While big data and artificial intelligence (AI) are certainly changing the face of industry, research from the Boston Consulting Group (BCG) and the World Economic Forum (WEF) has found they could also produce benefits worth $100 billion for businesses worldwide. This conclusion is based on a survey of 996 manufacturing managers, and by calculating the savings their various suggestions could bring to manufacturing companies.
The idea of data sharing was especially popular among the report’s findings. 47% of the managers surveyed believed that optimized assets were the biggest benefit of sharing data more widely. But how could this contribute towards billions in savings?
Learning to save
BCG and WEF’s study estimates that industry could save $40 billion by optimizing assets through machine learning. Here, the suggestion is that interconnected machines can use predictive algorithms to improve each other’s performance. With such data, machines can help in reducing downtime and optimizing processes.
A further estimated $40 billion in savings could be derived by more effectively tracking products along the supply chain. By knowing where a product is at any point in time, manufacturing managers can gain visibility that allows them to better plan their processes. The results can include reduced inventory levels and better-informed, even automated, purchasing decisions. Lower stock levels and more targeted purchase orders can directly benefit the bottom line.
In addition, BCG and WEF’s report estimates that a further $15 billion could be saved by knowing the origin and condition of products along the entire value chain. This knowledge can be invested into improved quality management, by finding the root causes of faulty products more quickly and accurately. Companies that operate in highly regulated environments, like the pharmaceutical industry for instance, could realize a large part of these savings.
Obstacles to data sharing
While BCG and WEF’s suggested uses for data sharing clearly have their advantages, manufacturers must first overcome several challenges.
One involves implementing the computing infrastructure needed to connect machines, companies and industries, which can be costly. Buying entirely new equipment is expensive and time-consuming; therefore, a faster and potentially more cost-effective way to digitalize processes is by retrofitting new technologies to old equipment.
One way is through the use of smart sensors, the global market for which is growing at a 19% annual rate and is expected to reach $60 billion by 2022, according to Deloitte. Sensors can be fitted to existing machines to gather data on their performance and allow decision-making about production processes, in real-time. A specialist supplier of industrial automation parts can help in sourcing the retrofitting components needed to fully embrace digitalization.
An example of this would be in fitting a range of computer numerical control (CNC) machines with sensors that supply data about their performance. So, if a CNC machine in one location produces burn marks because of an improper feed speed, the same type of machine at another location could automatically correct its speed to avoid the same problem — otherwise known as machine learning. By analyzing the resulting stream of data, manufacturers can glean clues on common faults and maintenance cycles, and achieve tangible cost savings.
Data sharing can also involve sharing design specifications and tolerances with suppliers and customers. This is highly relevant to manufacturing because it can reduce tolerance stacking that occurs when different suppliers all maximize their tolerance allowances and, as a result, the final product falls short of the customer’s requirements.
A further obstacle that manufacturers must overcome is siloed thinking. The Harvard Business Review conducted research on the benefits of breaking down silos. It concluded that many large and small companies need to share data horizontally across the organisation, rather than just vertically up and down the businesses’ ranks. Companies are missing out on revenue opportunities because of this. With digitalized data sharing, however, they can share information horizontally as well as vertically.
Despite these benefits, more possibilities for data sharing can also create more security issues. Data sharing multiplies the access points through which hackers can infiltrate computers, and that’s why manufacturers should put an effective systems hardening strategy in place, to close such loopholes. Effective data sharing should be accompanied by regular systems hardening audits to identify system vulnerabilities, and help install software patches that secure the sharing of data.
Better to share?
With effective digitalization strategies and the use of smart technologies, manufacturers can glean useful data and share this securely and effectively among stakeholders in ways that benefit their bottom line. AI and big data have made it possible for a stream of information — such as that gleaned from sensors fitted to manufacturing machines — to be continuously analyzed and contribute towards asset optimization.
The advantages of data sharing are clear, but manufacturers must make the bold decision to not play with their cards so close to their chest. While $100 billion is a huge estimate, companies can nevertheless harness the power of digitalization to share data safely, securely and in ways that significantly benefit their bottom line.
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- How Manufacturers Could Save $100 Billion Through Data Sharing - August 21, 2020