Sight Machine enabled IPG employees, from the shop floor to its executive offices, to monitor real-time performance, troubleshoot problems and capture quality issues early in the production process.
With OEE Insight from Sight Machine, Metals Company Fixes 5% Scrap Rate.
Challenge
Plant engineers observed increased out-of-specification surface unconformity defects that were causing 5% of the output to be scrapped. They at first struggled to identify the root cause of the problem. But they were able to use Sight Machine’s variation-analysis tools to find an abnormal variation in evaporation-zone temperature in the oven.
Solution
Using Sight Machine, they identified a correlation between evaporation-zone temperature variation and the oven’s airflow-control mechanism. This problem would have been difficult to identify without Sight Machine’s tools and modelled data
Outcome
The Result: Zero Scrap. Sight Machine enabled them to resolve the problem, eliminating the defects that were causing 5% of the product to be scrapped, reducing the scrap rate to as low as 0%. The project at the plant was so successful that the company has decided to expand the use of Sight Machine’s productivity platform to its global network of plants.
Customer - Fertilizer Manufacturer
Fertilizer Manufacturer
High-speed, highly automated producer with outstanding production practices created real-time Data Foundation at multiple plants. New OEE gains achieved by detecting elusive bottlenecks and improving scrap.
Challenge
Toros Tarim is the leading manufacturer of fertilizer for Turkey and neighboring regions. This large scale process requires a huge industrial chemical complex with each step in the production pathway (sulfur acid to phosphoric acid to ammonium diphospate for example) being dependent upon the former. Downtimes are extremely costly, with potential to impact all areas, including the on-site co-generated electricity whose surplus also bolsters the Turkish electrical grid. Toros approached Sight Machine as part of their digital transformation and lean journey with the clear expectation to start at building a contextualized data platform of all their data, facilitating an holistic view of their process network, before moving to data visibility and automated dashboarding of reports and key performance indicators (KPIs).
Solution
The next part of the journey is now underway as Sight machine and Toros partner to use their data platform to explore advanced analytics such as predictive and AI, process optimization (including for sustainability and emissions management), and maximizing plant reliability / minimizing asset downtime. This project was implemented entirely remotely during the COVID-19 pandemic and was possible because of the unique way Sight Machine is able to rapidly ingest and use automated tool sets to transform and contextualize tens of thousands of data fields in parallel.
Customer - Carbon Revolution
Carbon Fiber Wheel Manufacturer
High-speed, highly automated producer with outstanding production practices created real-time Data Foundation at multiple plants. New OEE gains achieved by detecting elusive bottlenecks and improving scrap.
Challenge
Carbon Revolution is as innovative and disruptive as a technology can be. They fabricate carbon fibre wheels (rims) that are both lighter and stronger than current aluminum constructions. While these concepts started with sales to niche manufacturers such as Ferrari, they are now becoming commonplace options on more mainstream manufacturers. As production ramps up and concomitantly unit cost drops these will undoubtedly become the new normal over time, just as aluminum rims did to steel decades before. Carbon Revolution is using Sight Machine to provide an end-to-end view of its fabrication process in real time. For this fast paced company, instant access to data is paramount as it rapidly grows. Track and trace of a wheel to understand weaknesses in quality and fabrication techniques needs expedited root cause analysis.
Solution
Sight Machine provides end-to-end visibility by automating all of the data collection, cleaning, aligning, and joining, even for out of order records. This frees up engineers to be refining their processes for growth and not using their valuable time hand-prepping data for hypothesis testing. With all of their data at their finger tips Carbon Revolution aims to retain their agility and speed as they scale and be at the forefront of using their digital data.
Customer - Large Paint and Coatings Company
Large Paint and Coatings Company
World-class batch process manufacturer improved its throughput by 7% in only two months.
Challenge
Asian Paints had capacity constraints in highly automated regional batch chemical plants. They could not solve for global constraints in a complex batching system of 100s of individual assets with their current data.
Solution
Sight Machine continuously streamed and contextualized data from different sources at both a system and asset level. This enabled instant visualization of individual assets with the line as a system to balance utilization and identify key bottlenecks.
Outcome
Reduced cycle time by 7% at first factory. This was achieved in two months and driven by operators using Sight Machine in company-sponsored hackathons to diagnose and solve problems. The improvement in cycle time represented a $7M increase in throughput. Additional outcomes are being driven across the network of factories now.
