Empowering your manufacturing operations with data analytics.

Key Takeaways for Manufacturers

• Invest in data analytics capabilities 🛠️🔍

  Implement and set up a solid data collection system across the organisation, in your production line and supply chain.

• Deploy the right analytics tools and capabilities🛠️📊

  Implement data analytics tools that address your challenges and fit your specific needs otherwise you won’t be able to extract relevant data to help you make the right decisions.

• Empower your people with data analysis skills & capabilities🤖👥

  Train your team to work and operate with data, interpret it and act upon the insights that are generated.

• Continuous improvement & process optimization🔄📈

  Regularly analyse your processes, gather feedback and make adjustments to continuously improve they way you work with data, analyse it and make decisions upon it.

📊💡Driving operational success with improved data analytics.

Manufacturing is a very dynamic area of the economy with lots of challenges, disruptions and constantly being impacted by the industrial revolutions that happen in different economic cycles and technology advancements. Still with all disruptions and technological advancements manufacturing companies still struggle in many areas of their operations one of them being the way they use data to uncover insights that lead to certain decisions.

🏭 Many manufacturing companies still do a lot of stuff manually which leads to data and information being very sparse or sometimes they don’t even know that can extract relevant data from specific processes that can help them in better decision making around their business and operations.

Here are a couple of challenges manufacturing companies face and how implementing various data analytics solutions. ❌

❌ Unexpected downtime of manufacturing machines. ⏱️🚧

Not everything just works forever so it happens that sometimes manufacturing machines breakdown and after long usage glitches can happen and oftentimes many of them can be found before they even occur.

✅ How data analytics can help: Predictive maintenance 📊🔧

Deploying a data analytics tool can help collect real time data from the machinery to spot or predict potential inefficiencies and breakdowns. Having this predictive analytics capabilities you can better plan your manufacturing operations and schedule maintenance works based on insights from the collected data.

❌ Quality fluctuations and inconsistency 📉🛠️

Not delivering the same quality and product on a consistent basis will heavily impact customer satisfaction and it will result in increased costs and revenue decline because of returned orders, post-production repairs or new product replacement. This will also affect your reputation and brand on the market.

✅ How data analytics can help: Real-time quality monitoring

Identify key quality metrics for each stage of the manufacturing process to put in place a framework and system for measuring quality. Implement sensors and IoT solutions that are connected to the manufacturing lines and machineries to extract the real-time data and intertwine it with your quality framework. Before implementing various sensors and IoT solutions check the current capabilities of your manufacturing machineries and line, many times they already have data extraction capabilities that just need some tweaks and custom integrations to be able to extract the relevant data you need. Also make sure that all the data you extract is centralised in a cloud platform or on-premise server depending on your requirements and infrastructure.

Set up automated alerts and notifications when the process deviates from your manufacturing quality standards. These alerts can be sent to relevant team members to act immediately and intervene if necessary.

❌ Inefficient Supply Chain Management 📉🌐

Disruptions, inefficiencies and delays influence a lot product deliveries, production time, revenues and ultimately client happiness. Not having control over your supply chain will influence your long term growth and bottom line.

✅ How data analytics can help: Supply chain optimization 📊🌐

Implement a data-driven approach in demand forecasting. Start by analysing historical data on sales to identify various patterns for demand, seasonality and trends. Involve your marketing and sales team in this process to identify and gather more relevant insights and ensure a more accurate forecast. You can even go more advanced and implement machine learning capabilities to offer you more assistance and give you accurate recommendations for you to plan the inventory levels accordingly.

Here you can also analyse and track supplier performance to identify bottlenecks, extract valuable insights on the work process with them and improve the way you work with suppliers.

Conclusion 🚀

This use cases of data analytics show how you can turn operational data into useful insights that will not only help companies immediately solve the issues but also create a solid base for long term growth and operational efficiency.

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