How will smart manufacturing transform the supply chain?
For manufacturers, managing the supply chain from beginning to end has been like traveling two superhighways interrupted by a long stretch of dirt road.
Manufacturers have benefited from increasingly powerful tools for demand planning and logistics management – the first and last parts of their supply chains – but tracking the performance of manufacturing production across the supply chain has remained stuck in the era of clipboards, whiteboards, spreadsheets and manually assembled reports.
For most companies, understanding machine capacity, throughput, efficiency, and quality across the supply chain remains a black box. Companies that rely heavily on contract manufacturers have even less visibility – challenged by partners with different systems, processes, and levels of willingness to collaborate.
Today’s supply chain monitoring systems lack the ability to look at machine and part/batch-level data across the supply chain, limiting a global manufacturer’s ability to manage their supplier base as an integrated platform. By enabling integrated visibility of machine and part/batch production data, manufacturers have the ability to:
- Benchmark and improve performance across the supplier network
- Perform system-wide load balancing & capacity planning
- Implement full-traceability
But can manufacturers, with global networks of disparate suppliers, really manage their supply chain as an integrated platform for production? I’ve seen it in action.
I was recently engaged with a global consumer goods brand that relies heavily on contract manufacturers spread throughout East Asia. The manufacturer was looking to understand the drivers of differences in cost, efficiency, and performance across different contract suppliers producing the same product.
By integrating and streaming near real-time machine and quality data from multiple suppliers, the consumer goods firm learned of significant differences in operating procedures (e.g. the time to load/unload a machine or raw materials usage) and utilization (e.g. machine idle time). This previously unavailable visibility into machine and part level data across the supply chain delivered unexpected and eye opening insights.
Benchmark and improve performance across the supplier network
The machine and system-level insight enabled the brand owner to establish consistent supply chain-wide performance metrics. Previously, operating metrics were calculated by each contract supplier in a different manner – impacted by different in-house systems, processes, and interpretations. By calculating KPIs such as availability, performance, quality, and ultimately OEE directly from machine data, the consumer goods firm was able to get an objective understanding of variances in execution and operational processes.
The machine data also provided a detailed view of how manufacturing processes were being executed at each facility. With this level of granularity, the consumer goods firm was able to disseminate best practices for machine operational procedures (such as load time) across their supplier network. By identifying machine operating variation and establishing targets for the lowest-performing facilities, the consumer goods firm identified a potential production uplift of 40 percent.
The consumer goods brand also discovered that this same machine-level benchmarking could allow them to look at material usage by machine across facilities. This data gave them new insights into cost metrics by supplier and by product. By using actual material usage rates and actual machine operating time, the consumer good firm can develop better costing models for planning purposes.
Perform system-wide load balancing & capacity planning
Even more interestingly, by looking at machine availability across their entire supply chain, the consumer goods firm was able to identify available capacity for specific products within existing contracted suppliers. This new level of insight provides the ability to respond to spikes in demand without having to source from additional facilities.
Visibility into real-time capacity by machine and by facility can enable agile capacity planning that responds to real-time changes in demand. It also provides unparalleled visibility to supply chain leaders, allowing them to provide accurate timelines to customers and retailers and internal constituents such as brand managers.
Supply Chain Traceability
As requirements for traceability continue to rise, manufacturers and brands are increasingly responsible for documenting the production conditions of products and raw materials. Many manufacturers have been cobbling together solutions to provide this capability which can be extremely challenging due to the complexity of the processes and numerous systems capturing production data. Knowing the exact conditions under which each batch or part was manufactured is a complex exercise in data modelling.
With the consumer goods firm’s ability to capture machine-level and system (quality, ERP) data, they are able to track production parameters/settings for finished products and raw materials at all levels in the supply chain.
The supply chain of the future is here. The availability of machine level data and of tools that use AI to automate the integration of information from multiple partners is unleashing a new set of business models and operating procedures. Supply chain leaders must play an aggressive role in helping their organization harness the power of this new era.
Originally this article was published here.
This article was written by Sudhir Arni, the Vice President of Manufacturing Transformation at Sight Machine, category leader for AI in Manufacturing and manufacturing analytics. In this role Sudhir leads a team of transformation leaders responsible for on-boarding all new customers and customer success managers who ensure adoption of Sight Machine technology across the global enterprise customer base enabling business expansion from existing customers.