Embrace the Next Industrial Revolution with the Snowflake Data Cloud for Manufacturing

In this second installment of our blog series on Industry 4.0, we cover the common industrial use cases our customers want to solve, and showcase how the Snowflake platform can be leveraged for Industry 4.0.  

As discussed in the previous post, one of the key drivers of Industry 4.0 is the ability to collect and analyze vast amounts of data, enabling more advanced analytics use cases and accelerated decision-making. To generate data-driven insights, businesses need a foundational cloud platform with data pipelines that can ingest both IT and OT data at scale. Additionally, this manufacturing data platform should facilitate IT / OT convergence with a precise asset model and plant hierarchy established in the cloud, coupled with AI/ML-based analytics capabilities.

Snowflake understands the need to establish this manufacturing cloud platform with data pipelines to bring in both IT and OT data to facilitate the convergence. This, coupled with our analytical tools such as Streamlit and SnowPark, provides our customers and partners with analytical capabilities they need to execute their Industry 4.0 use cases.

Snowflake’s vision is to move the focus of all our partners and customers to build applications on top of the Data Cloud, with open pipelines established for ingesting IT and OT data in an economical and scalable fashion.

Data analytics use cases fall broadly into the following four categories (given the large volume of multi-dimensional data, all four categories leverage AI/ML for deriving insights):

• Descriptive analytics: Analytical tools for providing details around “what” is happening at a manufacturing facility (for example, manufacturing KPIs such as OEE and cycle time) 

• Diagnostic analytics: Analytical tools that can help determine “why” an event has occurred (for example, why unplanned machine downtime occurred)

• Predictive analytics: Analytical tools for detecting anomalies and predicting an event before it occurs (for example, with anomaly detection we can predict equipment failure or a drop in quality before it happens)   

• Prescriptive analytics: These analytics combine data, AI models, and business rules to generate recommendations for decision-makers, helping organizations identify the best course of action to take in every situation (for example, preventing equipment failure by providing recommendations to schedule maintenance and order spare parts)  

Some of the solutions and accelerators that our customers and partners are building on the Snowflake Data Cloud for Manufacturing include the following (by no means an exhaustive list):

Cycle time analytics: Cycle time refers to the time it takes for a machine or process to complete a single production cycle. Reducing cycle time can increase the efficiency and productivity of the manufacturing process, as it allows for more units to be produced in a given period of time. Primarily in high volume production environments such as automotive or hi-tech industries, customers are trying to analyze micro stoppages with the goal to reduce them by effectively utilizing manufacturing assets, which lead to improvement in cycle time. 

There are several ways to analyze and optimize cycle time in an Industry 4.0 setting. For example, data from machines coupled with other data sets can be used to identify bottlenecks and inefficiencies in the production process. AI and machine learning (ML) algorithms can analyze this data and suggest process improvements. Additionally, near real-time monitoring of cycle time can allow for adjustments to be made in real time to optimize overall performance. Solutions ranging from descriptive to predictive analytics from partners such as LTI, Wipro, and Dataiku can help in understanding cycle time metrics and optimizing them. 

Yield: This refers to the proportion of product that is successfully produced in relation to the total amount of raw materials used in the production process. For example, if a factory produces 100 units of a finished product from 500 units of raw material, the yield would be 20%. Yield is an important measure of efficiency in manufacturing, as it can impact the overall cost of production. 

Factors that can affect yield in manufacturing include the quality of raw materials, the efficiency of production processes, and the effectiveness of quality control measures. Improving yield can involve identifying and addressing bottlenecks or inefficiencies in the production process, which can potentially lead to quality improvements. This can be accomplished by implementing data- and AI-driven insights to accelerate the RCA and reduce and/or eliminate these bottlenecks and quality issues to improve yield overall. Solutions from our partner Dataiku have demonstrated precise business outcomes with customers leveraging the Data Cloud for Manufacturing and analytical tools from Snowflake.  

OEE: Overall equipment effectiveness (OEE) is a metric used to measure the efficiency of a manufacturing process or production line. It is a measure of how well a production process is utilized, and considers factors such as availability, performance, and quality. Availability refers to the percentage of time that a production line is available for production, considering planned and unplanned downtime. Performance measures the speed at which the production line is operating compared to its designed capacity, considering factors such as slow cycles and machine speed. Quality measures the percentage of good products produced, compared to the total number of products produced.

OEE is calculated by multiplying the availability, performance, and quality of a production process. For example, if a production line has an availability of 90%, a performance of 95%, and a quality of 99%, its OEE would be calculated as follows: OEE = 90% * 95% * 99% = 84.55%. This calculation of OEE needs data from both machines and IT systems such as quality / maintenance systems. Partners such as LTI have solutions that provide OEE insights and RCA tools to understand a drop in OEE, then correlate that back to the individual constituent elements to better understand the issue. 

Predictive maintenance: This is a maintenance strategy that involves using data and analytics to predict when equipment or machines are likely to fail or require maintenance, so that maintenance can be scheduled in advance. This contrasts with reactive maintenance, where maintenance is only carried out when a problem or failure occurs, or preventive maintenance, where maintenance is performed on a predetermined schedule regardless of the current condition of the equipment.

In the context of Industry 4.0, predictive maintenance can be enabled using sensors and IoT technologies, which collect data on the performance and condition of equipment and machinery. This data can then be analyzed using predictive analytics techniques, such as ML algorithms that can identify patterns and trends that indicate when maintenance is likely to be needed. By scheduling maintenance in advance, it is possible to minimize the risk of unplanned downtime and improve the overall reliability and efficiency of the production process. Our partners such as Wipro and LTI are leveraging Snowflake’s AI/ML capabilities as well as Snowpark to build predictive maintenance applications. 

Quality: There are several ways in which Industry 4.0 technologies can be used to improve quality in manufacturing. One of the most common approaches is to automate the quality control process by adopting computer vision-based quality control. Instead of a human performing visual inspection, this approach involves a CV system that does the visual inspection, supported by an AI model trained to detect defects. This significantly improves the inspection accuracy and throughput for this process. Snowflake is working with partners such as Wipro and Dataiku that have built solution offerings on top of the Snowflake Data Cloud to execute this use case.  

Energy optimization: Another important use case of interest for our customers is optimizing energy consumption at a manufacturing facility where the biggest consumers of energy are the machines running production, followed by the HVAC systems and air compressors. Customers need to understand peak load characteristics for the manufacturing facility and to reduce the peak load by contextualizing the energy data with respect to line, machine, shift, operator, and products being produced. In addition, customers seek to optimize the overall energy consumption by leveraging AI/ML algorithms to heat or cool the production facility instead of set point-based functioning thermostats controlling the HVAC systems. Snowflake is working with partners such as Opto 22, which provides energy sensors that can ingest energy data with high fidelity from power meters and machines. Snowpark and Streamlit can then be leveraged to use this data for visualization, contextualization, and optimization work. 

To learn more about how Snowflake’s Data Cloud can help your manufacturing business embrace the new era of Industry 4.0, visit Snowflake for Manufacturing.