning

Anyone who has done demand planning knows it is extremely complex, with forecasting challenges and rapidly shifting consumer demand, often exacerbated by seasonality, new product introductions, promotions, and myriad causal factors (e.g. weather, social media). The good news is that[...]
Recreating the Environment in which Historical Demand Occurred 8 January 2019

Recreating the Environment in which Historical Demand Occurred

At last year’s Gartner Supply Chain Executive Conference analyst Tom Enright summarized the essence of successful demand forecasting with the following statement: “Demand forecast accuracy depends on your ability to recreate the environment in which historical demand occurred.” While seemingly[...]
Demand Planning is the top Machine Learning App for Supply Chain Planning 11 December 2018

Demand Planning is the top Machine Learning App for Supply Chain Planning

Gartner recently polled both users and vendors on which types of supply chain planning (SCP) applications were employing machine learning technology. In three different analysis, demand planning and demand forecasting came out on top of the list. With years of experience now in[...]
Advanced Analytics versus Artificial Intelligence 26 April 2018

Advanced Analytics versus Artificial Intelligence

For firms with Advanced Analytics or Artificial Intelligence (AI) in their future, Gartner has just published a useful report with three findings that clarify the difference between the two technologies and offers insight on employing them for augmenting versus automating supply chain decision making. 1. Advanced Analytics is[...]
Trade Promotion Forecasting: the Present and the Near Future 30 January 2018

Trade Promotion Forecasting: the Present and the Near Future

Trade promotion forecasting is difficult: getting it right involves factoring in many variables. Marketing people, who are often responsible for it, may excel at creating and running promotions. Forecasting demand data, not so much. This can leave supply chain professionals[...]
Will you be a Supply Chain Disruptor or the Disrupted? 28 November 2017

Will you be a Supply Chain Disruptor or the Disrupted?

You should be interested in applying disruptive technologies in your supply chain not only at a physical level, like in transportation and logistics, but also at a more virtual level in your supply chain planning. And you should be thinking[...]
Is Your Supply Chain Planning Ready for the “Experience Economy”? 17 October 2017

Is Your Supply Chain Planning Ready for the “Experience Economy”?

Gartner says we are entering what they call the ‘Experience Economy”, where personalization, speed, agility and the ability to predict consumer trends before they happen will separate the winners from losers (Aspire, Challenge and Transform in a Disruptive World, 27[...]
Traditional Statistics versus Machine Learning. What’s the Difference? 19 September 2017

Traditional Statistics versus Machine Learning. What’s the Difference?

Since machine learning is a hot topic, we thought it useful to clarify the difference between machine learning and traditional statistics as applied to supply chain planning (SCP). Both technologies help you better understand data and are classified as analytics,[...]
The Power of Downstream Data for Sensing Demand 8 August 2017

The Power of Downstream Data for Sensing Demand

Lora Cecere, CEO of Supply Chain Insights, has released a useful new report entitled “The Power of Downstream Data” which describes previously undocumented benefits for collecting and analyzing downstream supply chain data. Downstream data is the demand data that powers a market-driven, “outside-in”[...]
A Path Towards IoT Enabled Service Parts Planning 25 July 2017

A Path Towards IoT Enabled Service Parts Planning

Editor's Note:  This week we have a guest post from Bob Ferrari, Managing Director of the Ferrari Consulting and Research Group. This blog first appeared in Supply Chain Matters. It is the second in a series on how companies can build foundational service[...]
Seven Recent Trends in Retail Demand Forecasting and Replenishment 24 May 2017

Seven Recent Trends in Retail Demand Forecasting and Replenishment

Since most retailers are facing a shrinking operating “margin for error”, many are looking for more accurate demand forecasting and intelligent stock replenishment. Gartner analyst Mike Griswold explains how in his recent report on retail demand forecasting trends, entitled Market[...]
Retailers, Is Machine Learning a Little Black Dress or an Accessory? 28 April 2017

Retailers, Is Machine Learning a Little Black Dress or an Accessory?

