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For products in the consumer, parts, food & beverage, and print & packaging markets, a significant portion of the manufacturing process still relies on manual tasks performed by human operators. Despite the buzz and excitement around robotics, AI, and Industry 4.0, humans are still an important part of manufacturing for assembly and inspection. This is particularly true for manufacturers specializing in short-run, custom, or specialty products where automating tasks can be complex and expensive. 

Here are 5 common areas in manufacturing generating errors


Making mistakes at initial set-up

If you don’t read the instructions first, this job is not for you.

Setting up a production or assembly line is a complex first step to manufacturing, and it comes with several detailed tasks. At this phase, paper checklists are often used by operators to track steps and requirements per product. The complexity of this setup requires human interaction, however, we also possess the very traits that make this phase vulnerable to errors.

Human nature tends to allow us to slide into ‘auto-pilot’ while performing familiar activities. For an operator who has gone through the same checklists multiple times, naturally they risk making mistakes when performing their tasks. This is a critical part of the product process, and a single error or seemingly small mistakes may cause substantial setbacks in a production facility. The error could go undetected until further down in production where a quality issue arises, or results in producing an entirely wrong product. Not only does this cost potentially thousands of dollars in line-down time and product waste, but resources are then spent retracing steps to identify the source of the error. In addition, for regulated industries, the defective product may be hazardous or pose a health risk to consumers.

To avoid costly errors, the practice of setting up carefully and correctly should be applied at any initial set-up point in production. This may mean extra attention is needed for measurement and test equipment that requires calibration and verification so operators can trust results. Similarly, if a piece of test equipment used to validate a part of the product is not properly set up it can generate incorrect results, and by extension add additional time in debugging and troubleshooting. With questionable set-up tools, the operator is unsure if the equipment is working properly, or if the product is incorrectly passing inspection when it should be failing.

Using a software-based solution to digitize this process is one step to help reduce these types of manual errors, providing operators with a guided approach to set-up.


Incorrect assembly

Chances are that in your lifetime you’ve assembled IKEA furniture. That five-page pamphlet you received for a nightstand just became a 5000-page document for complex assemblies if you are an operator in the automotive, aerospace, or defense industries.

Operators have a challenging job, and for new employees, training and learning the initial product assembly instructions requires attention to detail in addition to understanding what is considered a ‘good’ vs. ‘bad’ quality product.

Best practices for things like work instructions, assembly instructions and Standard Operating Procedures (SOPs) include making sure you keep steps and directions concise but specific. Annotated images or videos for each step can aid operators during assembly, especially when some parts require long-sequence assembly.

Assistive software solutions are available for operators to easily translate text instructions into interactive, augmented virtual instructions with sophisticated checkpoints using AI to help guide operators, avoiding costly mistakes.


Counting and measurement errors

Manually counting items can be tedious and time-consuming. Counting up to 1000 can be easy, but repeatedly tracking items and collecting the total count can take its toll on humans and create a window for error. For operators who inevitably face distractions and are required to manually count and verify, fatigue can set in.

Measuring objects can be as equaly time-consuming, especially when managing large batch production. For companies in print for pharmaceutical packaging, each leaflet included in the package must be measured to ensure it meets the correct specifications. Many companies still do this manually with a ruler.

Vision systems can help automate this process by implementing features to detect objects and edges so that the counting and measurement of different products can be done easily and without error.


Flawed labelling

Labelling errors are very common across industries and can be found at multiple points with the production process. This can include mislabeling directly on a product, its packaging, or its shipping materials.

The magnitude of this type of error becomes a much larger issue for regulated industries, like pharmaceutical, medical, and food & beverage, where an incorrect label can lead to a costly product recall.

Several manufacturers continue to rely on human operators to manually apply labels on products. Incorrect label alignment errors can be costly to correct, as often the error remains unnoticed until final batch inspection and shipment. If these products make it to store shelves, consumers associate the errors with overall poor product quality, dismissing the brand and damaging brand loyalty.

Manufacturers who implement robotics for labelling also experience label placement errors and have products that should have been could be classified as rejects. Vision inspection systems can be used to help detect errors in labelling through an automated verification or secondary verification process, with features to record and store images of every product before shipping.


Subjectivity in decision making

Sometimes, it’s hard to make objective criteria around what makes a quality product. As humans, we rely on each other for objective judgement. Individual operators must also be as objective as possible during visual quality inspection. However, when we become distracted and fatigued, visual inspection presents a large window for error.

To avoid errors and ensure top-quality products, creating a list of objective criteria with images or videos to show examples of correct assembly, label application, etc., provides operators with the tools for consistent judgment.

AI algorithms can continuously evaluate products using the same parameters to provide a more consistent and objective view on quality. When used as assistive technology, AI helps the operator by providing decision-making support.

As manufacturers continue to strive for end-to-end quality production with human visual inspection, adding AI decision-support and automating processes for operators can help ensure quality control and lower error rates in production and assembly.