The Industrial Internet of Things (IIoT), big data analytics, automation, and related technologies are revolutionizing supply chains around the world. Never before have manufacturers been able to gain as much visibility into their production and distribution processes as they do now.
With the use of sensors, it’s possible to know the operational and productivity state of every piece of manufacturing equipment in your organization. Products can be tracked from their raw material source to their manufacturing and assembly lines, and on to packaging, storage, and distribution.
Product digitization has made all this possible. Organizations can now embed unique identifiers on physical products and register them as digital assets. (To understand how this process works, read our article on Five Steps To Digitizing Your Product). This process enables companies to collect data that can be combined, compared, and analyzed to find ways to optimize the supply chain.
How To Optimize The Supply Chain With Data
When you gather accurate data and ask strategic questions during your analysis, you can come up with strategies that make a significant impact on your supply chain. These strategies can affect a wide range of business performance indicators, including revenue, productivity, and customer satisfaction.
Speaking at a panel discussion on the future of US manufacturing, Stephen Ezell, the Vice President of Global Innovation Policy at the Information Technology & Innovation Foundation, shared: “One US automaker estimates that its implementation of IIoT combined with big data analytics has saved it over $2 billion in operational costs over the past five years—a return on investment of over 400 percent of the approximately $350 million investment that they made.”
Ezell also cites analysts’ predictions that smart manufacturing can increase the productivity of factories all over the world by 25 percent over the next decade, as well as reduce safety-related incidents by 25 percent. It can also improve energy efficiency and innovation.
These benefits can only be attained, though, by using the data gathered through product digitization to inform operational decisions. Businesses need to understand how to use data to optimize their processes, including their supply chain. Here are some strategies to do so.
Link Different Data Streams From Internal And External Sources
When kept in silos, data sets can provide you with limited insights. Patterns and connections emerge when you combine and compare information from different machines, systems, and locations. After all, it’s important for different teams and departments to understand what’s happening in other parts of the supply chain.
For example, sales trends and forecasts can be compared with inventory data. US-based retail giant Walmart does this with the use of an app that lets store managers access real-time sales information and control stock orders. By comparing sales and inventory information, they can prevent oversupply and avoid high levels of out-of-stock merchandise.
McKinsey shares the example of one industrial company that implemented a central data engine. The engine processed data streams from different sources—both internal and across the company’s supply network. By linking different data streams, they were able to see how one part of the supply chain affected operations in other areas. They identified systemic issues, such as how mismatched lead times and past-due purchase orders often “prevented reliable indicators of future demand from reaching suppliers”.
As a result of linking and comparing cross-functional data, the company improved planning efficiency, increased planning productivity by up to 30 percent, and reduced inventory by 20 percent.
Share Data With Major Suppliers
One advantage of digitalizing your supply chain is the ability to easily collect and collate data on your products and their movement. This information is not only useful to you as the producer or manufacturer. Other parties, such as your raw materials supplier and your shipping partner, will be able to make use of this data to optimize their process and benefit your operations as a result
3M, a global manufacturing company, shared their planning systems data with one of their largest chemical suppliers. The supplier also shared their own planning systems data with 3M. As a result, the manufacturer gained visibility into the supplier’s capacity, while the latter could see 3M’s consumption levels. This data-sharing helped the supplier anticipate 3M’s needs for chemicals. 3M was able to secure supplies in advance, ensuring that production would not be disrupted even during unexpected surges of product demand.
Identify Barriers To Productivity
Data can help you identify process bottlenecks, equipment underutilization, and energy inefficiency, among other issues. By spotting these problems, you can improve productivity across your supply chain.
Rockwell Automation, a maker of industrial automation software, helped one coffee manufacturer adopt an IIoT solution to improve productivity. They equipped the coffee production equipment with sensors to gather real-time data on their operational states.
The manufacturer found that 40 percent of their machines were offline at any given point during the production process, as these had to be cleaned of contaminant vapors. To make up for the offline equipment and maintain productivity, they needed to have as many as 100 extra machines.
The real-time operational data also revealed a significant difference in the rate of fouling between machines that produced caffeinated coffee and those that produced decaffeinated ones. This helped the manufacturer to more accurately predict which machines needed to be taken offline, and when; this enabled them to reduce the number of surplus machines.
Predict Equipment & Systems Failure
Unexpected breakdowns of equipment or systems can impede production. They can also increase delivery times, drive up repair costs, and reduce product quality and consistency. However, data can reveal a series of events and environmental conditions that typically lead to failures.
When Western Digital, one of the world’s largest manufacturers of disk drives, analyzed machine-to-machine data, they discovered patterns that predicted failure. This enabled the company’s engineers to spot possible equipment and system failures early in the production process.
As a result, they were able to reduce the number of yield losses and prevent defective hard drives from being sold to consumers. By doing so, they were able to decrease the number of product returns and improve customer loyalty.
Share Data with Policymakers and Certification Bodies
Supply chain data can help speed up tedious product certification processes while reducing costs and complexity. It can also influence the creation of policies that affect your industry.
For instance, by implementing track-and-trace solutions, organizations can trace crops to specific farms. This supports the process of certifying produce as organic, as certification standards also require that surrounding farms practice safe agricultural methods. Sensors can also be used for analyzing soil properties and identifying the presence of harmful chemicals.
Food producers can use their digital supply chain data to prove to regulatory bodies that they meet safety standards. For example, food supply chain data, especially those encoded in an unalterable blockchain ledger, can provide transparency into the safety standards your supplier’s supplier, as well as monitor food temperature and environmental factors during distribution and delivery.
In 2018, the UK’s Food Standards Agency completed a pilot scheme that used blockchain to track meat supplies and quality. It shared data with cattle slaughterhouses to verify compliance with relevant food regulations. The agency aims to expand the program to allow the sharing of inspection results with private companies.
Digital maps of farmland can provide information on seasonal cultivation and harvests. This, in turn, can inform agricultural policies, such as the provision of subsidies, funds, seeds, and equipment. Crop monitoring through geo-mapping can help with assessing the extent and effects of environmental problems, such as drought, in a certain area.
Turning Data Into Value
Digitalization enables you to gather data and use it to create value for your organization. This value may come in the form of cost savings and more efficient processes. It may also manifest to benefit engagements/relationships with third parties, such as by strengthening supplier relationships and speeding up the certification process. Consumers may also benefit, especially when your data helps you improve product quality and consistency.
The key is to work across different departments and with major suppliers to identify metrics to measure and problems to solve. Each department may have its own information requirements, as well as a distinct perspective of how certain data sets affect its operations. Engineers may want to identify patterns that predict systems failures, sales managers may want to see how well a new product fares in a certain store, while customer service teams may gather information on item returns.
Keep in mind that data analysis is an inherent part of digitalization—not an option or an afterthought. As such, identifying data requirements, analysis tools, and key metrics must be part of your company’s digital transformation plan. On the other hand, if you’ve already digitalized your supply chain but are not gleaning significant insights, you may need to assess the types of data you collect and the systems you’ve put in place to share, categorize, and analyze them. The strategies above are a good place to start in optimizing your supply chain with data.