Customer - High-speed Packaging Company
High-speed Packaging Company
High-speed, highly automated producer with outstanding production practices created real-time Data Foundation at multiple plants. New OEE gains achieved by detecting elusive bottlenecks and improving scrap.
Challenge
Data was isolated in disparate systems, making it difficult to perform data-driven analyses of quality and performance problems.
Solution
Sight Machine integrated quality data, process, and sensor data from the production line enabling regular visibility, problem-solving, and data-driven Continuous Improvement across a network of plants
Customer - Global Dairy Processor
Global Dairy Processor
Global dairy processor optimized throughput and quality for a 5% increase in yield of its high-performance product line.
Challenge
This dairy producer used Sight Machine to optimize yield in fermentation and separation processes by analyzing the processes, providing alerts as to optimal times to run and stop the processes, and automating recommendations for the decisions holistically across multiple assets.
Solution
Sight Machine enabled end-to-end traceability of each batch and modeled optimal times for critical processes so as to increase both yield from the processes and throughput.
Outcome
The platform allowed the manufacturer to increase output by optimizing production for the entire line vs individual processes.
Customer - Large Paper Manufacturer
Large Paper Manufacturer
Insights within the first 2 weeks. Large paper manufacturer used Artificial Intelligence and Machine Learning to pinpoint discrepancies with Warp, defects and energy consumption.
Challenge
Corrugated paper plant had trouble knowing which specific process parameters drove up scrap and customer claims. In addition, plant wanted to know when and which SKUs resulted in driving peak energy consumption.
Solution
Sight Machine integrated process data, energy data, quality data and customer claims data to provide integrated models that were then utilized to run advanced analytics including AI/ML algorithms.
Outcome
AI/ML yielded insight within 2 weeks about causes of quality issues that had previously eluded company data scientists. Similarly, connections between process variation and energy use and defects and process variation represented new insight into mission-critical questions.
Customer - Manufacturer of Aerospace Sensors
Manufacturer of Aerospace Sensors
Manufacturer of aerospace sensors performs root cause analysis with data to reduce quality problems across its 90 step-process.
Challenge
This manufacturer had extremely tight control over each process step. But with more than 90 steps, subtle interaction effects between each step were challenging to see.
Solution
Sight Machine provided a way to associate quality data throughout the entire process as well as trace parts and perform multivariate analysis. This enabled them to analyze variation impacts on quality throughout the entire production process.
Outcome
Within the first months, the company achieved a significant reduction in scrap related to quality problems. They use Sight Machine as a single source of truth to understand quality and process data across 90 different operations.
Customer - Automotive OEM
Automotive OEM
Automotive manufacturer achieves 91% reduction in machine downtime through predictive downtime analytics.
Challenge
OEM body assembly involves 100s of robots, where intermittently a catastrophic failure would create costly line downtime. Operators had a manual system for maintenance, which was difficult to keep operationally supported, and were seeking a predictive analytic for robot downtime.
Solution
Sight Machine automated the operational maintenance alerting and built a predictive maintenance solution that led to increased effectiveness of maintenance scheduling.
Outcome
Sight Machine enabled the manufacturer to monitor anomalies in torque sensor data and detect patterns correlated to failures enabling the servicing of robots likely to experience failure reducing manual inspection 90%.
Customer - Plastic Packaging
Plastic Packaging
World-class manufacturer, regularly recognized as a leader in quality and plant operations, reduces downtime, scrap and waste by deeply incorporating constant analysis of Data Foundation into its workflow.
Challenge
The facility was not able to efficiently utilize its raw material causing higher scrap and rework.
Solution
Process and Quality data was leveraged to run correlation analysis and perform setpoint enhancements.
Outcome
Projected savings of $2.5mm per year at the first plant served, resulting from better Raw Material Utilization improvement across 4 lines.
Customer - Pulp and Paper Production
Pulp and Paper Production
Leading paper manufacturer improved energy use by analyzing thousands of data sources across all its processes.
Challenge
Steam usage had been high on a paper machine coming up from an outage with no identified root cause.
Solution
Variance analysis and correlation analysis identified a single valve that was found to not be 100% open. Root cause was found and corrective action was taken for a valve cut back.
