Why Digital Manufacturing Projects Fail

Why Digital Manufacturing Projects Fail

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What’s the difference between a successful digital manufacturing project and one that fails to meet its objectives?

Technical challenges tend to be the first thing that comes to mind, but in my experience as a industrial transformation leader, I’ve discovered that technology issues are usually not a factor driving success. I’ve learned that in most cases, projects that go wrong are often doomed to fail from the beginning.

The problem is that too many manufacturers start off on projects that aren’t well-suited to the levels of technical or organizational readiness of individual plants. The critical success factor is to choose the right projects for the right plants.

Why Digital Manufacturing Projects Fail

Misalign between plant/factory attributes and digital manufacturing project objectives lead to a number of challenges including:

Inaccessible or irrelevant data: We often find manufacturers select use cases that aren’t appropriate for the data capabilities of specific plants and/or lines. For example, we’ve seen companies looking to embark on projects aimed at identifying root causes for quality issues at a factory when their data quality is not suited for this. In a number of situations factories have data available only for real-time visibility into operational metrics. Data quality issues can be due to multiple reasons such as:

  • Critical machines may not include the needed sensors, or may be unconnected
  • Traceability of the product through the process might not be possible, even if the machines are capable of delivering data, a system of record may not be in place.

Data accessibility issues often make initial impact assessment from the initiative unrealistic.

Knowledge and operational gaps: There’s also a big difference between getting access to the data and generating insights from the data. Collecting all the available data in a data lake doesn’t by itself generate great insights. Plants need an onsite team that understands relationships between data and processes – how the data generated by machine sensors represents the manufacturing process. We find many manufacturers either don’t have the expertise or haven’t committed the right staff to the project.

Operationally, it’s critical that the local team is brought into the project and has the change management capability to impact the change outlined in the project objectives.

Inability to scale: The ambition of manufacturers is rarely limited to a one-off project at a particular machine or line. The highest value is achieved when digital manufacturing projects scale across multiple machines, lines, and facilities, making use of a broader set of data and applying the resulting insights wherever they are relevant. To accomplish this, a project must be designed to scale. It can’t rely too much on manual manipulation of data. The data models developed are far more valuable if they can be applied to multiple lines and factories. Too many projects are one-off, custom applications that fail to develop data models that broadly represent a manufacturer’s key processes.

At Sight Machine, we developed the Digital Readiness Index to help our customers pick projects that are likely to succeed—because if they succeed, we succeed. Our shared goal is for our customers to move to higher levels of readiness as quickly as possible, so they can take on projects that will successfully deliver strong returns.

Visit https://sightmachine.com/digital-readiness/ for a chart showing in-scope projects appropriate for each Digital Readiness Zone.

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Sudhir Arni

Sudhir Arni

Senior Vice President, Business Outcomes at Sight Machine In this role, Sudhir leads a team of transformation leaders responsible for on-boarding all new customers and customer success managers who ensure adoption of Sight Machine technology across the global enterprise customer base enabling business expansion from existing customers

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