A Perspective of the Manufacturing Future,
Part 2

Production scheduling

Published: Thursday, November 10, 2016 - 11:06

In my last article about the future of cutting tools, I discussed a vision and road map that I created by imagining what a manufacturing ideal might look like using what we know to be technically possible today. Here, I’m going to describe a vision for a futuristic production management system where data silos don’t exist, predictive analytics provide glimpses into the future, and algorithms optimize throughput to balance costs and demand.

The current state of production scheduling

Frequently, production decisions are made with partial information, and what may seem like an “optimal” solution on a local level creates costly disturbances on the macro level. For example, manufacturing equipment needs preventive maintenance, but production schedules typically don’t include machine downtime for these events due to a lack of visibility for them. To permit production to continue, preventive maintenance is often delayed. If this continues for too long, the equipment may fail prematurely and cause significant cost disruptions. There is also no visibility to the state of equipment to detect potential failures or changes to the production schedule. Some common production scheduling problems include:
• Indirect materials (e.g., cutting tools and other consumables) may run out of inventory.
• Personnel may become ill and cross-training may be limited, causing the process to operate less efficiently.
• External influences like demand, weather, and supplier issues may affect production.
• The schedule might be managed in a spreadsheet with limited data visibility.

The future of connected data systems

In the future, all data systems will seamlessly share information with one another. There will be a contiguous data record that exists throughout sales, manufacturing, customer monitoring and diagnostics, services, and product development. Manufacturing production systems will have immediate visibility to sales, product design, supply chain (internal and external), personnel availability, personnel qualifications, and customer issues. Inside the shops, individual spreadsheets to manage production schedules won’t exist, and the state of all production resources, equipment, personnel, and indirect and direct material will be known at every moment in time. All data will be linked with their proper context.

For example, a component failure on a product at a customer’s site will be linked to the manufacturer's equipment and personnel inside of a shop, the supplier who supplied the raw material, the quality checks throughout the manufacturing process, the design of the product, the analysis and testing that was conducted on the design, and the custom order that drove the product configuration. This eliminates any information silos and provides full transparency throughout the supply chain.

In the future, manufacturing production systems will have immediate visibility to sales, product design, supply chain (internal and external), personnel availability, personnel qualifications, and customer issues.

The future of predictive analytics and production optimization

In the future, as manufacturing equipment ages, sensors will monitor the components of the equipment, and analytics will run on the sensor data to determine the “health” of the equipment. As the equipment’s components reach end-of-life, the data systems will be able to query the maintenance inventory to see if the necessary replacement components are on hand. If the parts aren’t on hand, the part supplier will be notified, and a requisition to purchase the parts will be created. That way production risk will be calculated and machine downtime will be added to the schedule at a time where risk is as low as possible.

With regards to direct and indirect materials, the data systems will predict potential disruptions by comparing the demand based on the production schedule with current inventories and scheduled delivery dates from the suppliers. As the product demand fluctuates, the manufacturing data systems will notify supplier data systems ahead of time so that suppliers can prepare to meet the new demand accordingly.

In the case of a machining/fabrication shop, many of the processes are developed using general-purpose equipment. That equipment could be used for different operations on multiple products. In the essence of being “flexible,” many shops have developed redundant processes so that if one machine goes down unexpectedly, production can be quickly shifted to a different machine. By coupling asset condition with material inventories and resource staffing, routings can be dynamic, and the system will be able to analyze multiple routes to complete a part and make suggestions to minimize overtime, outsource, and delivery penalty costs.

There will also be significant opportunities from using predictions or forecasts from sources beyond traditional manufacturing sources. For example, by analyzing production schedules in conjunction with information like flu outbreaks, the data system can begin to predict the probability that personnel will be out of the shop, and assess the risk of missing production deadlines and the costs associated with those risks. By making those assessments, the system can identify where cross-training may be necessary to reduce the overall risk and costs. By including weather forecasts and suppliers’ geographic locations, the manufacturing data system can predict which product schedules might have a higher probability of being affected due to weather events that disrupt the supply chain, like a blizzard in the South.

This is another glimpse into one manufacturing geek’s view of how production scheduling might function in the future. It’s very exciting to think about how shop-management decision making can be augmented by data systems and analysis. There are numerous possibilities, and this doesn’t even come close to representing all of them.

First published on the GE Digital blog.

Editor’s note: This is part two of a four-part series offering the author’s perspective on how different aspects of manufacturing may be affected in the future. As noted at the beginning of this article, the author discusses cutting tools in part one;. Read his views on inventory management in part three, and in part four, product quality.

About The Author

Andy Henderson

As an industry analyst at GE Digital, Andy Henderson leverages his experience from his time as an advanced manufacturing engineer within GE Power and his research during his doctoral program to promote a vision for the future of heavy industry/discrete manufacturing and drive strategy for achieving that vision.