Industrial analytics is the application of data analytics to extract insights and valuable information from the large amounts of data generated by manufacturing processes. Industrial analytics optimizes and improves the manufacturing process by identifying areas for optimization, reduced waste, and increased efficiency. This can be achieved through the analysis of data from various sources such as sensors, machines and systems, and applying advanced analytics techniques such as predictive modeling, anomaly detection, and optimization algorithms.
Industrial analytics can also be used to gain insights into customer behavior, supply chain management, and quality control. By leveraging data-driven insights, manufacturers can make more informed decisions and gain a competitive edge in the market.
Today, the analytics market is extremely fluid. More companies are pursuing analytics solutions and more employees throughout the enterprise want more and better decision making tools. And the increasing focus on Industry 4.0 (I4.0) and Industrial Internet of Things (IIoT) is driving demand for predictive maintenance solutions, which rely on advanced analytics.
All companies can benefit from enhanced insights into market demand, operations, inventory, and corporate performance. Analytics provide the information to effectively manage global markets, supply chains, and operations.
Artificial Intelligence Is Cited as the Most Impactful Technology But Also Comes with a High Rate of Failure
Artificial Intelligence (AI) relates to machines performing tasks that have typically required human intelligence, such as reasoning, problem-solving, and decision making. These systems hypothesize and formulate possible answers based on available evidence, can be trained through the ingestion of vast amounts of content, and automatically adapt and in some cases, learn from their mistakes and failures. Machine learning is a subset of AI involving the use of algorithms to learn from data and improve performance on a specific task without being explicitly programmed.
Advances in AI could not have come at a better time. Industrial manufacturing companies are constantly faced with a multitude of challenges, both current and future. As a result, manufacturers are increasingly focused on improving their business operations to ensure their short and long term viability.
However, implanting AI in manufacturing can be challenging, if not daunting, as it requires an understanding of business objectives and the integration of AI with existing processes and systems. This may explain why ARC primary research has revealed AI is cited as the most impactful technology over the next five years. If that is the case, then why do so many manufacturers also share stories of the collapse of their AI projects? For most companies, even the innovators, it is typically overwhelming to determine an effective starting point for any digital transformation project, including implementing AI.
Build Business Case Consensus
Applying analytics in an industrial setting is a complex endeavor. To help you build internal consensus while avoiding unnecessary costs and false starts, ARC can provide answers to key questions, such as:
- What are the key specific business problems or challenges that industrial analytics can address?
- What roles and responsibilities are required?
- What data sources are available to ensure the data is accurate and relevant to the problems needed to be solves ?
- How are the results integrated into existing processes and systems to ensure the insights are actionable and can drive meaningful improvements?
- How to ensure analytic efforts are aligned with the overall strategic goal of the organization and are being used to drive meaningful outcomes for customers and stakeholders.