Beating the competition requires the ability to continuously evolve business processes to deal with disruption. Today, the manufacturing industry experiences a high level of disruption and competition across the globe continues to increase at an intense pace. First-world manufacturers must contend with the growing manufacturing capability of developing countries and the affordable labor they leverage. Conversely, manufacturers in the developing world must increase their capacity to deliver high-quality products to compete in first-world markets. Bypassing these constraints is where the automation of manufacturing workflows comes into the picture.
In theory, the integration of artificial intelligence in the manufacturing industry empowers shop floor assets to function independently of operators or the need for constant human intervention. In practice, automated workflow means material handling systems functioning independently, machines scheduling maintenance appointments, robots conducting performance testing, and increased safety on the shop floor.
The above subsystems employ AI as an automation tool through three elements:
- Analyzing data patterns
- Making decisions based on analyzed data
- Taking action using data-driven information
For example, an automated mobile robot (AMR) analyzes its surrounding, discovers obstacles, and makes the decision to avoid the obstacle using artificial intelligence. The circle of analyses, choosing a path and moving along that path continues until the AMR delivers its load. In this scenario, AI-powered robots can be applied for just-in-time deliveries and to reduce the safety incidents associated with traditional material handling equipment such as forklifts.
Gaining the benefits of AI-powered automation starts with implementation and the responsibility of its adoption falls on the shoulders of C-level executives. Here, a comprehensive checklist to assist managers with implementing an AI framework will be provided.
Identify the Need for Automation
Adopting AI to automate workflows should be tied to solving specific problems. Thus, the first step to developing an AI framework or considering automation technologies is deciding the operations or processes that can be eased and optimized using AI.
Asking the right questions can help with identifying shortcomings that AI-enabled solutions can solve. Examples of these questions include:
- Do we need to eliminate workflow redundancies?
- Do we need to improve resource allocation and material delivery timelines?
- Do we have to improve testing and inspection accuracy to deliver high-performing products?
- Will automating specific workflows speed up production timelines?
The answer to these questions can kick-start the creation of an AI integration blueprint. The chosen problem that requires an AI-powered solution can then be further analyzed to determine the exact technology partnership that is required. Once the area for application has been identified, ensuring everyone is on-board with its use is crucial to delivering the benefits associated with AI-enabled technologies.
Identifying Staffing Challenges
Introducing AI-powered subsystems within the manufacturing floor requires new capabilities to successfully operate them. Manufacturers interested in automating workflows through AI must take a strategic approach to determine current limitations in terms of staffing and a process to fill the gaps. For medium to large-scale manufacturing enterprises, developing the staff strength to handle automation involves developing a new tech department or bolstering the operational team.
Small-scale manufacturers without the financial capacity to develop new departments or hire the services of full-time technicians can take other routes. These routes include working with service providers that provide turnkey AI solutions that can easily be plugged into existing manufacturing systems or hiring part-time staff. Advancements in remote monitoring technology and the digital transformation of the shop floor mean monitoring AI installments is possible. The following questions can help with determining your staffing challenges for implementing AI-enabled systems:
- What is our technology-readiness level?
- Do we have the technical staff with the related experiences to handle the transition process?
- What expansion process will be the best fit for taking on new AI capabilities?
Choosing the Right Technology Partner
Automating workflows using AI solutions is similar to adding another asset to the shop floor or choosing to implement digital transformation tools to improve productivity. Once the choice of what process requires automation has been decided, the next step is ensuring the service or solutions provider you choose is right for you. The right partnership will ensure the deployment and continuous use of the AI-enabled solution is an easy process.
Selecting the right partner could also assist with the second part of this checklist – identifying staffing challenges. Some service providers have developed turnkey solutions that are plug and play while others provide detailed instructions and extensive support for more complex AI deployments. To choose the right technology partner, the following questions can help:
- Has your solution been deployed within similar facilities and to solve similar operational challenges like ours?
- What are the after-sales services like and how does it assist manufacturers with achieving set goals?
- Can you take us through a demonstration using our facilities or data to showcase the ROI we can expect?
Continuous Assessment Strategies
Measuring operational outcomes provide manufacturers with a means of determining the effectiveness of AI-deployed solutions. Also, continuous assessment helps with fine-tuning deployment strategies to get the best out of your investment. Achieving the aforementioned benefits start with determining the key performance indexes (KPIs) for measuring progress. These KPIs can be reduced safety incidents when automated material handling is adopted or increased planning accuracy when risk-based simulation modeling and scheduling is applied.
A continuous assessment strategy provides manufacturers with hard data concerning the next steps to take when managing AI workflows. The success of earlier projects then becomes the launch pad for expanding automated workflows or pivoting existing frameworks to solve new problems. The questions that can help with creating an assessment strategy include:
- What KPIs should we use as the criteria to determine success rates?
- Do the earlier goals for adopting AI still matter and how has the deployment assisted in achieving these goals?
- Is everyone still on-board with taking advantage of AI to improve operational outcomes? If no, then how do I continue to motivate operators?
The manufacturing industry has a lot to benefit from leveraging AI to improve production outcomes. These benefits include eliminating repetitive time-consuming tasks, improving shop floor operations, and leveraging shop floor data to make accurate decisions. You can kick-start your implementation process using this checklist to develop a customized strategy for your facilities.