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Digital Manufacturing and the Information Access Problem

Posted by Charlotte Foglia

digital manufacturing

Manufacturing is one of the most complex industries out there. Projects can involve hundreds of employees, and often take decades to complete. Most manufacturers view knowledge from within as their intellectual property, fueling innovation and separating themselves from the competition.

But for many, this knowledge exists in the form of siloed and unstructured data buried deep within a growing number of systems—systems that are changing as digital transformations progress. PLM systems are upgraded. ERP processes are evolving. But while new technologies are critical to staying ahead, they often make already complicated decision-making more difficult.

When it becomes harder to access information, decisions rely more on guesswork. In areas of the business that involve complex, high-risk decision making, guesswork can cost an organization not just time, but millions of dollars. Below are three such areas where solving information access issues can greatly improve digital manufacturing efficiency.

Research: understand requirements and plan accordingly

Manufacturing R&D involves projects of massive scope and complexity. Understanding product requirements, identifying past issues and successes, and gathering market and competitive insights is key. But this information often lives within millions of documents, adding layers of difficulty and risk not found in other industries.

For research engineers to find the information they need, they typically have to search across multiple internal and external systems. Each separate search adds time to the project. And despite their best efforts, the vast amount of disparate and disconnected data sources severely hinders their ability to uncover insights.

What is the cost of slow or incomplete access to the full volume of available information? Let’s use a pharmaceutical company researching a new drug as an example.

Pharmaceutical R&D scientists at one company had to go to more than a dozen different places for external scientific literature that they rely on for research discovery. This is on top of the millions of internal files in different systems and formats that they also had to search through for organizational learnings. At the same time, the adverse event reporting system was slow and cumbersome, taking several minutes per query.

The time to find insights or respond to auditor questions, when multiplied over several queries per several hundred R&D scientists and adverse events representatives, added millions of dollars in costs. All of these extra steps to find information drastically slowed the R&D team’s progress which:

  • Reduced productivity.
  • Hindered insights.
  • Created potential regulatory risk.

Fuller, faster access to internal and external clinical trial information was needed to help the scientific team bring new treatments to market faster and at less cost.

Smart design: avoid delays and minimize design mistakes

When it comes to the design phase of manufacturing projects, gathering learnings from other experts in the company and from past experiences is the key to better, faster innovation. To inform design and reduce mistakes, engineers need access to things like technical requirements, existing CAD drawings, and current processes. They also need to understand what’s been done in the past and why, and leverage the knowledge already gained to avoid recreating the wheel each time. The name of the game is to use the lessons learned to reduce mistakes.

It’s proven that as the design process progresses through the product lifecycle, changes become more expensive. According to one energy company, “no mistake costs less than $3M in delays and penalties.”

That same energy company also reported that each project creates some three million documents. When an engineer embarks on a new design project, searching through other project data for insights and guidance begins to feel like a Sisyphean effort.

Inadequate access to knowledge during the manufacturing design phase can result in:

  • Safety issues
  • Delays and penalties caused by mistakes
  • Regulatory risk
  • Potential impact on customer satisfaction
  • Loss of top talent
  • Loss of institutional knowledge as workers retire or leave
  • Inability to locate expertise that’s hidden in systems and documents

A fast and error-free design process relies on knowledge retention and quick access to expert information.

Maintenance efficiency: reduce down-time and increase customer satisfaction

Maintenance is one of the largest costs in manufacturing. Reducing down-time is key to smooth, efficient, and profitable manufacturing.

But what happens when field engineers can’t quickly access the information they need to fix or replace a part? Beyond the technical expertise needed, addressing issues quickly requires the ability to find other related logged issues, source replacement parts, and see what other interactions have occurred with a given client. This information is often a mix of structured and unstructured data, like parts databases and field notes, and requires multiple searches and manual effort to combine everything into one view.

But the job of a field engineer is to repair, not find information. The more difficult it is to find what they need, the less efficient they become.

For example, one manufacturer was incurring considerable extra costs due to a high product return rate. They discovered that field engineers searching for replacement parts couldn’t locate the right part and were continually ordering the wrong one. This often resulted in the wrong part being shipped and a high return rate.

An inability to find the right information in the maintenance phase can lead to:

  • Errors or potential safety issues and the risk (and associated costs) of recalls, bad press, and poor customer experiences.
  • Repair delays.
  • Redundant tasks and mistakes caused by an inability to leverage expertise or access up-to-date processes, related issues, and key learnings.

A way to quickly surface knowledge from all available sources can solve these issues to improve field engineer productivity, reduce maintenance time, and increase customer satisfaction.

Digital Manufacturing – Why intelligent search?

Manufacturers need to solve these information access issues so that workers can quickly find existing parts for new products, locate organizational experts to help with a project, surface learnings from past successes and failures, and address engineering changes more quickly.

With intelligent search, manufacturing workers gain unified access to projects, products, and parts across the design, supply chain, manufacturing, and service process. This unified view improves speed efficiency, and quality, reducing costs and overall risk. As complexity increases (more people, more parts, more systems), these problems increase exponentially, which means that the benefits also expand exponentially.