Manufacturing companies are increasingly embracing digital transformation and data analytics to enhance their operations and their competitiveness. Data-driven decision making is becoming a crucial aspect of the entire manufacturing process, from supply chain management and the physical manufacturing process to aftersales and data analysis.
Our research has revealed how these organizations are leveraging such developments as advanced AI capabilities and digital assets to drive their businesses forward. What we’ve seen are three primary areas of focus: price optimization, augmented analytics, and practical insights.
Developing an optimal pricing strategy is crucial for manufacturing organizations, as it directly affects their revenue. A good pricing strategy must consider various internal factors, such as material supply, manufacturing capacity, and costs, as well as external factors, like economic indicators, market sentiment, user preferences, and government regulations.
In order to achieve an optimal pricing strategy, manufacturing organizations need access to real-time information to help them determine how to achieve short-term revenue goals while also establishing fair and reasonable prices. Historical data can be valuable for building pricing models and predictive analytics, but advanced AI algorithms can use real-time data to help companies go deeper and build a comprehensive picture of the immediate and future impact of their pricing decisions.
Real-time information helps manufacturing companies create adaptable AI models and predictive analytics that capture patterns and nuances in the data. This allows companies to create dynamic pricing strategies that adjust to match competitors’ prices, capture larger market share, or reduce losses.
Another significant challenge for manufacturing organizations is the need to reduce the complexity and manual intervention required to create useful analytics. These organizations need to gather data from multiple siloed sources, standardize the data, and then load it into a data platform with the proper data governance and quality controls. Companies can then use this intelligence to automate the data pipeline, train advanced AI models, and make informed decisions. But manufacturing staff don’t always have the technical expertise to operate these systems or to interpret the often voluminous output.
A new form of this process of data collection and analysis is called augmented analytics, which eliminates the need for extensive experience, knowledge, or technical skills by automatically providing users with relevant insights. Augmented analytics utilizes Natural Language Query (NLQ) technology, which provides self-service business intelligence based on natural language search. Generating custom reports and personalized dashboards becomes as easy as searching with voice commands. This provides assembly line engineers and technicians with access to critical insights without specialized training.
Of course, manufacturing companies need ways to make their data insights usable. One particularly fruitful area here is applicable in predictive fault prevention on the manufacturing line. Sensors that have been placed along the production line can monitor various important metrics, for example, production speed. The sensors can combine data from multiple sources to monitor the health and performance of each machine along the manufacturing process.
Measuring temperature, voltage, power consumption, and production speed can help manufacturers track and pinpoint specific machine faults. Once the measurements and data patterns exceed certain thresholds, maintenance teams can be alerted to check machines for potential faults. This enables them to repair or replace parts that might cause unexpected downtime on the entire production line.
Manufacturing organizations are increasingly looking for better ways to manage and analyze data in diverse structural formats. Traditionally, manufacturing companies would use schema-based data systems. These systems gather data that’s in multiple formats but the data then requires preprocessing before it is available for analytics. This requirement prevents organizations from scaling their data platforms and integrating more data sources.
To address data incompatibility, many manufacturing organizations are adopting data lake technologies to integrate information from multiple sources. A data lake is a central repository that allows companies to store both structured and unstructured data, and to do so at any scale. Data lakes significantly reduce the time needed to process and analyze data, allowing organizations to react more quickly to changes in the market and in their operations. Data lakes also enable manufacturing companies to scale their data platforms more effectively.
Implementing data lake technologies also allows manufacturing companies to better leverage machine learning and AI capabilities. This can lead to improvements in predictive maintenance, quality control, and demand forecasting, among others. As a result, manufacturing organizations can optimize their operations, minimize downtime, and ultimately become more competitive.
Manufacturing organizations increasingly seek better ways to manage and analyze diverse data formats to perform and benefit from advanced analytics. Data lake technologies in particular will likely play a significant role in enabling these organizations to remain agile and competitive in an ever-changing global market.
Bal Heroor is CEO and Principal at Mactores and has led over 150 business transformations driven by analytics and cutting-edge technology. His team at Mactores are researching and building AI, AR/VR, and Quantum computing solutions for business to gain a competitive advantage.