At first glance, a discussion about clothing and technology seems a bit incongruous, even for a retail focused blog. What could a dress possibly have to do with running your business? But they are more closely related than you may[...]
Why You Need to Embrace Autonomous Supply Chain Planning 18 April 2017

Why You Need to Embrace Autonomous Supply Chain Planning

Editor's Note: This piece (with small variations) first appeared as a Supply Chain Brain editorial joining the debate about autonomous software and its impact on work and society.     We are living in a new wave of automation. Machines are already performing many tasks previously done by[...]
Four Prerequisites for Demand Sensing Success 5 April 2017

Four Prerequisites for Demand Sensing Success

In a previous popular blog we described the key components of a demand sensing solution, which helps improve near-term forecasts by leveraging detailed, short-term demand data. It’s a great competency for any business—to be able to grab downstream demand data, identify[...]
Five Ways Machine Learning Improves Demand Forecasting 24 January 2017

Five Ways Machine Learning Improves Demand Forecasting

When it comes to demand forecasting, machine learning can be especially helpful in complex scenarios, allowing planners to do a much better job of forecasting uncertain demand. Demand forecasting with machine learning leverages the knowledge, experience, and skills of planners and[...]
Using Weather and Climate Data to Improve Demand Forecasting 6 December 2016

Using Weather and Climate Data to Improve Demand Forecasting

Demand forecasting software can usually factor in climate and seasonality, like more ice cream being sold during summer months or in warmer climates, but weather data adds a more granular time-sensitive signal. Weather can drive demand: like heat and beach goods, rain and umbrellas, or[...]
Why You Should Embrace Uncertainty in Demand Forecasting – Part 2 8 November 2016

Why You Should Embrace Uncertainty in Demand Forecasting – Part 2

Last week in our first post about uncertainty in demand planning, we said that in reality most companies are already part way down the path towards a probabilistic supply chain model. The evolution is happening naturally as the global business landscape[...]
Why You Should Embrace Uncertainty in Demand Forecasting 1 November 2016

Why You Should Embrace Uncertainty in Demand Forecasting

It's human nature to look for certainty. We believe that if we can predict the future with perfect certainty, then we will react and prepare accordingly. As such, we tend to put all our planning effort into making better predictions.[...]
Has Driverless Supply Chain Planning Already Arrived? 4 October 2016

Has Driverless Supply Chain Planning Already Arrived?

There has been a lot of news about driverless cars, and at the Supply Chain Insights Global Summit there was also some news about “driverless” supply chain planning when two companies described supply chain planning with almost no human intervention. More on[...]
The Amazon Supply Chain Revolution, Part I 6 September 2016

The Amazon Supply Chain Revolution, Part I

Did you know the Amazon revolution actually began 30 years ago. At the time, retailers were rarely known for their investment in innovation and technology, but Wal-Mart went against the grain in the 1990’s when it introduced the idea of[...]
The Seven Key Capabilities of Demand Sensing 23 August 2016

The Seven Key Capabilities of Demand Sensing

Many consumer product companies are showing keen interest in demand sensing. A few weeks ago, we published a blog describing why most CPG companies have hit a demand forecasting ceiling. This week Manoranjith Pathekkara, managing director of ToolsGroup's partner in India, LogicaMatrix, digs into how companies are[...]
Why Forecast Accuracy Alone Can’t Solve Service Level Problems 2 August 2016

Why Forecast Accuracy Alone Can’t Solve Service Level Problems

In recent blogs we’ve discussed the importance of forecast accuracy, but forecast accuracy alone can’t solve inventory issues—particularly in the long tail where a zero forecast for slow-moving items renders WMAPE forecast error essentially meaningless. The best way to understand this[...]
There’s Blood in the Supply Chain 28 June 2016

There’s Blood in the Supply Chain

Editor's Note: An associate of mine just pointed out that some readers may be uncomfortable with the title of this blog in light of recent events in Turkey. Since email, social media and other alerts have already been distributed, it's difficult to change[...]
Demand Analytics and the Omni-Channel Customer Experience 16 June 2016

Demand Analytics and the Omni-Channel Customer Experience

While much has been written in the trade press about the omnichannel opportunity, somewhat less has been written about the potential issues from a buyer’s perspective. This new shopping environment is empowering – name your product, name your price, you’re in[...]
Ex Machina: AI and the Future of Supply Chain Planning 12 January 2016

Ex Machina: AI and the Future of Supply Chain Planning

There’s been a lot of discussion lately about the future of artificial intelligence and its impact on human beings. It recently gained heightened attention with the movie “Ex Machina,” where an AI creation outsmarts its maker, escapes the closed experimental environment,[...]
Sensing Demand at Point of Sale – Part 2: Demand Signal Repositories 8 December 2015

