3 Ways to Unlock the Value of Data in Manufacturing

Shaping the Future of Advanced Manufacturing with the World Economic Forum

As we announced last year, DiManEx is a proud member of the World Economic Forum’s Global Innovators Community. As a member, we contribute by sharing our knowledge and expertise in the area of supply chain optimization and additive manufacturing. Most recently, we contributed insights for Data Excellence: Transforming manufacturing and supply systems, a study conducted by the Forum and Boston Consulting Group (BCG). The study discusses 3 key ways manufacturing companies are unlocking value from data, listing examples from Airbus, Johnson & Johnson, and others. In this blog, we summarize the findings and illustrate how the combination of data analytics and technologies like 3D printing are shaping the future of the manufacturing industry.

3 ways manufacturers are unlocking value with data

A recent BCG survey of more than 1,300 manufacturing execs found that 72% of manufacturing companies consider advanced analytics to be more important now compared to three years ago. In the same survey, 91% consider data sharing to be at least somewhat important, and 71% think it has significant importance. The applications for data sharing and analytics in this space are vast. You’ll hear about predictive asset maintenance, improved product and raw material traceability, improving the reliability of deliveries, and so on. All of these initiatives revolve around 3 themes:

• Increased productivity
• Enhanced customer experience
• Positive impact on society and the environment

Increasing productivity

According to the Data Excellence report, “80% of surveyed companies cited productivity-related improvements as their primary motivation for implementing advanced analytics in manufacturing.” Optimizing net working capital is a key objective and supply chain management is one of the most mature applications. In our specific area, we see inventory reduction as a key driver for adopting analytics. Our customers combine the advanced analytics capabilities of our SaaS platform with our network of distributed additive manufacturing partners. We enable them to identify areas for supply chain optimization and secure parts on demand rather than carrying excess stock.

Overstocking carries a heavy price tag. It ties up your working capital and increases logistics and handling costs. Excess stock can quickly turn into obsolete stock, meaning you will eventually incur scrapping costs as well. With advanced analytics, you can identify which parts are more cost-effective to produce in short runs. These are typically parts with a high minimum order quantity, but low demand.

Another use case for improving productivity is securing parts for aging assets. Machine downtimes are caused 15% of the time by spare part supply issues. This takes a toll on productivity, as engineers are forced to spend 10–15% of their time tackling supply hurdles. With a platform like DiManEx, companies can get spare parts more rapidly. Lead times can be up to 95% faster.

Enhanced customer experience

Speaking of lead times, manufacturers are also using data to enable “just-in-time” delivery of critical goods. This application is especially common in the area of medical goods or perishables. Here, real-time tracking and tracing of critical materials prevents stock-outs and improves availability. Original Equipment Manufacturers are doing something similar to secure critical components. Long lead times can be a very big problem for OEMs trying to secure parts for service, maintenance and repair.

An appliance manufacturer, for instance, works with us to secure critical components for customer service. Instead of overstocking these components, they order just the right amount to deliver a timely service. The result? Higher customer satisfaction, better product reviews, and more sales — with no tied up working capital.

Positive impact on society and the environment

Manufacturers are also leveraging data to enable resource efficiency, with their eyes set on reducing energy and natural resource consumption. Waste reduction is another big area of concern. What we’ve seen is that companies can cut back on waste by identifying parts which have a high minimum order quantity but low demand, and 3D printing them instead of ordering in bulk. In this scenario, if they leverage a distributed AM network, they can also minimize logistics miles travelled by routing the print job to a facility that’s close to the delivery location — something we take care of as part of our end-to-end platform.

A similar situation occurs with parts that have high demand unpredictability. Traditionally, purchasing managers would respond to this situation by overstocking. Now, they can stop worrying about predicting the future, and can simply get parts when they need them, in smaller quantities. This prevents unnecessary logistics in the short-term, and waste in the long run.

Overcoming hurdles to unlock value of data

As mentioned in the Forum’s report, there are some challenges that often get in the way of analytics efforts. These are typically insufficient skills, data security risks, complex internal governance and processes, and roles and responsibilities that are not clearly defined. We would definitely agree that change management is a common challenge.

We’ve successfully solved this challenge in cooperation with our customers, based on a mapped out strategy and a crystal clear set of objectives and deliverables. What we’ve realized is that there are different phases in this journey to unlock the value of data. These phases are:

• Measuring current metrics in a better way, for more accuracy and insights.
• Measuring metrics that have traditionally been difficult to measure.
• Creating new metrics based on your use case.

In a number of cases, we’ve worked first on accuracy and data quality. With other clients, we managed to present insights that were hard to get on the table until now. The most advanced stage of capturing value from data is what we’ve developed and worked on with many of our customers: new metrics and actionable insights driving supply chain optimization.

We managed to build actionable insights combining data from traditional manufacturing and additive manufacturing, and enriched this with deep supply chain insights. This allows our customers to make decisions which contribute immediately to increased productivity, enhanced customer experience, and a positive impact on society and the environment.

Sharing data with partners is also crucial, as supply chains operate in networks that are increasingly more vast and interconnected. To facilitate safe data exchange, consider using a cloud-based platform that supports real-time data sharing across your ecosystem in a secure way. For example, companies that use our platform are able to connect to the world’s best manufacturing facilities, going from data to parts delivered in a secure and controlled workflow.

If you’re curious about how our end-to-end platform works, check out this page. For more insights on this topic, download the complete Forum report on Data Excellence.