The growing narrative around sustainable development has brought into focus the need to align environment and financial goals at the enterprise level. For a long time, sustainable manufacturing was viewed as a zero-sum game between economic viability and environmental consideration. However, with digital transformation initiatives providing advanced data analytics capabilities, sustainable practices have ceased to be an either-or scenario in manufacturing. Enterprises can now use data analytics to drive sustainable practices that include reducing waste, improving safety and reducing production costs.
Automation and digitization of the manufacturing process has bubbled up data that could be analyzed to provide valuable business insights on a range of activities. From planning production to optimizing the supply chain and implementing systems that curtail waste, manufacturing analytics now enables safer, cheaper and more eco-friendly decision-making that drives sustainable growth with profits.
The shift towards sustainable manufacturing
For more than a decade, manufacturing companies have witnessed varied levels of disruptions on account of fast-changing customer preferences, demand uncertainty and competition. These demand swings have necessitated both operational and capital cost reduction. These trends resulted in the Industry 4.0 approach towards driving sustainable change. At the core of this approach lies a digital stack that helps manufacturers pursue multiple use cases that go beyond mere productivity. They focus on enhanced sustainability, agility, speed to market, customization and customer satisfaction too.
As the production scaled up resulting in higher volumes of data, the use of efficient and automated data processing methods have become critical to generate insightful analytics. For example, a company focusing on cybersecurity may utilize classification algorithms to sift through exhaustive web data to provide analytics on a dashboard for their clients. Similarly, enterprises use data analytics to discover hidden patterns between various operational arms that could provide learnings to better utilize their production units and supply chains.
The demand for clean and green solutions are growing as more customers are inclined towards eco-friendly products. A report by Mckinsey titled “State of New Business Building” says 92% of executives consider that upcoming businesses that come up over the next five years will focus on some degree of sustainability. And 42% of these executives are committing to make sustainability the unique selling point for their enterprises. Moreover, tech solutions help them attain economies of scale, which is critical to minimize wastage of resources
The direct impact of analytics on waste management
Data analytics is emerging as a powerful tool for reducing waste and lowering labor costs in the manufacturing space. An automotive factory in Europe implemented a digitally enabled device with apps at each workstation to connect workers across divisions. It used digital tracking and tracing and a flexible logistic flow to ensure that parts and vehicles are traceable. RFID tags on the parts and packaging provided end-to-end automation to ensure that the assembly line operated efficiently at all times. It also connected robots to manage process flows efficiently and collect necessary data to optimize production flows that resulted in reduced losses.
In fact, research suggests that manufacturing companies using data-led digital transformation actually achieved tangible results such as a 50% reduction in warranty incidents, an increase in flexibility to deal with multiple product configurations and an overall 10% cut in manufacturing costs. Some companies also added smart maintenance and assistance programs installed in the factories that helped the quality assurance process. This mix of digital manufacturing and a strategy-focused process enhanced quality, reduced costs and improved productivity, while also cutting down unplanned downtimes by 25%.
Using data analytics to reduce waste
It is essential to know where data analytics is applicable to reduce manufacturing waste. There are mainly five key areas where data analytics should be ensured to derive effective results for reducing manufacturing wastes of a business. These include the choice and sourcing of quality materials, effective supply chain management to curb waste, improving operational efficiency, technological integration, enhancing product design and engineering process and transforming product design, production, sale, consumption and the waste disposal process.
- Inventory optimization – Manufacturing companies need to identify the issues in the production and distribution aspects of the supply chain. Inventory data analysis helps identify the overproduction and excess supply compared to the sales patterns. Take the case of Coca Cola which deploys AI to manage their retail outlet cabinet coolers’ inventory by identifying and counting various products. The AI tool integrates this with demand forecasting data to automate the calculation of restocking orders. Retailers receive a delivery option and information about predicted cooler demand to enhance customer service and increase Coca Cola’s sales.
