How To Flourish In Industry 4.0, The Fourth Industrial Revolution (Part 2)

Bill Schmarzo

Part 2 of a 2-part series. Read Part 1.

Tomorrow’s market winners will win with the smartest products. It’s not enough to just build insanely great products; winners must have the smartest products! But what are smart products?

“Smart” is one of those overused, poorly defined terms that float around conversations about the Internet of Things (IoT) and artificial intelligence (AI). What do we actually mean by smart? For purposes of my teaching, I define “smart” as the sum of the decisions (optimized) in support of an entity’s business or operational objectives.

Organizations need to make the necessary investment (in techniques like stakeholder personas, stakeholder mapping, customer journey maps, and prioritization matrix) in order to identify, validate, value, and prioritize the decisions or use cases that comprise a “smart” entity. For example, Figure 1 outlines some of the decisions or use cases that a city would need to optimize with respect to a “smart city” initiative.

Figure 1: Decisions for a “Smart City” Initiative

See the blog “Internet of Things: Getting from Connected to Smart” for more details on creating “smart” entities.

Once we have identified, validated, valued, and prioritized the decisions or use cases needed to optimize our “smart” products and/or spaces initiative, we now need to develop the data management and analytics strategy to “operationalize smart.” That is, we want to create smart products and/or spaces that can self-monitor, self-diagnose, and self-heal (see Figure 2).

Figure 2: The Three Stages of Creating a “Smart” Entity

The three stages of a continuously learning “smart” entity are:

  • Self-monitoring: Continuously monitors operations for any unusual behaviors or outcomes (analogy detection, performance degradation).
  • Self-diagnosis: Leverages diagnostic analytics to identify the variables and metrics that might be impacting performance and predictive analytics to predict what is likely to happen and when it is likely to happen.
  • Self-heal: Applies prescriptive analytics to create actionable insights and preventative analytics to recommend corrective actions for the user or operator to prevent problems such as unplanned operational downtime.

Preparing for Industry 4.0

There is much hard work that organizations need to do to prepare for Industry 4.0, including:

  1. Begin with an end in mind. Understand your organization’s key business initiatives. Understand what’s important to the organization from a business, financial, and/or customer perspective, and use that to frame and accelerate the monetization of these technologies. While we may not understand the technology journey we’ll experience trying to reach that end, the endpoint should not be a mystery.
  1. Understand the key Industry 4.0 technology capabilities … but within a business framework. It’s important for IT to gain familiarization with how these technologies work, what’s required to support them, and what sorts of business and/or operational opportunities can potentially be addressed with them.
  1. Build out the solution architecture. Organizations should embrace a holistic architecture that supports these technologies in order to deliver “intelligent” industrial applications (that get smarter with every customer interaction) and “smart” entities (that leverage edge-to-core IoT analytics to create “continuously learning” entities).
  1. Use design thinking to drive AI organizational alignment and adoption. Embrace design thinking as a way to drive organizational alignment and adoption with respect to where and how these technologies can be best deployed to drive meaningful business and operational value.
  1. Build out the organization’s data and analytics capabilities. Become expert at acquiring, integrating, cleansing, enriching, protecting, and analyzing/mining the data that is the source of customer, product, and operational insights that power the organization’s top-priority business initiatives.
  1. Operationalize the analytic insights. Embed analytic insights and evidence-based recommendations into smart products and spaces that learn from each customer and/or operational interaction.
  1. Monetize the IoT edge. Leverage the IoT edge to enable near-real-time operational and product-performance optimization that further enhances business decision-making and extends more value to customers and operations.

As I noted in the first part of this series: It’s the right time to be in the data and analytics business, and I think even Forrest Gump would agree.

This article originally appeared on LinkedIn and is republished by permission.


Bill Schmarzo

About Bill Schmarzo

Bill Schmarzo is CTO, IoT and Analytics at Hitachi Vantara. Bill drives Hitachi Vantara’s “co-creation” efforts with select customers to leverage IoT and analytics to power digital business transformations. Bill is an avid blogger and frequent speaker on the application of big data and advanced analytics to drive an organization’s key business initiatives. Bill authored a series of articles on analytic applications, and is on the faculty of TDWI teaching a course on "Thinking Like A Data Scientist." Bill is the author of “Big Data: Understanding How Data Powers Big Business” and "Big Data MBA: Driving Business Strategies with Data Science." Bill is also an Executive Fellow at the University of San Francisco School of Management, and Honorary Professor at NUI Galway at NUI Galway J.E. Cairnes School of Business & Economics.