Top 5 manufacturing supply chain analytics use cases | 7wData

Top 5 manufacturing supply chain analytics use cases | 7wData

For any successful manufacturing firm, it is very important to find a new way to streamline their operations. Starting from the raw materials to WIPs, logistics and of course, the final product, manufacturing is an intricate process with countless moving parts.

Besides the tangible aspects of the manufacturing industry, there are financial and managerial aspects to oversee, not to mention a perpetually changing market demand and aggressive competition.

The manufacturing industry is undergoing a lot of automation, cost pressure is always high and so are the margins. Bringing in efficiency and productivity gain is important to ensure you are competitive as well as profitable. Hence analysing different moving parts spread across functions to make sure they seamlessly work in tandem to bring down the cost, push up the utilization and to increase the margins.

It takes months and extreme due diligence in examining each stage, coming up with innovative ideas and finally implementing them. The recent technological disruptions are not only good for manufacturers’ internal processes but are also a way to remain competitive and achieving organisation goals.

So, how can these organisations improve their underlying manufacturing processes and practices?

Let’s dive into some analytics use cases for manufacturing.

There are a series of processes going on in parallel and it generates a huge volume of data ranging from machine run-time to no. of units produced. When all this information is decoupled, analysed, and resynched together in a system, the results can be very powerful.

Machinery and systems are constantly operating for long stretches under heavy load and any fault can significantly impact your production. A reactive approach is not sustainable, using predictive analytics systems, factory supervisors can predict such failures in advance and avoid the downtime. A practice that is catching trend is self-correcting machines that warn once such threshold is achieved.

Machine utilisation and effectiveness data can lead to some crucial insights like what has been the run time for each machine, what were the reasons for any deviations from the schedule (human-error, raw-material scarcity, technical issues, etc.). Using analytics systems for Predictive Asset Maintenance is a growing trend across the manufacturing industry, IoT data from sensors can be pulled and analysed to understand the pain areas and help in improving machine efficiency.

The use of analytics is often limited to warehouse optimisation and forward logistics. However, while performing the Reverse Logistics Analytics on numerous occasions organisations have dug up some hidden insights into sunk cost and inefficiencies cropping up from certain activities.

Analysis of returned items provide insights related to which stage of the production process is generating the maximum volume of faulty pieces or end products. It leads to avoiding loss emanating due to customers’ dissatisfaction as well as the sunk cost associated with manufacturing them. Further, it also adds to optimizing the existing processes, updating the vendor scorecard and ratings.

Advanced analytics in manufacturing maximizes operational efficiency through three key applications: Predictive Asset Maintenance, Yield/throughput analysis and Supply chain optimization with advanced modelling.

There are several areas of supply chain management where data analytics can be of significant help.

Now, Suppliers & Manufacturers have a choice to share their production data with their partners and customers to bring in transparency and gain trust. This way the manufacturer can see exactly whether the supplier is delayed with production just in time to avoid any Lead Time. At the same time, the suppliers can pre-empt any such incidence and modify their production output accordingly.

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