Let’s face it, every industry faces numerous challenges throughout the year. Budget issues, resource constraints, new barriers to enter new markets, political, environmental; you name it, they exist. To top that all off, if you do any searches on the internet, you’ll see different top 5 or top 10 challenges by industry. It’s not an exact science, but experts and those that support the various industries do see trends and patterns pointing to various directions. Manufacturers that leverage the latest in technology have a better chance of overcoming the challenges they’ll face this year and the years to come.
While on the subject of technology, let’s talk about data, specifically data from machines. Yes, I’m talking about the Internet of Things (IoT) and the digital transformation age. Unless you are new to the workforce, or haven’t had any connection to the digital world, digital transformation is real, it’s here and it’s not going any where any time soon. Why do I say that you ask. Let’s look at some statistics in the world of manufacturing as it relates to the digital transformation era:
Enhancing the customer experience
- 76% of manufacturers surveyed indicated IoT increases insight into customer preferences and future behaviors.
- 40% of industrial manufacturers use digital technologies to monitor products sold to their customers
- 66% of manufacturers say IOT creates a competitive advantage and allows them to bring to market better products to meet customer needs and demand
- 35% of manufacturers currently collect and use data generated by smart sensors to enhance their manufacturing processes
Increases in efficiency and reliability
- 66% of manufacturers use IoT to measure risks, protect assets, and improve safety in their plants
- 61% of manufacturers are improving reliability or performance of their products with IoT
According to Gartner, IoT is becoming so big and so prevalent, that more than $1.5 Billion will be spent worldwide, by the end of 2018, on IoT Security. In addition, IHS indicates over a 200% growth in the IoT install base between 2018 and 2025.
Ok, you’re saying you get it and you’re seeing all of this already in the marketplace. What does this mean and why is it a challenge? It’s a challenge for a variety of reasons. One of the major ones, in my opinion (and you can certainly disagree if you’d like) is this: If you’ve been slow to adopt IoT for your manufacturing processes, chances are someone else, aka a competitor or ten, is already doing so and implementing process or other improvements to deliver a better product in a more reliable and efficient process. If they do this long enough and you continue to lag behind, your share of the pie may become smaller and smaller.
Now, let’s turn this challenge into an opportunities. Why implement IoT? What’s in it for me and my organization and manufacturing processes? I see three main reasons:
- Improve Efficiency: connect disparate assets to automate/improve business processes, monitor & track asset health, collect & secure data from assets for analysis
- Enable Innovation: analyze data in near real-time, apply historical data to new issues to predict trends, create operational intelligence to improve decision making
- Business Transformation: convert raw data to actionable insights, create insights for the right people, leverage analytics to create new business models/revenue streams
Let’s take a scenario about how you can improve efficiency through asset monitoring (with IoT and Connected Field Service within Dynamics 365) connecting assets, monitoring and managing them. Once connected, you can use remote telemetry (sensor info, alarms, status events) to stream that data to Azure Event Hubs. From there, Azure Stream Analytics would pull that data for analysis. This can then be used to possibly fix remotely or alert for scheduling to get a technician onsite to fix.
What now you may ask? A product team can exam all the data, using Microsoft Power BI. With this data, key company team members can identify risks and opportunities. The product team can work on updates that can be delivered remotely for products in the field or use these insights to design the next generation of products.
To take this a step further, your organization could use Azure Machine Learning to predict equipment issues by comparing incoming data with historical data to determine when a piece of equipment may fail. With Azure Machine Learning, data scientists can create an appropriate model, deploy it and then compare the live data against it and solve for a variety of scenarios, such as predictive maintenance.
There are many challenges in the manufacturing world. With these challenges come opportunities to transform your business and take advantage of all that IoT has to offer in this digital transformation age.