Persistent pressure on margins, changing trade relations and supply chain uncertainties are constantly forcing manufacturers to improve their operational efficiency. A crucial role in these improvements is increasingly being attributed to data and analytics, which act as driving forces for change and value creation in the manufacturing industry. The use of data is essential for achieving efficiency and optimizing processes, and this is particularly evident across a wide range of applications.
One specific area where data can play a significant role in improving manufacturers' margins is within the factories themselves. Sensors and other production equipment generate enormous amounts of data, which is often overlooked or discarded if it does not directly contribute to solving a current problem. However, when this data is analyzed and combined over time, it offers considerable opportunities to reduce operational costs.
An example of this is the use of data for predictive maintenance to anticipate the likelihood of machine failures. By leveraging historical data on equipment performance, manufacturers can use real-time sensor data to check for indications of future faults, such as vibrations or excessive heat. In addition, they can look back at previous situations to determine when equipment is in good condition and does not require scheduled maintenance, which can reduce unnecessary interventions.
Predictive analytics can also be deployed to improve customer service, even after products have left the factory. By using insights to predict future failures, manufacturers can alert users in good time for preventive maintenance. Moreover, automatic software updates can ensure that buyers of, for example, cars, security cameras and other smart devices no longer need to make time-consuming supplier visits to resolve problems.
Another important application area for production data is improving safety protocols and meeting compliance requirements. Especially in sectors such as the pharmaceutical and food industries, where quality problems can have serious consequences, accurate and reliable data play a crucial role. Pharmaceutical manufacturers, for instance, can use data to safeguard the safety, efficacy and quality of products, and these insights extend across all business activities, from R&D and clinical trials to revenue optimization.
Sensors, such as smart thermometers, have become indispensable tools for monitoring product status and quality throughout the supply chain, particularly in food production. They enable manufacturers to collect real-time production data and use it to protect products from exposure to hazardous temperature conditions. Despite these capabilities, many companies still struggle with optimally integrating the abundance of sensor data. To be effective, it is essential to combine sensor data with other business data sources, such as ERP and supply chain systems.
One challenge in these optimization efforts is the complex landscape of data storage, with both structured and unstructured data spread across local data centers and the cloud, possibly even across multiple cloud environments. It is concerning that 77% of managers in the field of production data indicate that this complexity makes it harder to extract value from their data. To realize innovations such as predictive maintenance and improvements in safety and compliance, it is essential that manufacturers unify all their available data on a single platform. Without such a data platform, they cannot harness the full potential of their business data.
In today's competitive manufacturing landscape, even a small margin improvement can make the difference between success and failure. Unifying all available data gives manufacturers the opportunity to unlock new value and continue to distinguish themselves from their competitors.