In reshaping the world toward a new, post-pandemic normal, the industry must leverage digital transformation at an accelerated pace. This shift is already happening — according to IBM, 67 percent of manufacturers have accelerated digital projects since the start of the COVID-19 pandemic. While improved operational efficiency is typically the reason for these changes, the industry should capitalize on the convergence of Industry 4.0 and environmental, social, and governance (ESG) goals to improve their sustainability credentials. Analyzing and acting on related data, however, could hold the key to reducing manufacturing’s waste problem.
Despite the increased incorporation of digital tools, only 59 percent of manufacturers cite improved sustainability as a reason to digitalize their operations. Digitalization’s potential extends far beyond its perceived applications, however. Data collated by the Association of Swedish Engineering Industries (Teknikföretagen) emphasizes the importance of digital tools in achieving sustainability goals. Leveraging IT technology in other sectors, such as manufacturing, has the potential to reduce total CO2 emissions by as much as 20 percent.
Increasing digital tool use is vital to aligning manufacturing activity with the Paris Agreement, increasing resource efficiency and reducing waste. Globally, industrial waste generation is almost 18 times greater than municipal solid waste, according to the most recent data from the World Bank.
All manufacturing facilities generate waste, but a lot of that waste could be prevented. Overproduction and defect output are two of the most common contributors to waste generation. Facilities running on legacy technologies that lack the sophistication to deal with the challenges of modern-day manufacturing are far too common. Machine shops that rely on older (legacy) machines and systems that aren’t, or haven’t yet been, upgraded to today’s Industry 4.0 standards. Such shops are missing out on the potential of digitalization to tackle issues like overproduction and defect output. However, this doesn’t mean that manufacturers should accept huge waste generation as an unavoidable consequence of production. Industrial waste must be reduced, and data can play a key role in achieving this goal — if manufacturers know what to do with the data.
Improving Operational Efficiency
Every manufacturing facility, regardless of its size, complexity, or age, generates a significant amount of data every day. In a smart factory, this data can include everything from equipment performance tracking to product quality assessment and is collected via sensors installed on each machine. Starting small, with each individual machine’s data, is a good starting point to reduce waste on a large scale.
Small changes to individual processes can collectively have a huge impact on operational efficiency. Analyzing data from machinery on the shop floor, for example, can enable manufacturers to monitor and improve processes based on the data. Individual machine processes that significantly contribute to overall energy use can be optimized by detecting inefficient processes, streamlining production and logistics planning, and predicting upcoming maintenance needs. By making several smaller processes more energy efficient, data can be used to control — and reduce — overall energy consumption.
Energy inefficiencies can be identified in real time, giving manufacturers the opportunity to identify potential causes and solutions. For example, when looking at energy consumption data, manufacturers may discover one piece of equipment that uses significantly more energy than others. Using this information, manufacturers can then identify the cause of increased power consumption and implement improvements to streamline machine efficiency and reduce energy waste.
Continuously analyzing data in real time can also help reduce machine maintenance. Data analytics can identify upcoming problems before they happen. If a machine experiences a problem or a change in performance, it may start to produce parts that are not up to standard, which would ultimately end up discarded as waste. Data on machine performance can help to identify minute changes in machine behavior as soon as they occur so that engineers can carry out predictive maintenance before multiple defective products are produced.
The CoroPlus® suite of products data-driven machining offered by Sandvik Coromant, for example, can help manufacturing organizations improve efficiency, reduce waste, and increase productivity. The CoroPlus Process Control monitors machines in real time and triggers actions in accordance with programmed protocols. If specific, predetermined issues occur, the software automatically triggers a correctional action, such as stopping the machine or replacing a worn cutting tool. Conducting maintenance in this way can improve operational efficiency by as much as 89 percent and reduces waste by allowing manufacturers to assess data, monitor machine performance, and identify faults before they occur.
Considering the Whole Life Cycle
Data produced by conducting life-cycle assessments (LCA) can also help reduce waste. An LCA outlines the environmental impact of a product at every stage of its life span by considering how a product’s raw materials are extracted; the quantity of resources required; the materials and energy used during manufacturing, packaging, and distribution; the impacts of the product’s functional use; and the waste and pollution created at the end of the product’s life.
By considering every step of the product’s life cycle, an LCA leaves no stone unturned. Once an LCA has been completed, manufacturers can identify significant sustainability flaws in a product, evaluate the sustainability of products still in development, and design new, more sustainable solutions.
Packaging assessment is also necessary because the resources required to produce — and later dispose of — packaging raises many environmental concerns. The problem is shared globally. In the UK, for example, nearly 44 million tons of packaging waste are produced annually by commerce and industry, and 28 percent of total municipal solid waste in the United States is attributed to packaging.
Sandvik Coromant recognizes the problem with packaging, even for products like cutting tools, and recently launched its package selector application (PSA). The PSA uses data to analyze a 3D CAD model of the company’s product to be packaged, identifies its critical points, and uses an AI algorithm to recommend the smallest amount of packaging possible. This process improves the LCA of the company’s tools by reducing packaging waste produced for tool distribution to manufacturers.
Circularity Through Transparency
Data can also be used to facilitate a closed-loop manufacturing chain, in which waste from one process is used as a resource in another. Operating in this way promotes a circular manufacturing economy, minimizing waste as much as possible through continuous reuse.
Using data from an LCA, along with machine data, manufacturers can improve the efficiency and circularity of their products through constant product and process improvements. Efficiently implementing a production system of this kind requires a robust data strategy. To create a strong data architecture, manufacturers need a digital infrastructure that can easily synchronize operations, potentially across several locations, and identify opportunities to utilize waste that would otherwise be in silos. Machine connectivity and sensor-embedded tools make digital machining accessible to manufacturers, equipping them with the close to cutting-edge data needed to make use of their waste resources for more sustainable operations.
Data can seem overwhelming at first — with such vast quantities available, it can be hard to determine what to do with it. The correct, tailored strategy, however, can prove to be effective in reducing industrial waste by improving efficiency; enabling predictive maintenance; inspiring innovative product and packaging development; and streamlining resource management across machines, facilities, and even entire companies.
This article was written by V. R. Vijay Anand, Head of Digital Machining, Sandvik Coromant, Sandviken, Sweden. For more information, visit here .