Manufacturers are bombarded with endless streams of data from suppliers to production to customers. Business intelligence (BI) is an essential tool for collecting, classifying and evaluating information from all levels of an organisation. Within manufacturing, BI applications can filter through data from across the supply chain – from suppliers and materials to production and shipping. Business intelligence and big data has become the driving force of more and more software implementations, for the manufacturing industry however business intelligence software implementation has its own particular considerations.
Big data – Getting the most out of your business intelligence application
The first step is data preparation across the organisation. This includes defining the data, determining how to measure it and assessing its value to the business. BI goes beyond simply gathering and storing company data. BI technology enables manufacturers to make real-time, informed decisions to improve shop floor efficiencies. Providing insight into production line yield trends, product configurations, BOM profitability, cost to order, inventory levels, external trends, sales strategy development, turnover and forecasting, resource management and more. BI investment should be part of a wider business strategy, “…the impact of developing a superior capacity to take advantage of Big Data will confer enhanced competitive advantage over the long term and is therefore well worth the investment to create this capability. But the converse is also true. In a Big Data world, a competitor that fails to sufficiently develop its capabilities will be left behind.”
The next challenge is to define which data will be aggregated. Many traditional BI applications begin with a dedicated data warehouse. However as many BI tools are already incorporating data from various source systems using the power of the cloud, manufacturers may choose not to implement a data warehouse, for example when raw materials flow in from thousands of locations to arrive across the globe on-time, on-spec and in-budget. Cloud can provide the computational glue that enables data to be collected and analysed to support the IoT. Either way, a business will benefit from having one logical place where all pieces of data can be stored and related to one another.
Now that you have pinpointed the data you wish to include in your BI project, you need to understand how accurate it is. Manufacturers are increasingly focused on data quality processes and technologies to ensure that BI systems display accurate information. Many manufacturers are looking to product information management (PIM) in hand with their BI solution to guarantee reliable product data throughout applications and departments.
Once data audit and preparation is complete, the BI implementation can begin. At this point, it is vital that manufacturing professionals have an idea of which metrics they want measure and which data should be shown in dashboards in order to get the most value from their BI system.
Business intelligence and big data benefits to manufacturers
BI software can transform raw data from a variety of sources into reports, dashboards, scorecards key performance indicators (KPIs) and other metrics. Presenting information in context, to make informed decisions quickly, such as making changes to production scheduling. Manufacturers can establish links between operational KPIs and critical business metrics. As a result, they gain insight into everything from asset utilisation to machine uptime and warehouse productivity while also monitoring energy usage, uncovering the cause of quality problems, and ensuring consistent production across multiple lines. Many manufacturers are seeing process improvements in the warehouse, throughout the supply chain and beyond. Here’s how –
- Business intelligence and big data can help optimise production schedules in regards to customer, supplier, production schedules and cost limits
- LNS Research and MESA highlight the top three areas business intelligence and big data can improve manufacturing performance; better forecasts of product demand and production (46%), understanding warehouse performance across multiple metrics (45%) and providing service and support to customers faster (39%).
- Providing visibility into supplier quality levels, and greater accuracy in predicting supplier performance over time. Using big data and advanced analytics, manufacturers are able to view product quality and delivery accuracy in real-time, making trade-offs on which suppliers receive the most time-sensitive orders. Managing to quality metrics becomes the priority over measuring delivery schedule performance alone.
- Measuring compliance and traceability at machine level using sensors on all machinery to provide immediate visibility into operating performance. Advanced analytics can also show quality, performance and training discrepancies by each machine and its operators to streamline workflows.
- Identify profitable build-to-order products with the greatest production efficiency. Customised products can deliver high margins but they can cost significantly more to produce. Using advanced analytics, manufacturers can monitor the impact to production schedules.
- Manufacturers can get insight into crucial corporate-wide quality management parameters and compliance.
- By combining daily production and financial metrics, manufacturers can profitably scale their operations.
- Manufacturers can use sensors to send alerts for preventative maintenance. Business intelligence and big data will make the level of recommendations contextual for the first time so customers can get greater value. Our previous blog discusses this point in more detail.
- BI can also help to improve efficiency at manufacturing organisations. Globalisation and new competition, in addition to weak economies, are forcing manufacturers to run leaner and meaner. At the same time, they must produce a greater number of types of products, if not custom products, as well as maintain increasingly difficult standards.
Technology applications including Business Intelligence tools such as Microsoft Power BI, Jet Reports or TARGIT can harness and interpret big data both on the production line and back office, whilst cloud platforms such as Microsoft Azure can connect data from across various sources.
BI tools are no longer separate from business management solutions. With the power of Cortana Analytics and Azure Machine Learning, customer relationship management systems such as Microsoft Dynamics CRM can inform predictive analytics and machine learning – supporting intelligent manufacturing and distribution operations. ERP systems such as Microsoft Dynamics AX incorporates embedded Power BI to surface contextual information on role based workspaces and dashboards, and providing next best action recommendations on the analysis discovered. Find out more about how technology can fuel manufacturing productivity by contacting Prodware.