Driving Long-Term Success with I-IoT

Introduction

What is the best way to drive long-term success with the industrial internet of things? It usually is not the technology that fails during these implementations. There may be some challenges there, but the systems largely do what they say they will do. When the implementations fail to meet expectations, it is normally because the solution does not get wide-spread adoption and use within a company.

This article will answer how to drive long-term success in two sections. First, we will walk through of some key success factors for the prime use cases for I-IoT. Next, we will cover how to get wide-spread adoption within the organization. This is not meant to be a comprehensive list - these are key findings from some of our most successful projects over the years.

You can also watch the recorded webinar on the VDI YouTube channel. In this video, I was joined by Rob Simmons from Sandalwood to discuss some of the best ways to get internal and external assistance with the topics listed below.

Starting Point for Today’s Discussion

As a starting point for today’s conversation, we’re going to assume that you have some sort of implementation underway. You may be just dipping your toe in the water with a pilot program. Or you might have an implementation that you’ve done in the past but stalled because it didn’t get adoption or didn’t drive the value management was looking for. Or you might have an implementation that’s working, but you’re simply trying to extend the success even further.

If you haven’t started yet – that’s okay. There’s still value in today’s discussion. But you should also go back to our article on “Getting Started with Industry 4.0” for more about how to pick the right projects, build a business case, and so forth.

Prioritizing Use Cases

The first topic to cover today is how to prioritize the numerous potential uses cases there are for IoT on the shop floor.

Start with Value

Smart Factory solutions can have a tremendous impact across the entire P&L statement. I won’t get into the details here, as I’ve covered that in other articles. Identifying the potential value is a key strength of Visual Decisions – if you would like assistance with this, please reach out to us.

The key point for today is to identify where the key opportunities are for driving value in your operations, and what needs to be implemented to achieve that value. Once that is known, execute those plans to achieve that value. With those financial gains in the books, that can pay for further expansion.

Focus on Enabling People

Another key when deciding which use cases to pursue is to focus on enabling people or augmenting their capabilities.

One area of focus should be to change how problem-solving is done within your business. If you can improve some of these fundamental capabilities of your people, then there is really an unlimited opportunity within your company. This can be done by helping your people become more data-driven and training them to utilize all of the information available to them. Digital Transformation projects are an ideal chance to enact changes like this within the organization.

Another key enabler is to focus on standard work. The standard work for the operators should be looked at, but also the standard work for leaders and all the support staff within the organization, as well. This is one of those primary levers to create the data-driven organization in the first place. Having a common approach to how work should be done helps new workers get up to speed faster and increases the overall competence within the organization.

Finally, this leads me to an example of why it is important to connect everything in an IoT implementation. One of my customers implemented IoT monitoring on a single asset as a trial, with management saying that if performance on that machine increased then they would expand the usage of the solution across the factory. But since it was only on a single machine, nobody changed how they went about their work. Quality didn’t change the way they did their jobs to look at the IoT data, because it was only available on that machine. Process engineering didn’t either. Neither did maintenance. In the end, since nobody was using the data, the machine performance didn’t improve – even though the IoT system was performing perfectly. Once I helped plant management see what was happening and was able to use the information from the machine to point out several things that could be done right away to improve performance, they moved forward with expansion.

This type of mindset is what causes “pilot purgatory” in many companies. These systems work and largely do what the vendors claim they can do. But the adoption has to be there and people have to use the data in their daily jobs in order to get the improvements you seek.

Orient People Towards Action

That leads me to my next big point – people have to take action to make things better. Having great data in pretty charts and graphs and sophisticated machine learnings models does no good until people act on that information.

The analogy I use all the time is a Fitbit. I have been wearing a Fitbit for over seven years now. I will admit that I have not lost any weight during that time. It is not that the Fitbit doesn't work. It works great – it tracks all my steps and shows me all the pretty charts and graphs. But I have not lost weight because I have not changed my behaviors.

If people are not invested in making changes and committed to taking action on the data, then they will not remove the waste. This is quite literally the case with my Fitbit (well, waist, in that case).

Connect Across Systems and Processes

One last bit about the use cases:?Look to break down barriers between departments and systems with these solutions.

