Manufacturers can

build on Ideas from

Sherlock Holmes to derive

value from data using

artificial intelligence

and machine learning

technologies.

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The CASE

of the

MISSING INSIGHTS

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to compartmentalize problems and then apply solutions appropriately to each quadrant.

Compartmentalizing vs. Sense-Making

P roblem-solving approaches are usually post facto, meaning a problem already occurred and

now it needs to be solved. The traditional attempt to problem-solving is to adopt so-lution approaches based on the type of the problem. Incremental gain seekers tend to focus on problems in the known-known quadrant. Businesses today must decide whether to start with known questions and answers or questions and answers they know nothing about.

When it comes to new answers to new

questions (unknown-unknowns), the typical approach is to try to solve the problem first, and then attempt to figure out what caused it in the first place. Unlike human problem-solvers, AI can leverage big data to go beyond the obvious (such as trying to use existing, known-known answers) by deriving its own conclusions based on both structured and un-structured data. In the categorization models, frameworks precede data and in sense-mak-ing models data precedes frameworks. In the classic HBR article, “A Leader’s Framework for Decision Making”, there is emphasis on the need for agility in decision-making styles to match changing environments1.

Machine-learning algorithms such as reinforcement learning game theory can be used to develop new theories on what

As companies attempt to optimize and dis-rupt business via new technologies such as artificial intelligence (AI), machine learning (ML) and the internet of things (IoT) — and grapple with proofs of concept — the chal-lenge has often been the focus on the wrong problem. Prioritizing the right problems in the run-transform cycle and focusing on business value will help firms derive value from the power of these technologies. In the end, focusing on return on data will ensure rich rewards.

What Are the Right Problems?

D ata science is about achieving positive business outcomes by bringing together the best ana-

lytical framework with domain know-how — or is it? Domain know-how is based on known answers to known problems. What if your business outcomes change constantly, but your decision-makers are still applying

approaches that worked with previous prob-lems? Do you need to be more intelligent about embarking on this transformation jour-ney? And, equally important, what problems should you try to solve first?

The answers to these questions are in the data itself and in what lies hidden in that data. Given the complexity and volume of poten-tial questions and answers faced by today’s real-world businesses, the probability of solv-ing any particular problem increases greatly when advanced analytics, AI, and ML are added to the mix.

The foundation for this new approach to problems and solutions is illustrated in the following four-quadrant graphic. The quad-rants progress from applying known answers to known questions (known-knowns), to ap-plying new answers to known questions, new questions to known answers, and, finally, new answers to new questions (unknown-unknowns). This visualization makes it easy

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Sath Rao is Director of Digital Solutions for Manufacturing, Prod-uct Marketing at Hita-chi Vantara, which is a member of the MLC.

“In the categorization models, frameworks precede data

and in sense-making models data precedes frameworks.”

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nterprise transformation depends on the successful application of technology and change management to both solve business chal-lenges and exploit new opportunities. The power of transformative technology can be expo-nential when an integrated approach towards humans, machines, and data is adopted. This concept can be applied to digital transformation, to addressing manufacturing inefficiencies, and to enabling next-generation business model transformation — all components of the fourth industrial revolution, Manufacturing 4.0.

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Focusing on the Right Problem-Solution Continuum

E“It is a capital mistake to theorize before one has data. Insensibly, one begins to twist facts to suit the-ories, instead of theories to suit facts.”

Sherlock Holmes,  A Scandal in Bohemia

(Sir Arthur Conan Doyle, 1891)

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works and what doesn’t. By using these algorithms, businesses can generate new insights and simulate outcomes through hypothesis testing. Further, the abil-ity to identify new problems based on the data and then develop answers helps scale economies of learning. This process takes human assumptions and traditional rules-based approaches out of the loop and en-ables autonomous insights. Organizations focusing on economies of learning can also leverage the asset-light model and can quickly challenge incumbents focused on economies of scale (think Uber or Tesla versus the automotive majors).

