Three Ways to Use Data Intelligently for Greater Efficiency and Sustainabilityby Brian Shepherd on July 3, 2019 From Technology, Thought Leader
Digital transformation is a big topic today among manufacturing thought leaders, and data is often at the centre of these discussions.
Data is key to enabling unprecedented levels of manufacturing productivity, but we need to remember one thing: not all data is created equal.
IDC predicts that 163 zettabytes of data will be created per year worldwide by 2025. But it’s expected that less than one percent of that data will be stored. This will be due in part to limitations of storage infrastructures, but mostly it’s because we simply don’t need all this data.
Any manufacturer should always ask two questions about the data they create:
- What data do we need?
- How do we use this data productively?
This blog is about helping to answer these questions.
Whether you’re looking to begin the Industry 4.0 journey or taking steps to start using actionable information more effectively, here are three ways data can be used intelligently to cut manufacturing cycle times, enhance products, and drive sustainability.
1. Connect Data Throughout the Product Lifecycle
Siloed departments are a major productivity block. In many manufacturing organisations, design and engineering, production, and quality are not communicating as effectively as they could be. Data produced by these different functions becomes siloed, causing missed opportunities to drive greater process efficiency, improve products, and reduce resource wastage.
Consolidating data infrastructures is a good place to start, or at the very least integrating the multiple management systems in use. More advanced organisations are using cloud-based data handling. Not only does this provide an infrastructure capable of handling big data, it also offers the visibility and accessibility required to ensure all necessary stakeholders get the data they need.
Connecting systems through the Internet of Things (IoT) enables real-time insights into different departmental operations, driving greater collaboration and quick response decision-making. Among the plethora of data created by the different departments, big data analytics ensures you’re quickly identifying patterns in the relevant data and are able to capitalise on the right opportunities/quickly identify the most crucial areas for improvement.
With IoT, machines not only interact to share data but can alert users of crucial real-time information, for example if the machine is in need of maintenance or it is operating outside of prescribed parameters (like temperature range). With machine learning, systems can self-regulate and fix themselves without the need for operator intervention.
Such connectivity makes data analysis much more effective. With an holistic picture of the product lifecycle, you can see where bottlenecks are appearing in the process and how different operations might be interacting ineffectively. What’s more, with greater connections comes more detailed simulations. Whether you’re creating digital twins or simulating the end-to-end process, this can provide game-changing levels of insight to predict outcomes and drive efficiencies.
2. Build Data into Your Aftermarket
Before the advent of Industry 4.0 technologies, it was very difficult for manufacturers to keep track of a product once it had been sold. Aftermarket services relied heavily on obtaining a customer’s perception of the product’s condition and quality. Manufacturers can now equip the products themselves with IoT sensors to deliver information that will enhance customer support capabilities.
This could simply involve remotely monitoring a machine to know when maintenance or an upgrade is required. But more mature operations will enable machine learning, with systems able to identify when they need repair work.
Clearly, data insights such as these present a clear opportunity for manufacturers to evolve their support services and enhance customer loyalty to their business.
3. Increase Data Visibility in the Supply Chain
For large manufacturers with suppliers all-round the globe, uncertainty in supply chain management can be a major problem. Your internal teams and major vendors might give you good insight into pain points, but it’s delving into the data that will enable you to make connections across the supply chain, track any interacting issues, and hone in on what’s most significantly impacting the bottom line.
Rather than exhaustive communication via web or phone, the combination of IoT and big data allows for automated end-to-end monitoring of products from creation through shipping. And the typical logistical blind spots during shipping are removed. Instead of relying on reports from key checkpoints during the journey, data can be exchanged in real-time to help manufacturers manage inventory and track location, enabling them to take fast proactive measures if there are potential delays. Sophisticated sensor technology can also provide insight into the condition of the goods themselves.
This is a level of data connectedness that goes beyond typical current infrastructures, with potentially thousands of supply chain touch points for large organisations. It will take a good deal of integration and strong governance to reap the benefits of the actionable information available here, but if achieved the levels of efficiency and waste reduction would be game-changing.
Clearly, optimised data usage presents incredible opportunities for manufacturers. And as Hexagon President & CEO Ola Rollén discussed in his HxGN LIVE 2019 keynote 'Your Data Can Save the World', there are opportunities to increase efficiency and reduce waste in ways that are beneficial to both business and the planet. Watch the keynote below to see how data is key to scalable sustainability and why this is so important for the future of business and our environment.
Brian Shepherd has the responsibility for the coordinated strategies and synergies across the application software portfolio of Hexagon Manufacturing Intelligence, addressing the lifecycle quality initiatives for discrete manufacturers operating in the Industry 4.0 era. Previously, Brian held several different roles at PTC for over 20 years including EVP of Software Development and EVP of Enterprise Segments.