Analytics: How to avoid the top hindrance to (re)building your business

Manufacturers and others working through the COVID-19 crisis and planning to regain their competitiveness will rely on data analytics more than ever. But they’ll need a smarter, more strategic approach to prevent the most common cause of failure. Fortunately, the problem is more human than technical in nature, and it’s fixable.

The Fourth Industrial Revolution or Industry 4.0 brings among other things IoT data visibility, fueling more and better data modeling, machine learning, deep learning and Big Data. For purposes of this discussion, I’m committing a transgression here, lumping these together as “analytics” despite the technical distinctions. In reality, things just keep converging so that instead of plain-old IoT, more attention is going to the newly-coined AIoT, or artificial intelligence of things.

When deployed in tandem, artificial intelligence (AI) and the internet of things (IoT) can bring powerful new capabilities and competitive advantages—a net effect that’s greater than the sum of its constituent parts. How much more powerful AIoT is than vanilla IoT at tackling organizational improvements? Take a look at the percentages inside the little orange circles:

AIoT is 39% to 45% more effective than IoT, per a survey of 450 business leaders. (SOURCE: SAS, Deloitte, Intel and IDC)

Those surveyed said that among the benefits sought for their IoT efforts, increased revenue topped the list, regardless of geography, industry or company size. AI turbocharges that effort. (More details in the full article.)

AI, analytics: old news

Since my early-1990s coverage of early analytics and AI in such applications as statistical process control and machine vision, solutions have evolved with compute power and cloud services. The practice can now reach data anywhere, including networks bridging sites within single sites, across multiple sites or throughout enterprises and supply chains.

Ceci n'est pas une cobra
Magritte, meet pachyderm: “Ceci n’est pas une cobra”

In seeking improvements, top officers of the company need to instill a pervasive culture of improvement. True, improvements can be made in the silos of R&D, engineering, production, sales, finance or other functions, but still should be guided by market signals and customer demand, not just the blind pursuit of efficiency or cost-cutting. Without that holistic approach, line managers down can fall victim to the parable of the vision-impaired persons and the elephant. In short, things aren’t always as they seem.

If such talk seems a generality, it’s also very real. Plant managers, for instance, can achieve operational excellence, but so can their counterparts in competing organizations — the technology is a known commodity available to all. This diagram from ARC Advisory Group analytics specialist and VP Mike Guilfoyle helps illustrate the scenario — which gets more play in a Smart Industry story (hitting print and online channels at month’s end):

Transformational improvement with analytics can come when market signals lead internal improvements. (Source: ARC VP Mike Guilfoyle)

Where are the gaps in your apps?

The market’s flooded with analytic sellers and solutions. Here are for deceptively simple considerations that might help uncover how to find growth opportunities; where to find the greatest needs; how to approach solutions and who will support a cycle and culture of improvement:

Imagining I were in your shoes, I came up with a thought exercise with four very basic questions that might be a good first step closing the opportunity gap in your application of analytics:

1. In every sector and at every level, organizations generate massive amounts of presumably (or potentially) visible IoT dataso where are most data generated throughout your entire digital universe that you can, or should be measuring?

2. For every area that can be measured, analytics solutions (or platforms) can be bought, adapted or created to yield greater insightso where are there gaps in your management and automation systems across your value chain(s) that if filled, will yield the greatest visibility for tracking and potential improvement/innovation?

3. Every business and operational system in the IT/automation marketplace must include some provision for the use of analyticsso how capable are your systems to perform analytics via native functions, third-party partnerships or via a suitable level of standard integration capability?)

4. Every application of analytics requires a sustainable ecosystem of training and support for better decision-making and ongoing innovationso do you have a sustainable culture of improvement, with suitable lifecycle support structures in place via in-house and/or reliable external resources?


How are you approaching analytics?

Can you help me support the cause of progress? Share your thoughts and anything allowable…I’ll work with you and your organization to see how we can get the word out.

The eye in the sky, now on the edge

Discussing digital transformation with a brilliant mind in the field of digital transformation recently, I asked: “What emerging technology do you believe will be most transformational in the industrial sector?” His answer: Video.