Outcome
This single insight and the corresponding best practice developed from it – one of dozens generated annually – yields millions per year at the enterprise level in steam savings and increases in production rate.
Customer - Global Consumer Goods Company
Global Consumer Goods Company
Global manufacturer improved development and performance of supplier equipment by implementing a network of visibility and analysis tools across suppliers.
Challenge
As part of the initial steps in a conversion from manual to automated processes and a parallel shift in costing processes with contract manufacturers, this manufacturer sought real-time visibility across a wide network of plants.
Solution
Sight Machine implemented across multiple contract manufacturer plants, and enabled previously unattainable visibility and asset management.
Outcome
This manufacturer confirmed that in just the first year, visibility alone into assets was worth 2x program cost.
Customer - Contact Lens Manufacturer
Contact Lens Manufacturer
Leading contact lens manufacturer needed to analyze thousands of alarms on a highly automated line to identify throughput delays and reduce scrap.
Challenge
High-speed medical device line had more than a thousand alarms per shift, but could not identify alarm sources and the root cause of micro-downtimes and process variations.
Solution
Sight Machine contextualized and continuously streamed data from 12 different data sources to enable an AI application that identified causes of micro-stops across a 300-yard long automated line.
Outcome
Sight Machine’s platform enabled the site to make real-time decisions. Site engineers and operators used results from the AI application to prioritize problem-solving efforts. The site reduced scrap by more than 50%.
Customer - Medical Device Production
Medical Device Production
One of the world’s largest medical device manufacturers struggled with CNC machines’ scrap rate.
Challenge
Despite having data in many forms, it was not possible to identify causes of scrap.
Solution
Sight Machine identified the causes of defects by analyzing voluminous process data.
Outcome
Small adjustments in materials temperature resulted in 18% reduction in scrap.
Customer - Global Automotive OEM
Global Automotive OEM
Global automotive manufacturer produced engine blocks across 3 plants in its luxury division. Persistently high defects could not be resolved.
Challenge
Using traditional quality procedures, in-line quality issues in die casting and machining processes were impossible to resolve across 3 plants. Serialization varied from plant to plant, and data could not be combined.
Solution
Sight Machine integrated process and part data from multiple sources and facilities to empirically represent and analyze the entire production process with a single Data Foundation. This enabled traceability and provided an analytical foundation across processes.
Outcome
The OEM was able to identify the root cause and reduce rejects by 25%.
Customer - Tier 1 Automotive Producer
Tier 1 Automotive Producer
Major automotive supplier controlled costs and reduced waste in their seat production and assembly line.
Challenge
High material waste, cost and low productivity due to lack of process instability in the extrusion production process.
Solution
Sight Machine integrated PLC, MES and in-line test data to identify causes of process variability. The platform’s statistical process control analysis was used to proactively monitor output.
Outcome
After implementing SightMachine this company saw a 25% reduction in scrap plus better planning and scheduling capabilities.
Customer - Pharmaceutical Manufacturer
Pharmaceutical Manufacturer
Large pharmaceutical company wanted to take Continuous Improvement to the next level by reacting faster to losses in a new packaging line
Challenge
Manufacturer was looking to improve quality and efficiency of a new, highly automated line.
Solution
Sight Machine contextualized real-time data to identify cycle time variability during setup. Visibility application provided a view of defect types and sensor readings by machine to identify opportunities for efficiencies.
Outcome
Productivity and efficiency nearly doubled as Sight Machine enabled rapid improvement.
Customer - Leading Glass Manufacturer
Leading Glass Manufacturer
Leading glass manufacturer incorporated predictive analytics into workflow to help operators anticipate quality issues and reduce scrap. Scrap is a perennial source of energy loss in the glass industry, and improving it drives significant gains in sustainability. Predictive analytic had almost 80% accuracy in anticipating scrap.
Challenge
The lag between the causes of quality issues in a furnace and the emergence and detection of faults in glass 3 days later, makes it difficult for glass producers to manage yield and results in glass being returned to the furnace at every glass company. This results in increased energy use when glass has to be re-melted to remove defects.
Solution
By continuously analyzing 45 furnace parameters, Sight Machine predicted increases in fault density up to three days in advance with an extremely high degree of accuracy.
