Resource and Operations Management for Industry 4.0

Synonymous with smart manufacturing, Industry 4.0 is the realization of the digital transformation of the field, delivering real-time decision making, enhanced productivity, flexibility and agility. It is revolutionizing the way companies manufacture, evaluate and distribute their products.

Manufacturers are integrating new technologies, including Internet of Things, cloud computing and analytics, and AI and machine learning throughout their operations. Smart factories are equipped with advanced sensors, embedded software and robotics that collect and analyze data and allow for better decision making.

Even greater value is created when data from production operations is combined with operational data from ERP, supply chain, customer service and other enterprise systems to create new insight from previously siloed information. Industry 4.0 concepts and technologies can be applied across all types of industrial companies, including discrete and process manufacturing, as well as oil and gas, mining and other industrial segments.

Adopting data-driven models for process control and decision-making provides scalable solutions to industry challenges, which comes with many additional requirements. This project focuses on generating model-driven process insights to build trust in forecasting models and applying user feedback to correct model performance, all with the goal of building AI models that help users forecast their future needs and adjust to changing circumstances.

How it Works

The project team focuses on building a reusable and scalable platform for multi-variate long-term time-series forecasting, incorporating a wide spectrum of various novel state-of-art algorithms and scalable ML pipelines using data from sensors embedded in Industry 4.0 facilities. One of the key focus areas in this work stream is to learn generalized representation for time-series in different industries and domains.

The primary goal is to capture the industry’s domain knowledge as a time series embedding in a semi-supervised way, that can be used to bootstrap and speed-up various downstream tasks like forecasting, anomaly detection, and so forth. These techniques greatly help in improving the accuracy of the forecasting models, especially when labelled data are scarce. These assets are packaged as part of IBM’s Foundation Model ecosystem and can be customized based on various client requirements.

Active forecasting models the effect of external factors on the forecast. It is essential for business strategy evaluation and process control, but demands process knowledge which is often unknown in many scenarios. As part of this work, the team leverages data-driven simulated experiment-based resolution of causal impact, a differential equation-based model for approximating the data generating process.