Improve analysis within manufacturing by including information from the greater context. For example, being able to show the cost or revenue implications of downtime or other events on the floor can change the ways that people react and overall behaviors

Automate workflows to orient towards action – I talked earlier about creating an orientation towards action. One way to do this is by automating certain tasks, or at least the beginning of those tasks. For example, one place this can be done is to automatically create a work ticket in the maintenance system when a production machine goes down. The ticket can even be populated with information from the machine showing the nature of the fault.

Analyze business outcomes across departments and systems – an example here would be looking at items that end up in warranty. By combining the warranty information with the IoT data from the shop floor, it is possible to identify correlations with manufacturing processes and which items end up in warranty.

Use the data from manufacturing to improve other areas – finally, use the information from the shop floor to enhance other systems and processes. One of my favorite examples here is to create a closed loop between the shop floor data and the planning and scheduling function. This can have tremendous benefits for the company when done correctly.

Driving Adoption

Next, I’ll focus on how to drive adoption of these systems within your organization

Gaining Cultural Adoption

The basics of cultural adoption are the same as they are for the implementation of any new technology, but IoT offers some unique methods of adoption that I’ll get to in a bit. For now, let’s cover the basics.

Fundamental change management applies here as much as in any project. If you’re looking for a reference that handles the basics well, “Leading Change” by John Kotter works here.

Building habits within the organization is another big key. BJ Fogg’s model on Tiny Habits is fantastic and highly recommended for everyone. Fundamentally his equation for behavior is “Behavior = Motivation + Ability + Prompt”. One of the unique features of IoT is the ability to both increase or augment the ability of people and to set up prompts for desired behaviors.

Overcoming “Big Brother” is a key concern in many of these projects. You have to know that people love visibility – downwards. Let people know that the system is not going to be used to beat them up – it’s going to be used to make their life easier.

Finally, have people participate throughout the implementation to get their buy-in on the solution.

Skill Up the Workforce

Implementing these systems will require new skills within the workforce. This can be done by training your existing staff, hiring people from outside the organization, or bringing in outside help. My strong recommendation for the implementation is to bring in some outside help to get things up and running in the first place. Work with a team that has “been there and done that” to get those initial successes under your belt. During this same time, have people that will own the system over the long-term work with that outside team to develop their ability to take ownership and run with the solution so that you do not have to pay the consultants forever.

Just as important as it is to skill up that core team, though, is to skill up the people that will use the information from the system. Don’t forget to use this opportunity to train the rest of your staff on how to use the data from these systems – and to use data in general – to improve problem-solving, root cause analysis, and more.

Data-Driven Processes

I did a webinar just a few weeks ago on the digital transformation of operational excellence, so I won’t dive into the details here. But one way to drive adoption of these systems is to change the structure of your existing operational excellence program to have data at its core. This is probably the single biggest leverage point to drive adoption of IoT within your company. Please reach out to me for more details or watch for my white paper on the topic on LinkedIn that will come out this Friday.

Clean Data

Just a quick note on having clean data here: people will not use a system if they don’t trust the data.

Be sure to implement robust data validation strategies – not just for initially commissioning the system, but for ongoing data validation. This is a very easy way to lose the people in your organization. Fortunately, it is also a fairly easy problem to avoid. Continuously validate the data in the system – and make that visible so that nobody can doubt the veracity of the information.

People won’t adopt a system they can’t trust

• Data Validation:
• Outlier Analysis
• Watching the watchman
• Missing Data
• Reason Trees:
• Automation & mapping
• FMEA Overlap

Visual Controls

I’ll end this section with one of my favorite phases I’ve heard from a customer, “Make the ugly visible.”

The idea is that nothing will get improved if the problems are hidden behind the curtain or swept under the rug. Put visual controls in place to highlight exactly what needs to be done and who needs to do it. This is one way to create that “orientation towards action” that was discussed earlier. It also helps to make the system “real” for everyone since they see it every day.

Just be sure that the information doesn’t fade into the background. It isn’t enough to put a big screen TV with data on the shop floor. Build your shift change meeting around that information. Have the supervisors make it part of their daily work to review that information with the operators. Incorporate the data into management’s Gemba walks.

Make it ubiquitous, make it visual, and make it part of everyone’s daily routine.

Conclusion

That's it for today. Please leave a comment with other key success factors you've seen in your projects.

Thanks!