Machine Learning Archives

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Don’t Forecast Demand, Model Your Demand

14 May 2021

Demand modeling is different from demand forecasting. Simply put, it doesn’t forecast demand, it models demand. Forecasting typically starts with a time series of data—usually presented as a bar chart displaying demand by month or by week. It makes a[...]

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[...]

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[...]

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[...]

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[...]

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[...]

21 February 2018

How Not to use Machine Learning for Demand Forecasting

Eight years ago ToolsGroup was one of the first supply chain planning software vendors to employ machine learning to improve demand forecasting. As the tag line to a popular commercial goes, by now “we know a thing or two because[...]

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[...]

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[...]

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[...]

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,[...]

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”[...]

Demand Sensing reduces demand variability by extracting signals out of noisy demand, provides earlier demand insights, and enables more accurate short-term plans and faster reactions to market changes.

ToolsGroup’s Sales & Operations Planning (S&OP) solution helps companies achieve both higher levels of S&OP maturity and better business outcomes by bridging the gap between strategic planning and operational execution.

Promotions Planning gives cross-functional teams the visibility to synchronize demand shaping campaigns and promotions with supply chain operations ensuring that inventory is in the right place to meet demand on a daily basis, right down to the store level.

Production Planning provides unparalleled visibility, insight and control of the entire production process to improve efficiency and quality control, and service demand.

Inventory Optimization factors in multiple planning variables and probabilities to generate an optimal multi-echelon inventory plan for every item in a portfolio to achieve target service levels.

Allocation and Replenishment automatically calculates optimal inventory levels for both existing and new items to create a time-series plan that achieves target service levels even in the face of demand variability and distribution complexity.

Demand Forecasting and Planning automates the creation of demand plans using machine learning and empowers demand collaboration reducing forecast error and optimally deploying inventory.

Supply Planning provides visibility across vendors and warehouses, simplifies collaboration with suppliers, optimizes the supply network and leverages machine learning to continually improve planning by analyzing a wide range of variables and data across the supply chain.