Part 2 of a 2-part series. Read Part 1.
In Part 1 of this series, we looked at how to optimize a decision across multiple variables, using operational downtime as an example. Here, we’ll explore how intelligent technologies can digitally transform economic value curves.
What if 60% of your downtime problems are caused by customers (dropping stuff, getting sick, struggling to get in and out of cabins, etc.) and not mechanical problems? Could you just ban any customer who might cause any of these sorts of problems (disallow cellphones, disallow loose clothing, disallow anyone above a certain height or certain weight or certain BMI)?
Optimizing across n-dimensions drives innovation
In fact, the more dimensions against which you have to optimize, the more creative and innovative your data and analytics-driven solutions will have to be. For example, how about:
- Increasing operational uptime while reducing maintenance costs while improving customer satisfaction, or better yet…
- Increasing operational uptime while reducing maintenance costs while improving customer satisfaction while reducing carbon footprint and emissions, or better yet…
- Increasing operational uptime while reducing maintenance costs while improving customer satisfaction while reducing carbon footprint and emissions while increasing employee job satisfaction.
I think you get the picture: The more dimensions against which you have to optimize, the more opportunities (requirements?) there are for thinking outside the box, for dropping conventional thinking (and limitations) to find ways that can optimize all n-dimensions.
Changing the economic value curve
New technologies are emerging to help organizations digitally transform their economic value curves. However, each of these emerging technologies – robots, Internet of Things, drones, virtual reality, augmented reality – are highly dependent upon analytics such as machine learning, deep learning, and artificial intelligence (AI) that make these emerging technologies smarter than just replicating existing, irrelevant operational process. (That’s just paving the cow path, which a good old Iowa boy like me totally understands.)
Organizations have an opportunity to exploit these emerging technologies to create intelligent products and smart spaces that can self-monitor, self-diagnose, and self-heal. As I covered in the blog “3 Stages of Creating Smart,” the three stages necessary for creating a continuously learning “smart” entity include:
- Self-monitoring: Continuously monitors operations for any unusual behaviors or outcomes (analogy detection, performance degradation, security violations); leverages Internet of Things sensor data to closely monitor key operational processes
- 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; leverages Internet of Things intelligence to predict operational problems and the severity/urgency of those problems
- Self-fix or self-heal: Applies prescriptive analytics to create actionable insights and preventative analytics to recommend user or operator corrective actions to prevent problems such as unplanned operational downtime; leverages robots, drones, augmented reality, and virtual reality to support and accelerate the ability to self-fix (with minimal human intervention required)
Using the economics value curve to drive innovation
So what is innovation?
Innovation is the ability and willingness to constantly challenge conventional business and operational processes and models (willingness and ability to unlearn conventional thinking, heuristics, and rules of thumbs) in order to re-engineer the organization’s value creation and capture processes using strategic, actionable, and material customer, product, and operational insights.
Economics, data science, design thinking… wow, now we’re starting to have some real fun!
This blog originally appeared on LinkedIn and is republished by permission.
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