As more businesses apply AI to defin-ing and solving problems for business out-comes, the challenge is to let go of past prac-tices. Domain expertise is very important to guide outcomes especially when controlling critical situations. The key deficit area now is trust. Explainable AI (XAI) is an emerging area where the aim is to help demystify the black-box approach of arriving at decisions by progressively building trust in the system. Decision making can then be split into tiered layers with standalone systems that can also connect to a system of systems that help with prediction accuracy, decision understand-ing, and inspections and traceability.

A System of Systems Approach

A n evolving paradigm is the horizon-tal approach that determines where computing will be done to solve

problems based on the complexity of the prob-lem, the tolerance for connectivity-oriented latency, and decision latency, as shown in the illustration. Note that there is still a place for steady state point-of-use computing based on the time needed for the decision.

In some instances, computing for prob-lem solving must happen locally. For exam-ple, a self-driving car needs to use onboard algorithms while it’s driving so that it can make moment-by-moment decisions for braking, changing lanes, and so on, with-out communicating with cloud databases. It can still communicate with the cloud to gain broader knowledge that isn’t critical in the moment, such as traffic conditions and alternate routes. Based on permission, if a traffic congestion situation can prompt special incentives for a detour to a favorite eatery, and reserves a table, too, it would be a whole new level of customer experi-ence! Reinforcement learning could actu-ally deliver a combination of surprises that are pleasant and unexpected.

In a manufacturing environment, the ability to drill down to root-cause, examine incoming supplier records, and use video-based analytics for inventory backlog analysis can trigger op-

timization opportunities in the manufacturing process and drive forecasting insights.

Sourcing Data from the Edge to the Cloud

D ata today is flowing in at an unprec-edented rate, from sources on the edge, in the cloud, and everywhere

in between. This data provides a wealth of an-swers never before attainable, but, at the same time, it produces an overwhelming number of new questions. The sheer volume of data being generated is no longer possible for humans to decipher. Addressing this challenge is another aspect of the new paradigm, an approach that moves from the edge to the cloud and from storage to enrichment.

In manufacturing, the horizontal ap-proach might translate to computing at the edge to monitor processes within a single machine, while communicating with the

cloud to coordinate with other machines or processes and lines, or even across the sup-ply chain to optimize the overall manufac-turing process. This is a key component of Manufacturing 4.0, which relies on data-driven insights to reframe assumptions and look beyond past practices.

Applying the New Paradigm

M aking AI part of your approach to Manufacturing 4.0 requires more than just the technology; it

requires a new way of thinking about that tech-nology. Manufacturing 4.0’s core promise of cyber-physical systems — the coming together of physical and cyber functionality -- enables the modeling of digital twins for processes and products, and it provides the ability to predict failures and initiate remediation ahead of time. When you have AI integrated into your journey to Manufacturing 4.0, you can decide which

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“Machine-learning algorithms such as reinforcement learning can be used to develop new theories on what works and what doesn’t.”

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“Education never ends, Watson. It is a series of les-sons, with the greatest for the last.”

Sherlock Holmes,  The Adventure of the Red Circle (Sir Arthur

Conan Doyle, 1911)

AI-enabled Insights Activation

The Future of Problem-Solving: A New Paradigm “There is noth-ing more decep-tive than an obvious fact.”

Sherlock Holmes,  The Boscombe Valley

Mystery (Sir Arthur Conan Doyle, 1891)

steps to take with greater confidence, can look at the business context before making decisions, can predict bottlenecks, and much, much more.

In the customer-centric economy, the real value is in the outcome for the customer and the ability to differentiate based on ex-perience and personalization. The ability to generate insights that can actively empower customers is going to drive the next wave of competitive differentiation. Gartner notes in a research outline, “The CX Pyramid: A Framework for Powerful Experiences”, that the focus of differentiation ranges from solv-ing problems to proactively making custom-ers feel better and empowered with answers. 2

Manufacturing 4.0 will empower progres-sive manufacturers to provide this visibility to their customers both in a B2B and B2B2C context. While the current focus of Manufac-turing 4.0 has been on digital twins of prod-ucts and processes, increasingly, AI/ML will help drive a vision of the digital twin of the en-tire enterprise, and, based on the customer’s digital-entity, transform their experiences.

