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An undesirable rigid production schedule leads to excess inventory while distracting the resources from making the required stock. The brittle supply chain process leads to raw material delays, slipped production schedules, and missed customer SLAs. 

Plants are still producing goods by consensus-based forecasts as opposed to actual orders and current external drivers. For instance, contract manufacturers would highly benefit if they dynamically pick suppliers and contract manufacturers by supplier quality threshold, transportation cost, customer deadlines, the past delivery window from the time of order placement, and production time. It involves orchestration and interoperability across various transactional systems(upstream and downstream)

This is the state of today’s discrete or process manufacturing. Therefore, it is high time to bring resiliency to both Just-In-Time and Make-To-Order Manufacturing.

Manufacturing enterprises of all sizes face this challenge, but the problem cost varies.

One may ask, what’s holding enterprises from achieving resiliency?

• Is it lacking technology?
• Is it lacking business practices?

The answer must be surprising to many…

Manufacturing business does follow best practices with some exceptions. However, their technology solutions are missing the bigger picture.

Manufacturing system designed for mainstream scenarios.

• Not Adapting to market and environmental changes

Manufacturing systems are Fragmented, siloed

• Not translating real-time signals missing interoperability.

Manufacturing Operational systems are delivering standardization

• Not yielding a competitive advantage

For instance, northwest-based engine manufacturers took a single vendor approach and implemented the best-of-the-breed cloud manufacturing, procurement, CPQ, WMS, and ERP Suite. All these systems are transactional and integrated from point to point. They cannot read external signals in real time or interpret them beyond A to B conversion.

Manufacturers are not in a position to employ technology and mathematical modeling to solve business problems. To be precise, hyper-automation and advanced data analytics technologies are lacking in manufacturing facilities. 

What is involved in the AI initiatives?

• Technology licenses for data integration, warehousing, orchestrations and and AI algorithm
• Team of Data Scientists – Integration Team –DevOps Team – Functional Experts – Operational Specialist
• Orchestration between predicted process, and execution systems

How do we fast-track the implementation period?

• Identify tools that are business-friendly and yield levers to trial and error without development or deployment time
• Identify tools that are simple to understand and justify
• Identify tools that seamlessly connect with your execution systems

In order to bring resiliency to the manufacturing process, innovation (over standardization and status-quo) has to be prioritized. It starts with interoperable ecosystems, embracing hyper automation and advanced data analytics.