The pressure of global markets to maximize return on assets has highly complex factories operating 24/7/365 requiring uninterrupted uptime. Traditional competitive advantages and products have become commoditized and margins eroded, making it a price game. IoT, Industry 4.0, and technology increasingly drive customer solutions.

Machines, components and processes have collected operational data forever. It is the analysis and converting this data into useful information which creates true customer value. Analyzing machine‐, product‐ & environment data, using algorithms to identify deviations to standard and have operating systems change operational parameters to avoid costly breakdowns takes this to a new level.

This changing environment affects the role of the CFO, which has many facets: custodian of the company’s assets, safeguarding investor interests, statutory and ethical compliance, trusted business and strategy partner to the CEO and functional teams of a company and providing guidance in information technology and business systems.

Compiling credible forecasts and market guidance requires a thorough understanding of the business, products, markets, required core competency, cost and pricing models.

Understanding what creates value, how much value, at what cost, how to convert this to market-based offerings is essential to build reliable financial models and forecasts.

"Generating, mining, and analyzing operational machine and process data can be used in many ways to create value for a customer"

Generating, mining, and analyzing operational machine and process data can be used in many ways to create value for a customer. In the same manner as your vehicle indicates that an oil change is imminent at certain mileage intervals, many different aspects of operational data can be analyzed and interpreted for predictive and preventative maintenance or to change the operating parameters. Monitoring wear and tear, energy consumption, output‐ & product quality, non‐conformance events, vibrations, temperature, heat and friction, water‐ air‐ oil‐ pressure, abnormal signals can be identified as precursors to imminent unscheduled interruptions. Artificial intelligence can learn from this data and initiate mitigating action that goes beyond rules-based reaction.

Cost of unscheduled downtime can run into hundreds of thousands of dollars per hour and disrupt supply chains and production lines. With predictive analysis, downtime can be planned and scheduled and spare parts, tools and resources be made available.

Digital twins, such as Schuler uses for its presses, provide a virtual model to evaluate data from the physical unit and simulate under varying conditions instead of just in a linear manner. Remote diagnostics interface with many machine types data. For training, the functionality and operation of the system can therefore be tested and trained on the virtual machine behaving like a real machine.

Artificial Intelligence can recognize deviations to standard and change operational conditions to eliminate the condition or mitigate it till corrective action is scheduled