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A Survey of Manufacturing Decision Makers |
NEWS |
ABI Research recently completed its “Manufacturers’ Technology Adoption & Attitudes Survey” of 450 decision makers in the United States, Germany, and Malaysia. What follows are the key findings and takeaways related to manufacturing data analytics.
Many Companies Will Spend up to 1 Year Defining Overall Equipment Effectiveness (OEE) for Their Business |
IMPACT |
Research interviews and client inquiry point to three main reasons for data analytics initiatives: 1) to drive efficiency gains, 2) to address for regulatory and compliance requirements, and 3) to support Artificial Intelligence (AI) initiatives (general & generative). Process industry users focus heavily on improving the efficiency of machines to avoid yield loss, while discrete operations are challenged to pay extra attention to product quality root cause analysis throughout tiered production networks.
Most companies are devising implementation programs for data analytics at the plant manager and Information Technology (IT) levels; however, senior management continues to evaluate suppliers for both production line and equipment-focused analytics applications (e.g., predictive maintenance).
Survey results indicate a commensurate maturity around specific use cases with 67% of respondents able to collect and analyze data in near real time, and about the same (60%) able to collect and normalize data from assets/equipment. More advanced use cases have started to take hold with 33% of manufacturers performing prescriptive analytics, yet there is still a way to go for most users.
Data analytics solutions are also trending to be more comprehensive with the majority (73%) of companies leveraging Application Programming Interfaces (APIs) for deeper integrations. As one example, 55% of users can leverage data analytics to quickly identify the root cause of an issue, although the same data means about 50% of users still need to develop the capability. Interestingly, large organizations (>25,000 employees) are farther ahead in their ability to perform root cause analysis of an issue than collecting and normalizing data from assets/equipment (it is the inverse for smaller organizations).
Predictive analytics applications, which precede prescriptive use cases (e.g., because of X, which is predicted to happen, do Y), are another area of great progress in the last few years with 50% of companies able to perform such tasks.
Data Engineering as the Next Frontier |
RECOMMENDATIONS |
To achieve more advanced and contextualized use cases that scale well across production networks, there is a good-sized cohort (25% of respondents) focused on protocol conversion and industrial DataOps for improving quality in the next 12 months. Companies that enable such capabilities include Litmus Automation and PTC (Kepware) for the former and HighByte and Crosser for the latter.
There is also a significant base (45% of respondents) investing in digital threads that link product designers, manufacturing engineers, and production engineers. These more wholistic digital thread solutions are best filled by End-to-End (E2E) industrial software providers, including Siemens, Rockwell Automation, and Dassault Systèmes, while use case-specific vendors, such as Hexagon for quality-based digital threads, remain relevant for certain functional areas.
Some firms may face alignment issues, as most (69%) senior managers believe that the potential of data analytics is not fully appreciated by staff. Data analytics initiatives are also impeded by lacking sufficient time and expertise to evaluate and execute implementations, which are the top concerns among respondents.
For vendor selection and group buy-in, survey results indicate it is important that a vendor has a robust onboarding process with training, resources, post-sales follow-up, etc.; the solution is underpinned by AI; and leadership has knowledge of the solution.
There is currently a big focus in the industry on minimizing the movement and storage of unnecessary data, mainly by structuring unstructured data and filtering data in motion, to facilitate lower data storage costs to Amazon Web Services (AWS), Microsoft, Google, and IBM. Transforming data in motion is one good way to ensure it is better optimized at rest for faster recall and analytics, and the survey evidences enthusiasm for the potential of data analytics, but it’s an ecosystem and increased skills are required on the buyer side.