- Production optimization – Once automation is in play at a production facility, the data that it provides helps enhance efficiencies besides evaluating mechanical challenges and possible downtimes. Additionally, these data points allow supervisors to monitor wastage of raw material as well as time in some manual processes. Siemens automated their gas turbine manufacturing process resulting in a 10% spike in productivity and a 6% decline in manufacturing time, alongside reducing gas and power wastage.
- Energy & resource optimization – Using advanced analytics, enterprises can reduce wastage of material, energy and water consumption at their plants. In fact, IBM used data to optimize their cooling systems at a data center in Dublin. The real-time data analytics platform used temperature sensors, energy meters, and machine learning algorithms to predict cooling requirements based on server utilization and weather data. As a result, IBM reduced energy consumption by 15%, water usage by 20%, and identified inefficiencies to optimize overall data center performance.
Even data analytics needs to work efficiently
While enterprises collect data by the terabytes, its impact on sustainability-driven growth has a direct correlation with how it is used efficiently. Today, a variety of devices can be used to enhance data collection capabilities, though the analytics part is largely driven by artificial intelligence and machine learning. A report from the World Economic Forum says digital technologies can fulfill close to 60% of the UN’s sustainable development goals or SDGs. By harnessing the power of data and digital technologies, organizations can make more informed decisions, identify areas for improvement and drive positive social impact, it said.
The growing impact of Internet of Things (IoT) devices has been instrumental in adding additional data points that could be analyzed to understand the role of human resources in reducing wastage at the workforce level. In fact, an earlier study by the McKinsey Global Institute says enterprises that use data analytics for workforce planning and optimization saw up to 20% improvement in productivity and a 15% reduction in labor costs.
And once the improvements for reducing various wastes are in place, it is equally important to monitor them. Variations or deviations are easily captured, which makes remeasuring analytics into a critical step in ensuring an organization’s sustainable development effectiveness through data analytics. A Harvard Business Review article says companies integrating sustainability into their core business strategy see an average 19% spike in operating profits and a 9% increase in revenue growth. Leveraging data analytics, organizations can identify opportunities for reducing waste, optimizing resource consumption, and enhancing supply chain sustainability.
Once all the automation and technological integration for using data analytics is in place, it is crucial to apply strong controls. This helps ensure that the redefined production process does not fall prey to inefficiencies. Applying warning systems and controls allow for the early detection of any manufacturing wastage.
Some Best Practices in data automation
- Identify the business problems you want to solve and define clear objectives for your data analytics automation initiatives. This could include optimizing production processes, reducing costs, improving quality, or increasing efficiency.
- Collect and validate data such as sensors, machines, and systems. Ensure that the data is accurate, complete, reliable, fit for purpose and free from errors.
- Select the right tools that align with your business objectives and are scalable to meet future needs. Consider cloud-based solutions to reduce costs and improve flexibility.
- Build a data analytics team that includes data analysts, data scientists, and data engineers who can work together to deliver data analytics automation initiatives. Train your staff to develop the necessary skills to work with data and analytics tools.
- Develop a data analytics roadmap that outlines the scope of your data analytics automation initiatives, including the data sources, data processing, analytics, and visualization. Prioritize initiatives based on their potential impact on the business.
- Establish data governance for data access, usage, and security. This includes setting up data access controls, data retention policies, and data privacy policies.
- Monitor and evaluate performance of your data analytics automation initiatives, measure the impact of your analytics efforts, and adjust your approach as needed.
In conclusion…
Data analytics is an essential tool for the manufacturing industry to ensure efficiency and effectiveness in production process models. By analyzing large amounts of data, businesses can identify any system flaws in their entire product cycle, which helps companies optimize their manufacturing processes, reduce waste, and enhance their productivity. Furthermore, data analytics has become even more critical as the market demands more eco-friendly solutions. By utilizing data analytics, businesses can reduce waste and opt for green solutions that are less harmful to the environment. This will help businesses meet their environmental targets and contribute to a better future for everyone.