Transforming Tradition: Addressing AI Implementation Struggles in Manufacturing Industries

Transforming Tradition: Addressing AI Implementation Struggles in Manufacturing Industries
Transforming Tradition: Addressing AI Implementation Struggles in Manufacturing Industries

The COVID-19 pandemic has highlighted the vulnerability of traditional manufacturers to supply chain disruptions.

Many leaders in traditional industries now recognize the urgent need to embrace digital transformation and implement AI. They understand that these technologies can help them better manage their supply chains, mitigate disruptions, and predict demand more accurately. However, the challenge is that these companies are typically labor-intensive operations without in-house data teams, and they also face internal resistance to change.

This article examines the typical challenges faced by traditional manufacturers and provides solutions for overcoming them.

Why do traditional manufacturers struggle with implementing AI?

The lack of endorsement from high-level executives and managers in traditional manufacturing companies is often the primary impediment to their digital transformation endeavors. When digital transformation initiatives are isolated within technology teams, there is often a barrier to gaining acceptance among factory staff. This is partly because technology teams do not fully understand the challenges faced in the factory and select solutions that are not suitable for driving real change.

Another common issue is that these initiatives are not fully championed by managers and department supervisors, giving the impression that they are not important and can eventually be ignored with a bit of finesse.

A further issue is the lack of realistic and meaningful key performance indicators (KPIs) set for the implementation of these initiatives. Manufacturing is driven by KPIs, and without clear and meaningful goals, staff will view them as abstract objectives that will not be evaluated.

Traditional companies may also lack the necessary sensitivity to technology to make the digital transformation a success. It can be challenging for them to understand the potential of AI and new technologies and how their adoption can positively change day-to-day operations.

To foster meaningful change, two critical aspects must be prioritized. Firstly, organizations must cultivate a cultural shift and secure broad support for implementing AI. Secondly, selecting the right AI vendor is imperative, as an ill-fitting solution can undermine long-term success.

1. Fostering employee buy-in for AI and digital transformation endeavors
Instigating AI implementation requires top-level commitment, and organizational leaders need a well-defined roadmap for driving change.

Top management needs to ensure clear communication of the benefits of AI adoption to effectively engage other leaders and employees. It is crucial to demonstrate that AI is intended to enhance their work rather than be perceived as a threat or a means of replacing them.

Conducting hands-on demonstrations with teams is crucial, as the benefits of AI often appear abstract to non-technical staff members. Witnessing AI in action allows them to gain a clear understanding of its implementation and its potential to improve processes and make their lives easier.

Equally important is providing leaders and staff with adequate training to effectively navigate AI implementation. Learning from previous middling attempts, our customer, BenQ Materials, took a proactive approach by conducting intensive training courses for supervisors and organizing AI classes within individual departments, which led to a successful implementation process.

2. Selecting the ideal AI vendor for traditional manufacturing
Outside of culture changes and getting company-wide buy-in, the other big challenge for traditional manufacturers is choosing and implementing the right AI vendor. Many vendors are not geared for the specific challenges that manufacturers face.

When considering AI vendors, manufacturers need to consider the following:

  • Domain Knowledge: It is important to choose an AI vendor who has domain knowledge of the manufacturing industry because they will have a better understanding of the real-world environment in which the AI/ML algorithms operate.
  • Industry Reputation: Look for a solution that has a good reputation in your industry. Try to find a company that has a client list that includes similar-sized manufacturers as yours.
  • ROI: Before seeking out an AI solution, businesses must identify the specific problem they need to solve and insist on a demonstration of the quantified value and ROI that the potential AI partner can deliver.
  • Team Background: Many newer startups may be technically impressive but don’t have experience implementing enterprise solutions in traditional companies, which is the most difficult part. Look for a vendor with a mature and experienced team that will be able to demonstrate and get buy-in from your staff members.
  • Flexibility: Consider how attentive the vendor will be to your specific needs and how willing they will be to make customizations.

By focusing on culture changes as well as employee buy-in and selecting the right AI vendor, traditional manufacturers can successfully implement AI and stay competitive in the market.

About The Author

Jerry Huang is the co-founder and CEO of Profet AI. Previously, he worked in global software companies such as IBM, SAP, and PTC and other companies in the manufacturing industry for 20 years before starting his own company, Profet AI, an AutoML enterprise solution for the manufacturing sector.

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