In manufacturing IoT, we identify 4 steps toward a successful digital transformation. Step 1, ask and envision. Step 2, experiment. Step 3, small scale.
Manufacturers have been optimizing production processes and products for decades, and the conventional routes are all but maxed out. But there’s a new lever to pull. Manufacturers can now leverage intelligence from machines and from the human-machine interface, to do things differently – process transformation – and do different things – product and service transformation.
Here I outline four steps toward a successful digital transformation.
What does a digital future look like for our organization?
What are the business cases for transformations? Look at potential projects in production and product.
How will we get there?
What cultural changes will the company need to make?
Where’s the low-hanging fruit? Quick wins can help secure funding for further initiatives.
Experiment with projects that will bring clear results and benefits, even if some parts begin manual or have workarounds.
Talk to technical partners to ensure your plan is realistic, to avoid wasted effort.
Get input from customers.
Take on a lab mindset, testing, re-hypothesizing.
It’s essential to see how the initiative works in the real world. Move experiments to small-scale production. What needs fixing? What can you refine? Show the value.
Which projects will you move to the production environment, and how? What technology, infrastructure, and skills do you need? What will the costs be?
Minimize planned and unplanned downtime of your equipment, with condition based management.
Identify patterns, anomalies, and causes of events, with analytics across variables such as speed, acceleration, displacement, noise level, vibration, torque, and temperature.
Use this data to predict failure and optimize maintenance planning. Find the roots of the problems and continually make improvements.
In addition to cutting downtime, manufacturers have seen maintenance gains in parts and labor in the region of 30% as compared with reactive maintenance.
Define what your customers need in terms of the benefits they want, not the box they get. For example, instead of ‘HVAC equipment’, think in terms of ‘quality air’.
Change your product charging model to a service-based one. Your customers will now pay for what they actually want – quality air, uptime, and quality of service – instead of boxes. This shift from CapEx to OpEx for your customers will mean regular, ongoing payments for you.
Risk is transferred to you – good for your customer, clearly. The data from your equipment on your customers’ premises can help you mitigate the risk. Detect before things break, introduce predictive maintenance, prevent bigger, more costly problems.
The data and learnings you’ll get will help you design smarter products and better advise people on how to use and benefit from them.
Monitor equipment and production remotely, analyze, and improve processes.
Receive real-time notifications of predicted production bottlenecks.
Implement condition-based maintenance.
Leverage AI and ML to improve operations, quality of products, and bring cost savings.
Analyze product usage data to continually improve products and service.
Implement condition-based servicing of customers’ products.
Add new products based on customer data.
Monitor inventory and orders to optimize the supply chain.
Reimagine production and products to see where you will go in the long term.
plgd makes it simpler to build a successful IoT initiative – to create a proof of concept, evaluate, optimize, and scale.