Sensing Demand at Point of Sale – Part 2: Demand Signal Repositories

Last week we discussed how grabbing hold of data at the point of consumption—essentially, taking continuous-frame snapshots of buyer behavior—can improve supply chain performance and enhance inventory optimization. But many companies don’t have their own captive retail network and therefore don’t have[...]
Sensing Demand at the Point of Sale 1 December 2015

Sensing Demand at the Point of Sale

There is a new technique for improving forecast accuracy and inventory optimization—sensing demand from Point of Sale (POS) data or "POS Demand Sensing". When you see which products, or product variants, are actually moving—and how fast they’re selling, in what[...]
Five Things You Need to Know About Machine Learning for Supply Chain Planning 27 October 2015

Five Things You Need to Know About Machine Learning for Supply Chain Planning

First a confession: I borrowed this blog’s title directly from Gartner Analyst Noha Tohamy and her recent research note Five Things Supply Chain Strategists Need to Know About Machine Learning. I couldn’t do better myself. Noha’s main message in her note[...]
The Challenge of “Click and Collect” Supply Chain Planning 29 September 2015

The Challenge of “Click and Collect” Supply Chain Planning

“Click and Collect” lockers and sales kiosks are becoming increasingly popular methods of order fulfilment, and for good reason. Customers order items any way they want and pick up the goods at a location convenient to them. It is the[...]
Sensing Demand or the Art of Hitting a Curve Ball – Part 2 30 June 2015

Sensing Demand or the Art of Hitting a Curve Ball – Part 2

How do you get to be really good at forecasting demand and driving higher supply chain performance? The same way you get to be good at hitting a curve ball – by sensing what’s happening sooner. In baseball, you want[...]
Demand Sensing and the Art of Hitting a Curve Ball – Part 1 23 June 2015

Demand Sensing and the Art of Hitting a Curve Ball – Part 1

They say sports is not just about doing the right thing, but being able to do it very fast. Think of an outfielder in baseball who, at the crack of the bat, senses whether the ball is headed behind or[...]
Rise of the Machines – Predictive Analytics in Supply Chain Planning 19 February 2015

Rise of the Machines – Predictive Analytics in Supply Chain Planning

Editor's Note: Chainalytics is a leading global supply chain consulting firm and long time ToolsGroup partner. “Open the pod bay doors HAL…. I’m afraid I can’t do that Dave.”  The movie 2001: A Space Odysseywas made in 1968, so the idea of independent computer[...]
Improving Demand Forecasting with Machine Learning 20 January 2015

Improving Demand Forecasting with Machine Learning

In our last blog we talked about using best-of-breed software to overcome the shortcomings of ERP-based supply chain planning systems. One way to improve demand forecasting and demand planning is with machine learning and demand analytics. Business complexity and increased[...]
Demand Modeling Part 2 – Separating the Signal from the Noise 11 November 2014

Demand Modeling Part 2 – Separating the Signal from the Noise

It is impossible to generate a perfect demand forecast. What makes it impossible is noise—seemingly random or arbitrary fluctuations in demand. Noise is not forecastable. Many times, when planners manually adjust the forecast, they are trying to predict the noise that[...]
Supply Chain Innovation: The Internet of Things 14 October 2014

Supply Chain Innovation: The Internet of Things

The next big thing has arrived—and it’s called the “Internet of Things” (IoT). McKinsey Quarterly sums up the impact: “The predictable pathways of information are changing: the physical world itself is becoming a type of information system. When objects can[...]
Supply Chain Innovation: Four Ways Demand Sensing Improves Forecasting 7 October 2014

Supply Chain Innovation: Four Ways Demand Sensing Improves Forecasting

In the last post we saw how Costa Express used POS data and analytics to optimize forecasting and inventory replenishment. Costa shortened the time frame for gathering data and performing analytics. It concentrated on what was actually being sold, rather[...]
Supply Chain Innovation Case Study: Demand Data Powering Growth 30 September 2014

Supply Chain Innovation Case Study: Demand Data Powering Growth

Last week we talked about demand sensing and market-tuned forecasting, and the importance of capturing granular supply chain demand data. This week we’ll go beyond the abstraction and look at how one company does it—and achieved 3X growth, without adding headcount and[...]
Supply Chain Innovation: Three Steps Up to a Market-Driven Forecast 23 September 2014

Supply Chain Innovation: Three Steps Up to a Market-Driven Forecast

Last week we delved into demand and demand analytics, which has become a new supply chain innovation priority. This week we take the next step to plot specific steps to becoming more demand-driven and even to market-driven demand forecasting. Business[...]