At the End of the Day, It's All About Analytics Forecasting


Charlie Chase, Industry Consultant, SAS Retail/CPG Global Practice

Charles Chase is the co-author of Bricks Matter: The Role of Supply Chains in Building Market-Driven Differentiation and the author of Demand-Driven Forecasting: A Structured Approach to Forecasting, as well as over 50 articles in several business journals on demand forecasting and planning, supply chain management, and market response modeling. He is the principal industry consultant for the SAS Retail/CPG global practice, and writes a quarterly column entitled, "Innovations in Business Forecasting" in the Journal of Business Forecasting.

Machine Learning Changes the Way We Forecast in Retail and CPG

Large retailers and consumer packaged goods companies are using machine learning and predictive analytics to improve customer experiences.

The Demand Signal Management Proposition...

While the availability and collection of data are compelling companies to invest in demand signal management solutions to support their planning processes, many have not gotten the return on their investment. However, others are realizing that the real value is in visualization, exploration, and predictive analytics.


Re: Knowing what to measure
  • 9/3/2017 7:27:43 PM
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In re "...technology that should be an adjunctive aid to human work, and a way to boost productivity, is being used as an "almost as good as" replacement for the humans themseves."

Which means it will be coming to a call center or outbound telemarketing department near you!

The upside: Their algorithms will soon learn I never pick up.

Re: Knowing what to measure
  • 9/1/2017 7:32:30 AM
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..

Terry writes

In re "...If AI/ML is worth anything, surely the machines will soon have the capability to perform their own self-tweaking?"

Yes, yes, yes, Lyndon. This is the logical conclusion, if we take all the breathless hype around AI at its face value. The algorithms that can teach themselves create ever greater efficiencies and savings have it handled!

I don't know if this actually true. But the flavor-of-the-month way the industry is talking about artificial intelligence and machine learning certainly suggests they believe it.

As AI/ML is substituted for human endevor, one of the multiple problems I see coming involves quality. Businesses (and lots of public-sector agencies also) are continuously looking for a cheap fix in a kind of desperate drive to reduce ongoing costs. AI/ML may work for some substitutions, but with the level of current technology, only up to a point. Nevertheless, automated substitutes are often being implemented if they're "almost as good" as the human performance they're replacing. Basically, technology that should be an adjunctive aid to human work, and a way to boost productivity, is being used as an "almost as good as" replacement for the humans themseves.

..

Re: Knowing what to measure
  • 8/31/2017 11:19:09 PM
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From what I understand, while computers writing their own code or stealing them from other programs is a reality, for right now it's for simple math problems.  At this moment it's not ready to scale to tackle great problems.

Re: Knowing what to measure
  • 8/31/2017 11:11:09 PM
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Interesting. I didn't realize this was a current trend. I think it's great!

Re: Knowing what to measure
  • 8/28/2017 12:14:51 PM
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In re "...If AI/ML is worth anything, surely the machines will soon have the capability to perform their own self-tweaking?"

Yes, yes, yes, Lyndon. This is the logical conclusion, if we take all the breathless hype around AI at its face value. The algorithms that can teach themselves create ever greater efficiencies and savings have it handled!

I don't know if this actually true. But the flavor-of-the-month way the industry is talking about artificial intelligence and machine learning certainly suggests they believe it.

Re: Knowing what to measure
  • 8/27/2017 11:22:22 AM
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@tomsg, more emphasis should be put into getting common buy-in from all sectors. I don't believe enough is done to sell the common goal which will override any resistance.

Re: Knowing what to measure
  • 8/27/2017 6:27:50 AM
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They key is to overcome the inherint resistance.

Re: Knowing what to measure
  • 8/26/2017 4:16:07 PM
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'Things do work best when different disciplines work together' Couldn't agree more! There is an ingrained culture of resistance to fully coorperate with the perceived competing disciplines.

Re: Knowing what to measure
  • 8/25/2017 5:16:38 PM
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In his blog post, Charlie Chase writes

You need to start with analytics-driven forecasting using large scale automatic hierarchical forecasting technology supported by data scientists who have the domain knowledge, and the advanced analytical skills to monitor, track, and tweak models as the market and consumer preferences change.

I can see the day coming when this would go beyond the expertise of the data scientists, or at least their ability to monitor-tweak etc. with sufficient timeliness. IF AI/ML is worth anything, surely the machines will soon have the capability to perform their own self-tweaking?

..

Re: Knowing what to measure
  • 8/24/2017 8:20:51 AM
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I agree. Things do work best when different disciplines work together.

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