Do you remember when a typical tire plant could confidently run the same tire types for days – or weeks? Changeovers could take hours. But because it was an occasional and predictable event, changeover did not dramatically impact productivity.
I don’t need to tell you those days are long gone. As a tire manufacturer, you must produce more SKU variations than ever to meet the evolving demands of automotive manufacturers. At the same time, the tire aftermarket expects minimal lead times – and reliable supply across your distribution network.
To boost production agility and meet contemporary challenges, tiremakers across the board are turning to digital technology. But exactly how to approach digitalization can be a daunting task for cost-conscious producers with an extensive installed base of legacy equipment.
There’s Not a Single Path to Digitalization
In my work as an automotive and tire industry consultant, I have learned that exactly how a particular tire manufacturer begins their digitalization journey is as unique as the challenges within their plants. Here are the key questions they are asking:
- Where are the bottlenecks?
- Where and how can digital initiatives make the biggest impact – given the ever-present constraints of time and available funding
When it comes to managing complexity – and responding to market demands – a manufacturing execution system (MES) checks all the boxes. An MES can orchestrate and manage your manufacturing process from raw materials to mixing, prep, assembly, curing, finish, test and warehousing.
When you install a platform MES, you can expect significant benefits throughout plant operations. An MES schedules and orchestrates the production of every tire – including the machine set-up and material requirements – based on each product’s engineering master data. An MES provides comprehensive control, sequencing, monitoring, traceability, alerts and documentation – throughout the entire manufacturing process. An MES enforces build standards and work instructions – and allows managers to make dynamic workforce assignments based on skillset and actual production needs.
This level of control and organization significantly reduces expired stock, work in progress, and scrap product in any given plant. And an MES across your entire manufacturing footprint improves overall agility and gives you the control to change production schedules quickly and efficiently to meet market demands and improve overall operating efficiency (OEE).
But while a platform MES is critical for sustainable end-to-end optimization, moving forward can prove to be a challenging decision for some tire manufacturers. Frankly, implementing a comprehensive MES requires an enterprise commitment to change – plus determination to break down organizational barriers and certain operational processes. It can be a significant investment, especially for existing plants that have developed and grown without consistent structure over time.
That’s why the path to digital tire often begins more simply – with initiatives focused on applying scalable Internet of Things (IoT) technology to optimize specific machine center performance.
Scalable IoT Technology Starts with Visualization & Analytics
Tire manufacturing is certainly a complex process. But with many similar machine types, tire manufacturers can gain significant incremental value from optimization efforts targeted on bottlenecks.
While people interacting with the machines day in and day out have good instinct into where those bottlenecks are, pinpointing issues is difficult when real-time machine performance visibility is limited. In addition, many machine centers are a combination of newer and legacy equipment – with varying levels of automation and performance visibility.
So how do you know which equipment is meeting designed cycle times – or which operators are falling behind? How can you view the operation of the machine center as a whole and determine where bottlenecks actually are – and how to optimize performance?
To improve process visibility, many tire manufacturers focus IoT initiatives first on scalable analytics software that can collect and consolidate data from many disparate sources and place it in meaningful context.
Basic analytics turns raw data into the kind of reporting and dashboards that can help you identify performance issues and make informed decisions. Specifically, this will help you answer the questions: “What happened?” and “Why did it happen?”
With a data science approach, you can extend these capabilities to predictive and prescriptive analytics – which help you answer the questions: “When will that happen again?” and “What can I do to avoid that?” These advanced analytics often use machine learning and artificial intelligence to deliver extraordinary insight – and performance gains:
- Predictive analytics recognize and learn patterns in the data that precede equipment failures or production anomalies – and can alert personnel to investigate or perform maintenance proactively.
- Prescriptive analytics not only predict a likely issue, but also prescribe actions to avoid downtime, improve cycle time or help ensure product quality.
A “machine learning control” strategy takes the power of prescriptive analytics one step further. Prescribed actions are tied directly to the control system – and executed automatically in real time to automate machine optimization.
Stepping Up to Digital Twin
Digital Twin software is another way to add scalable value to your operations. A digital twin is a virtual replica of a physical asset that can mimic asset performance.
A digital twin delivers value throughout the lifecycle – beginning with machine designs that can be tested and commissioned virtually before deploying on site.
Using a digital twin, you can train your workforce on the virtual machine so they can operate and maintain the equipment on Day One. And with a digital twin, you can also test “what-if” scenarios in the virtual environment – and determine how changes in the process might affect machine performance and throughput.
Keeping It Scalable, Supportable & Sustainable
Starting your journey to digital tire production with focused IoT solutions is one thing. But maintaining and building on your success is another.
How can you cost-effectively move toward a fully integrated, digitally transformed company? Here are a few things to keep in mind:
- Vision and leadership. Too often, a company starts down the digitalization path without a vision or cohesive leadership at the corporate level. The result is often multiple, uncoordinated initiatives that are not tied to a business case – and are difficult to evaluate and extend.
- Scalability. To prove its value, an IoT pilot project cannot be too narrowly focused. It must easily scale to other machines, processes and plants.
- Supportable and Sustainable. Investing in a supportable, commercial off-the-shelf IoT platform will pay dividends for years to come. “Homegrown” digital solutions developed by in-house personnel might initially address an issue. But keeping people on staff who can support, sustain and extend that solution for the long term can prove challenging – and costly.
Learn more about how you can create a cost-effective successful roadmap to digital tire. No matter where you are on your digital journey, we can help.
Bill Sarver, Automotive & Tire Industry Senior Consultant, contributed to this blog post.
Published April 5, 2021