Outcome
The furnace analytic predicted 78% of all defects from the entire process.
Customer - Medical Device Producer
Medical Device Producer
Manufacturer analyzed CNC data to reduce downtime and variation
Challenge
Medical device plants depend on CNC machines and downtime is costly. Variations in operator practices make standardization difficult.
Solution
Sight Machine modeled the production line, consisting of many different CNC machining operations. Analysis on modeled data provided insight to improve uptime by 30%.
Solution
Large reduction in downtime alleviated planning and scheduling constraints.
Customer - Leading Hardware Manufacturer
Leading Hardware Manufacturer
Leading hardware manufacturer enhanced its efficiency across multiple lines through automation and visualizing Takt time.
Challenge
The company used Sight Machine to balance Takt time across high-speed, automated production lines. Sight Machine enabled a substantial improvement in yield.
Solution
Sight Machine enabled the client to accurately capture and continuously visualize Takt time at every station on multiple lines, thereby identifying bottlenecks that arose from slight miscalculations in the machine set up.
Customer - Heavy Equipment Manufacturer
Heavy Equipment Manufacturer
Heavy equipment manufacturer challenged by lack of visibility into downtime
Challenge
For a maker of construction mining and forestry equipment, weld integrity is of paramount importance. This manufacturer’s welding process experienced multiple unplanned stoppages.
Solution
Sight Machine provided a scalable solution to aggregate, store, analyze, and visualize data from hundreds of robot welding machines. This allowed concise data blending, visualization, access, and product evaluation.
Outcome
Visibility into metrics, including availability, downtime reasons, and key performance indicator, E, where E = weld deposit (kg) / cycle time (hr). Analyzed 60K cycles across the fleet of robot welders to gain correlated insight into the cause to accelerate corrective actions, reducing downtime and bottlenecks.
Customer - Global Brewer
Global Brewer
Global brewer solves 20-year downtime problem by analyzing thousands of alarms per shift.
Challenge
High-speed packaging triggers thousands of alarms, many of which in turn trigger other alarms, so that is impossible to identify causes of micro-downtimes as they are happening.
Solution
Apply a blend of natural language processing, unsupervised learning, and sequence analysis to analyze alarm data and identify on a continuous basis causes of downtime.
Outcome
Identified insights to reduce downtime events resulting in 16% throughput improvement.
10 Hot AI-powered IoT startups
The Internet of Things generates a lot of data that needs to be processed, and innovative startups recognize that artificial intelligence can lighten the load. Jeff Vance of Network World selected Sight Machine as a 10 hot AI-powered IoT startup. Read on to learn more about what Sight Machine does to address this.
Problem Sight Machine solves: Manufacturers struggle to make optimum decisions quickly. When dealing with problems that emerge on the plant floor, any indecision or delay in decision making could be costly.
In manufacturing, data variety (due to thousands of sources) is far greater than in other IoT use cases, and according to research from Morgan Stanley, the sheer quantity of data is also larger than anywhere else. Traditional analytics tools can’t cope with either the variety or volume.
How they solve it: Sight Machine software uses canonical data models and AI to ingest, integrate, and map massive amounts of heterogeneous data into operational models. The canonical data models represent any machine, line, facility, supplier, part or batch that the manufacturer specifies. Once modeled, data is then systematically and continuously analyzed.
By standardizing the manufacturing models and following a data-first approach to decision making, Sight Machine enables manufacturers to automate data ingestion in a rapid, highly repeatable manner. The standardized model allows manufacturers to create downstream applications that immediately leverage the modeled data.
Analytical techniques include advanced inferential statistics, machine learning and AI, all of which are applied to generate manufacturing-specific insights. Within its platform, Sight Machine analyzes and visualizes data, so results can be viewed via a browser on any connected device.
Why they’re a hot startup to watch: Sight Machine has the deepest pockets in this roundup, backed by $50 million in VC funding. CEO and co-founder Jon Sobel was previously with Tesla and Yahoo, while co-founder and CTO Nathan Oostendorp and co-founder and Chief Data Scientist Kurt DeMaagd previously co-founded Slashdot.org. Finally, the customers Sight Machine has accumulated are impressive, including GE, Fiat Chrysler, and Fujitsu.
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