His focus on video stems from the growth in IT bandwidth and the ability to support video with AI-powered analytics to more effectively discern patterns when data is deployed across IoT networks. AI and the Internet of things (IoT), or the combined AIoT, is deemed critically important to digital transformation initiatives, industry leaders say. Where AI analytics are deployed depends upon the challenge to be addressed.

As an AI-focused executive with a leading firm connected to these converging technologies, the gentleman I was speaking with was fascinated by a demo he saw at a recent trade show. It depicted a factory with thousands of cameras installed, one above each employee workstation. The goal, he said, was “to effectively use a camera to ‘sensorize’ a person [and] figure out if folks are deviating from the agreed processes.”

Businesses of all types — from small machinery suppliers to large manufacturers (like Foxconn) have for decades used cameras and machine vision software for applications such as quality assurance, to recognize patterns and detect and reject off-spec products. It’s really no surprise, but a logical extension of what Henry Ford did in automaking; what W. Edwards Deming did in post-WWII Japan; and what Ray “McDonald’s” Kroc did in fast food.

What may be surprising how real-time video analytics can transcend the mechanistic aspects of how people act to discern how they feel. In video and audio applications, machine learning has already been applied to customer service call centers and doctor-patient telemedicine apps to identify such things as anger and frustration. The data inputs for A/V analytics include voice analysis, facial expressions, movements, and posture. Industrial applications are no doubt coming, if not already here.

The edge of progress

As market demand fuels technical capability, video analytics solutions will continue raining down from longer-term remote cloud applications to people-watching applications based in real-time edge computing networks on or near the production floor. Intel’s Chet Hullum and his team improved semiconductor manufacturing at the edge because addressing cloud latency issues “would be far too expensive,” as I reported for SmartIndustry. (Intel Tweet and LinkedIn post (below) give more background:

Logroll on the blogroll: Please to have Intel ‘social’ my writing.

With advances in connectivity and pervasive analytics, the eye in the sky really isn’t the limit anymore.


help me help you!

The robots are winning

AI disruption seems to have thinned the workforce for good. Now what? 


Where would we be if wheels were still square? If the Luddites kept machines out of textile mills? If the cotton gin didn’t decimate the pre-war Confederate workforce? Today, one has to wonder how many truck drivers will be able to find new work once driverless rigs rule the roads. 

With regard to the the long-term impact of automation general, I’ve been ambivalent for decades; gung-ho for progress but concerned about a gutting of the workforce. In the mid ’90s, I spent a day with an engineering leader who gave me a tour of his workplace, a refinery that stretched for miles. He showed:

  • Pride in his control network, which bore early AI enhancements
  • Sorrow for the loss of most employees at the plant and industry-wide due to automation, and
  • Resentment of the coming “corpocracy” in which individuals lose their humanity.

I shared my ambivalence in a column I wrote for Control magazine at about that time after a conversation with a senior engineer at another Big Petro firm folding 17 subsidiaries into one. I started the column ranting about corporate “destructuring” and “dumb sizing,” and ended with: “It’s hard to argue with the bottom line.” 

ReThinking robots

Likewise, I’m mixed about the impact of industrial robots, worth about $40 billion and pegged to top $71.72 billion by 2023. Companies choose to replace people when the technology is available because they’re a better-faster-cheaper way to go. I was saddened at the demise of ReThink Robotics, maker of Baxter and Sawyer, the industry’s friendliest collaborative robots, or cobots. (collaborative robots). And I was glad the company was quickly bought-up by Germany’s Hahn Group. Rethink, a small player compared to leaders such as FANUC, ABB and Yaskawa, failed not for lack of demand, but in large part due to technical issues, if this RobotReport postmortem is accurate.

Baxter (pictured) and brother Sawyer have a new parent in Hahn.

In better times (2016), I spoke with ReThink’s Jim Lawton, chief product and marketing officer, who extolled his bots’ rapid ROI (as low as 1.5 years) and quick installation time (under a month) as well as speed and flexibility. For instance, he told me of a Tier One automotive supplier that replaced 20 hours of manual labor day: “$25,000 for the robot, and a little bit for the grippers, and we’ve saved them $180,000 a year.”

Robots don’t call in sick, need healthcare, come in late or take days off. They do work when workers don’t want to, or can’t be found. And companies are bound by the need for competitive advantage and higher profits to use them if they’ll keep the shareholders happy. But again, the industrial workforce, once an engine of the middle class, is shrinking.   

