Getting There Starts With A Focus On Outcomes
If something broke, fix it. But be aware that the process can be a complex journey. The good news is that process improvement is well understood.
The bad news is that it has been dependent on data quality as well as people- and time-intensive.
The Future Is Prescriptive Applications
Prescriptive applications combine 4-components:
- Knowing: notifying that something is broke, about to break or needs repair
- Doing: taking repair action
- Measuring: quantifying the success or failure of the repair action
- Learning: providing guidance for future events based on prior outcomes
- Service history
- Warranty and recalls
- Build details
These siloed applications results in poor data quality. But other critical information is still missing. It’s in faxes, emails, handwritten forms, and manual data entry.
Effective analytics requires high quality data. Capturing information (doing) in a structured format is key:
- Why was it done (what fault codes were active, was it scheduled or unscheduled, etc.)?
- When and where was it done?
- Who did the work?
- What was done?
- How long did it take?
- How much did it cost?
- What was the outcome?
Unfortunately lack of standards creates further data quality challenges. But that’s a topic for a later date.
With work completed, what was the outcome?
Sometimes the outcome of a repair event are not immediately known.
- Repeat repairs are a common problem in many industries.
- Invoices with detailed technician stories are late.
- Warranty filings may take even longer.
Ultimately, you need all the information. What worked, what didn’t, etc.
This allows you to measure outcomes:
- Diagnosis and Repair Effectiveness (actions and outcomes)
- Diagnosis and Repair Efficiency (time to understand/fix the problem)
- Total Costs of Repair (Parts, labor as well as other related costs – rental, downtime, etc.)
The use of technology to help businesses make better decisions about how to handle specific situations by factoring in knowledge of possible situations, available resources, what has happened in the past, and what is happening in the present. Prescriptive analytics works with predictive analytics — the use of statistics and modeling to determine future performance based on current and historical data — to improve business decisions despite uncertainty and changing conditions, and to help companies determine what action to take.
Let’s Tie It All Together
- Captures all the data
- Manages outcome preferences
- Proactively notifies key team/ecosystem members
- Presents optimal actions
- Manages process
- Measures outcomes
Nirvana or Pipe Dream?
Artificial intelligence and machine learning technologies are improving. Analytics have graduated to being prescriptive. But a gap still remains.
How do we leverage these insights to directly affect outcomes? While IIoT faces many challenges, could Prescriptive Applications be a key missing piece?