Data plays a pivotal role in today’s ultra-connected business environments. When properly exploited, it’s the source of one or several competitive advantages — e.g., leaner operations, superior customer experience, higher loyalty, and better-tailored products. In fact, a McKinsey survey shows that customer-analytics champions vs. laggards are almost 19 times more likely to reach above-average profitability.
In light of these benefits, why aren’t all companies becoming entirely data-driven? Well, while the rationale for ubiquitous data exchange and processing is straightforward, getting things to work in practice is a far more complex undertaking.
In the case of machine manufacturing, data collection is often laborious, supply chain players are disconnected, and it’s not always clear how to break down IT silos dispersed geographically.
However, the positive impact data can have in the machine industry is just too large to be ignored. In this blog post, we take a closer look at the challenges manufacturing companies face to integrate and make sense of data, as well as what they can accomplish once all pieces finally fit together.
Barriers towards data transparency
It’s hard to keep track of what’s happening after machines move from the producers to the customers’ hands. How is the overall machine health? What are the most frequent anomalies of a specific model? Is changing supplier for a given part a good idea? Are customers even using the machines they bought?
Manufacturers typically end up out of the loop with little to no understanding of how their products perform in real-world settings. This lack of transparency makes it difficult to identify design opportunities, create and test new features, and, ultimately, achieve competitive differentiation.
Additionally, heavy machinery such as cranes and big saws are put under a lot of stress due to conditions that are outside of manufacturers’ control. Factors such as wind, temperature, and humidity not only directly affect individual assets but are also unpredictable across working environments. This generates a lot of statistical variance that cannot be accounted for, and, therefore, inhibit accurate performance modeling.
Unleashing the potential of machine manufacturing
The good news is that advances in the Industrial Internet of Things (IIoT) — including edge computing, smart data collection, and analytics dashboard — are progressively making data more accessible to machine manufacturing companies, notably enabling them to:
- Be more responsive to customer needs Once manufacturers can keep an eye on machines, they can start anticipating customer needs and making just-in-time commercial proposals. For instance, sales teams can contact clients whose equipment is reaching the end of its useful life with an offer to upgrade. Or service managers may already plan maintenance based on actual machine usage.
- Help customers become more productive Inefficiency is a big concern for machine buyers. A piece of equipment that does not operate at full capacity can slow down entire production processes and reduce throughput. Data-driven organizations can minimize downtime by monitoring machine health and assist local service teams in tackling issues before they result in machinery breakdowns and expensive repairs.
- Innovate and offer better products and services By combining data from multiple assets and clients across circumstances, machine builders are in a position to make design decisions and balance over-specification and performance reduction — improving value for money and tailoring products according to different contexts. Additionally, data transparency allows providers to explore new business models like Machine as a Service.
While the impact of data in machine manufacturing is substantial, several barriers prevent machine builders from becoming more data-driven. Senseforce solutions can help you overcome these challenges and position your organization at the forefront of Industry 4.0. Would you like to see how? Contact us today at [email protected] to schedule a demo.