The U.S. middle class: Doomed? 

Technology both eliminates and creates =jobs, but the former appears to be winning in the U.S. marketplace. 

As one Clorox exec said during a panel discussion a couple of years back, “every” manufacturing company is busy automating and “leaning-out” its lines. A Kraft Foods alum added that it’s commonplace for companies to replace upward of “100 people on a Lunchables line [with robots] picking up stacks of pre-sliced meat and pre-sliced cheese.” 

“Employment trends have polarized the workforce and hollowed out the middle class,” David Rotman, editor of MIT Technology Review wrote in the article, “How Technology is Destroying Jobs.” Since then, TechCrunch promulgated the paradox that “Technology is killing jobs, and only technology can save them” (2016),” The New York Times shared in 2017 “Evidence That Robots Are Winning the Race for American Jobs.

What to do? Thought leaders across business, politics and industry have since given credence to the movement for Universal Basic Income, a flat payment to every citizen, to address poverty and job losses largely incurred by technological advancement.

UBI has been advocated by the Brookings Institution, given credence by Fortune (no leftist-socialist totem), and promoted by Elon Musk, Richard Branson, and many in Silicon Valley, including Mark Zuckerberg. More recently, UBI generated headlines in 2018 in Chicago with a petition by city Alderman Ameya Pawar (@Ameya_Pawar_IL) to take the city Universal:

Chicago, future home of Universal Basic Income?  

There ain’t no “i” in TEAM

Henry Ford did it, McDonald’s did it, and now the collective, global technology hive mind is doing it: Standardizing business processes to advance the competitive mandate for greater productivity and profitability. I love to hear stories of happy employees, but below photo mesmerized me to the point of distraction. So I’m using it to illustrate a point:

The point — no offense intended to the editor who penned the caption! — is that companies can’t afford for their employees to be “themselves” in that we can’t have guitar players, poets or sewing circle meetings on the factory floor. The employees pictured are essentially identical, down to their garb (as required by sanitary food handling rules). Their jobs are the same, too: to comply with uniform standards procedures.

But if there’s no ‘i’ in TEAM,” there’s still something of a “we.”  The ranks will thin, but there will remain a critical need for creative human minds to solve problems, even at the line level.

Almost a decade ago I helped a manufacturing exec, Greg Flickinger, document a cultural transformation at food firm Snyder’s-Lance in Charlotte, N.C. His team reduced scrap 40 percent; reduced customer complaints 41 percent; and, among other good stuff, slashed production changeover time to save more than $300,000 annually.

The human-machine interface, writ large over time, points to great gains and equally daunting challenges. Let’s face it: As a species, we’ve got a troubling historic myopia, and an immediate need to reconcile longstanding issues relating to our technology and economy, or techonomy.



Retail analytics: Here’s lookin’ at you, kids

I recently reunited with FoodOnline.com to write a story on Big Data analytics in the retail food supply chain. The first bylines I had for that site were in 1999, when I was Editorial Director for that related sites — before the big bursting of the Internet Bubble. Accenture’s Stages of Analytic Capabilities

When the Internet was new, there were no iPods, let alone iOS, Android or Bluetooth-powered beacons; Big Data was just a gleam in its young Business Intelligence mother’s eye. And retailers had no idea what to really do with their Business Intelligence systems. Those who finally do, today, see BI as old news as analytics — predictive and now prescriptive — come into their own, powered by Big Data and cloud computing.

Today, as an exec from SAS told me, a shopper who stands in front of a Nescafé display for more than 10 seconds might just get a virtual tap on the shoulder, or rather a bzzzz in the pocket, with a coupon to get that package of coffee off the shelf and in to the cart.

My favorite interview in this story just might have been Nick Hodson, former head of strategy at Safeway Stores and current leader of the North American consumer and retail business practice of Strategy& PwC, who reminded me that the technology isn’t at all the point of progress so much as creative minds who come-up with new things to do with it. Yes, major retail marketers from Walmart and Nestlé may well fave a backlash over privacy concerns if opt-in/out issues aren’t handled correctly, but these times sure are interesting. Read all about it in my story, ” Predictive Analytics Helping CPGs Reach Individual Consumers.”