The Importance of DataOps

T o realize the full benefit of AI technologies, you need a vision, and you need to figure out how

to derive value from your data. The overall

goal is to derive benefit from people, pro-cesses, and technology, but the key is to start with people. The human in the loop is al-ways the most important element. Manufac-turing 4.0 is as much about cultural change as it is about moving people away from their original paradigms — and DataOps can help you get there.

IT and operational technology (OT) skill sets are converging to drive business value. Organizations can support this by implement-ing DataOps to improve data analytics, layer-ing on lean manufacturing practices with AI and ML to increase efficiencies and improve quality. DataOps is data management for the AI era -- getting the right data at the right time, to the right person to reduce errors, improve quality, and drive insights, and do it all in a faster and secure environment.

A robust DataOps approach allows your or-ganization to use data in innovative ways, gain a competitive advantage, and, ultimately, mon-etize your data. Rather than simply hiring data scientists to solve every issue, focus on align-ing skill sets to deliver the four Cs of DataOps: connected, curated, contextualized, and cyber-confidential (see the illustration ). 3

These four Cs represent how to manage data and make it available for analytics processing that enables value extraction. Competing on analytics will require challenging past para-digms and encouraging a cultural shift.

Lessons for the Disruptive Decades Ahead

C entral to success with transforma-tive technologies will be your abil-ity to make three critical paradigm

shifts — all of which DataOps makes possible:1. Shifting from a walk-and-look ap-

proach to a look-and-walk approach. In the old paradigm, you would walk the shop floor or the top floor and look for the problems that needed to be fixed. In the new paradigm, insights from data analy-sis trigger investigation opportunities and predict anomalies. Only then do you walk the floor to deal with the problem.

2. Shifting from fixing the broken to bro-kering the fix. The earlier approach was production or operations was paramount and reaction was to failures. When some-thing failed, then you could fix it. Now, ad-vanced analytics allow you to look through data silos to learn what might fail and what preventative actions you might need to ini-tiate to broker the fix. Busting data silos is

what gets the maximum return on data. 3. Shifting from ring-fencing issues to

wringing value from your data. In the old approach, you would leverage data within a silo, ring-fence issues, and solve them. In the new paradigm, your reach extends much further as you wring value from the data across the organiza-tion and the supply-chain to impact the final frontier – the customer experience. It’s the customer- and outcome-cen-tric world that we need to prepare for!

The Right Growth Mindset

T he secret to Manufacturing 4.0 will be the ability to apply AI and ML approaches to solve problems

your team doesn’t even know about. The good news is that the approach to solving the mystery is in plain sight. It just takes the right growth mindset to identify it.

There is an explosion of data around the use and delivery of products and ser-vices. This is the precursor to the forma-tion of the outcome-based economy. To succeed in the coming years, businesses will need an acute focus on their return on data — and that requires agility. Your fu-ture depends on your ability to focus on infrastructure agility, data agility, and, ul-timately, business agility.

Build your organizations DataOps capabil-ity to relentlessly define and prove business value. This is your ultimate goal: to work with everyone, break down silos, and leverage AI and ML to make the most of the wealth of data at your disposal. M

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“The ability to generate insights that can actively empower customers is going to drive the next wave of competitive differentiation.”

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DataOps for Digital Transformation

1 David J. Snowden Mary E. Boone, A Leader’s Frame-work for Decision Making, Harvard Business Review, November 2007, https://hbr.org/2007/11/a-leaders-frame-work-for-decision-making

2 Chris Pemberton, “Create Powerful Customer Experi-ence,” Gartner, May 19, 2019, https://www.gartner.com/en/marketing/insights/articles/create-powerful-customer-experi-ences

3 Sath Rao, Manu-facturing 4.0 – Time for the DataOps Revolution,” Manu-facturing Leader-ship Journal, June 6, 2019, https://www.manufacturing-leadershipcouncil.com/2019/06/06/manufacturing-4-0-time-for-the-dataops-revolu-tion/

“The world is full of obvious things which nobody by any chance ever observes.”

Sherlock Holmes  The Hound of the

Baskervilles (Sir Arthur Conan

Doyle, 1901)

“Data! Data! Data! I can’t make bricks without clay.”

Sherlock Holmes  The Adventure of the

Copper Beeches (Sir Arthur Conan

Doyle, 1892)