Digital transformation is the biggest trend of the last few years, and it’s not going to end anytime soon. In order to stay competitive in the market, massively manufacturing companies are trying and implementing modern technologies, moving away from the traditional factory approach to the concept of Industry 4.0 with the help of IIoT. At the same time, we are seeing a surge of interest in artificial intelligence (AI) in the industry. The use of AI-based analytics to optimize the performance of manufacturing plants is becoming an obvious solution. However, according to a Gartner report, 85% of AI projects fail to deliver the expected results and value. Despite the many reasons that may lead to failure, industrial companies can improve their chances of success by preparing their businesses for the shift toward a data-driven organization that is inevitable.
Learn how you can achieve successful Industrial AI Analytics Adoption in your Business.
Tackle data aggregation in your organization
Analytics does not exist without high-quality structured data. Garbage in – garbage out. The quality of the output depends on the quality of the input data – it’s that simple. Nevertheless, this truism is one of the most important factors building the foundation of a successful AI project, and unfortunately, it is still usually not given the attention it deserves.
The biggest challenge today is no longer the lack of technical capability to capture signals from legacy equipment that is on the factory shop floor (with IIoT platforms and hundreds of off-the-shelf libraries and connectors like Kepware making it easy to collect data from literally any industrial asset) it is more the case that information is distributed between multiple IT systems, physical locations, with varying levels of quality and frequency.
The opinions we may encounter, that a data warehouse may be the solution, are partially correct. Data warehouses were introduced many years ago, as an evolution of classic RDMBS (relational database management systems) to store and process huge amounts of information (Big Data) for specific purposes. And this is the reason why classical data warehouses are usually not so useful for AI adoption – they contain already processed data, while the feature engineering process (extracting relevant information for further analysis and machine learning computations) requires raw data.
The solution to this challenge is the Data Lake concept. It was introduced several years ago and is already extensively adopted by major cloud providers. The purpose of a Data Lake is simple: to deliver an easily accessible and easily editable place to store raw data that can be used for further processing. Noteworthy is that the modern approach, sometimes called Data Lakehouse, enables top-of-data management, which brings together ultimately the flexibility of Data Lakes and the ACID transactions of classic data warehouses (a good example of a technology that is gaining popularity in this field nowadays is Snowflake).
SMART goals – discover reasonable and measurable goal
Finally, only after the data has been collected and preserved, comes the time to discover business use cases. This should not be taken to mean that industrial companies shouldn’t think about solving their pain points – indeed, quite the opposite – but instead, they should focus on measurable business value rather than the specific part of their manufacturing process the company wants to optimize.
It is very often the case that high expectations of ROI from analytics (especially AI adoption) are due to the mindset of high-level executives, but the truth is cruel – analytics is not a magic wand, and what’s more, you can’t always be sure that a defined business problem is solvable before you attempt to solve it.
Therefore, proper analysis of the business process and selection of an appropriate as well as cost-effective approach and use case is a key step to the success of any industrial analytics project, like Industrial AI analytics adoption.
Make better business decisions
Thankfully, some tools and solutions exist that can help you better understand your business processes by providing insight right into the data that companies collect. A popular term for such tools is Business Intelligence (BI).
Business Intelligence’s power is certainly the ability to visualize Big Data in a very clear and accessible way, usually in the form of advanced reports that present a wide range of line-of-business KPIs. In the industrial world, BI can be used to aggregate OT (manufacturing-derived) data with IT data (such as ERP, PLM, CRM, HR, WMS, and others), which opens up several opportunities to bring value to the business and make better decisions, including selecting the most promising AI use cases.
Business intelligence (BI) is a vastly undervalued area of analytics in the industrial sector today, especially because manufacturing companies are still frightened to move beyond their on-premises environments to the Cloud, while it is nearly impossible (and more importantly, not cost-effective) to apply a business intelligence solution (such as PowerBI) when data is not stored in the Cloud.
“Think big, start small, learn fast”
One other factor is the methodology for implementing AI-based analytics in the industry, which is usually not properly defined and applied. With AI, it is difficult to predict the future (which is a peculiar statement given that artificial intelligence is quite often used to predict future events). The classical approach to AI software development (even Agile type) usually led to an escalation of the analytics project or even its termination due to a lack of expected results – therefore, it is essential to define milestones carefully before starting the project, as well as to inform industrial companies – before deciding to invest in industrial analytics – about what potentially is ahead of them.
Once you have selected a business use case to address, the best approach is to focus your efforts on implementing a PoV (Proof of Value), which is a form of the feasibility study. It doesn’t necessarily have to be a most advanced machine learning model that outperforms others, but one that provides insight, clarity, and confidence that the direction is suitable for further exploration. If the PoV fails to show evidence of value-which is not uncommon-companies can decide whether to investigate and address the reasons for failure (perhaps install more sensors on the production line, collect more data, increase frequency, etc.) or rather take a step back, accept the lack of success, and choose another promising deployment case that can ultimately benefit your company.
When PoV is successful, production-ready software can be designed, developed, and deployed in a pilotage factory with the desire of scaling up for the whole organization, but what is crucial is that those common fiascos in the early stages of the project must not be treated as a failure, but rather as a necessary step to achieve final success.
Become a data-driven organization by industrial analytics adoption
Once a business sees the value that can be derived from the industrial analytics-first project ( whether BI or AI), there’s no turning back. Increasing areas of businesses are being digitized, more data sources are being aggregated and contextualized, allowing for better, more informed, and more sophisticated decisions to be made. A matter of time is the emergence of daily analytics in every manufacturing enterprise. A company just needs to be mature enough to adapt properly to this inspiring trend.
However, the faster the industrial sector switches to using the power that hides behind the tons of information already collected in its backyard and transforms itself into a truly data-driven organization, the faster production processes will be optimized and factories will become more eco-friendly in a short period.
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Get all the information you need to remember, in one place.
- Collect and store good quality OT/IT data in the cloud (Data Lake) using IoT
- Define a measurable and realistic goal that you want to achieve with AI
- Use BI to provide immediate value in business decision-making
- Tailor your project delivery methodology to meet the unique characteristics of industry analytics projects
- Trust your data and do not be afraid to base your business decisions on the information you have collected.
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