Real Time Shop Floor Analytics


A leading member of the meat and food processing industry, this client produces smoked and processed meats for the retail and food service industries.


Because of the impact of delays in production processes, the client needed the ability to analyze and respond to production related issues before a problem or event happened that could disrupt or even shut down production processes. The process of collecting, analyzing and integrating external data sources for the purposes of performing predictive maintenance on the shop floor can be a daunting task. This data is generated from a variety of equipment or machines, sensors, mobile devices, social media and the information systems that run the business. In most cases the data is generated in real-time and the client realized the potential business value by harnessing and analyzing the data in real-time as well. Having this capability is critical to the successful use of predictive analytics and using the information to increase production yields, increase profit margins, and avoid production downtime. The information can be used to predict equipment failures or detect anomalies in production processes before problems arise.


Start with identifying business processes that have the greatest potential impact for generating real benefits or posing the greatest risks to the company. Focus on identifying areas that will increase production yields and extend the useful life (RUL) of critical shop floor equipment.


As a result of the solution, the client was able to reduce production downtime due to equipment failure and implement more effective preventive maintenance processes. Ultimately these improvements will help to maximize their production yields across all products.


This solution used a mix of Microsoft Power BI, SQL Server, and Azure technologies.