An overview of manufacturing analytics provides a basic understanding of the technology and its benefits. Manufacturing analytics relies upon predictive analytics and can quickly summarize massive amounts of data and measure metrics in real-time. Manufacturing analytics dashboards combine metrics into standard dashboard components and can provide access to data by user role, restricting data to only the most important users. Scoreboards are a common example of this technology in action. These systems can also automatically find anomalies in data and send real-time alerts.
Analysis of requirements for big data platforms
The cross-sector requirements for big data platforms for manufacturing analytics were identified to maximize the potential of big data in the public and private sectors. While most conditions exist in some form within each sector, the significance of each differs. The criteria identified in two or more sectors were then categorized into five high-level requirements and 12 sub-level requirements, with the corresponding challenges. Listed below are the key findings from analyzing requirements for big data platforms for manufacturing analytics.
The first step in using big data for manufacturing analytics is to formalize heterogeneous data. This data is then filtered, de-duplication, and synchronized. The data may be sent for long-term storage and returned to the manufacturing infrastructure. Pre-processed data may then be analyzed for predictive analytics or knowledge mining from real-time streams. Some applications use the data for historical pattern search or predictive analytics.
Analysis of recent big data platforms for process data analysis
The current industrial revolution is focusing on the improvement of existing procedures and processes through the use of automated services. In addition, government initiatives to improve manufacturing operations have increased the adoption of advanced automation technologies. These developments are expected to create immense market opportunities for big data solutions. Big data analytics help manufacturers identify and improve their processes and automate operations. In addition, it helps them understand the risks and opportunities in manufacturing. By using this technology, manufacturers can improve their operational efficiency and reduce costs.
The benefits of using Big Data analytics are numerous for manufacturers, including better inventory management. Big Data helps manufacturers keep track of goods throughout the logistics cycle. With this technology, manufacturers can test products and processes before they are actually manufactured. Digital twins, VR environments, and manufacturing process simulations can help manufacturers test different products, settings, and scenarios. Moreover, they can use tools to predict product and process performance.
Growth prospects
The manufacturing analytics market in the U.S. is expected to surpass $2.5 billion in revenue by 2030. Over the forecast period, the market will expand at a CAGR of 17.6%. Rising demand for operational visibility and the focus on supply chain management are the major factors fueling the market. As a result, manufacturers are adopting manufacturing analytics solutions to help companies succeed in their business. The report analyzes eighteen countries globally and the five major regions.
The market for manufacturing analytics is segmented by deployment type, end-user industry, and geography. In terms of application, manufacturing analytics is growing at a CAGR of 24% during the forecast period. Applications of analytics in manufacturing are increasing to streamline the supply-chain logistics and minimize operational costs. The emergence of the industrial internet of things facilitates the adoption of advanced data management strategies. However, there are several challenges to the market growth.
Cost of manufacturing analytics
In order to assess the cost of production, manufacturers should be aware of their actual labor and machine time, as well as the overhead burdens associated with each hour. Manufacturing analytics can help manufacturers track downtime and OEE by providing real-time information and displaying these metrics on the floor. Moreover, the use of this solution can save the manufacturer money on raw materials and production by identifying problems at an early stage. Therefore, manufacturers should invest in this technology to better understand their operations.
Manufacturers can leverage the power of manufacturing analytics to increase production, improve efficiency, and reduce costs. Margin pressure is increasing in the manufacturing industry, which is why manufacturers need to use these tools to increase productivity and maximize their bottom line. Moreover, manufacturing analytics can also unlock new growth opportunities. For example, a major tobacco company sought the help of a data and analytics team to develop a repeatable method for analyzing different data sources.
