• AI
  • Machine learning

Bottleneck Analysis in Manufacturing: Tech Solution for Increasing Enterprise Productivity

Vahan Zakaryan
17 Jun 2022
8 mins
Bottleneck Analysis in Manufacturing: Tech Solution for Increasing Enterprise Productivity

Manufacturing production is often prone to disturbances and disruptions. With a system involving so many complex machines and dynamics between them, bottlenecks are inevitable. 

Throughput bottlenecks are most common: it’s a term used to describe a clog or challenge in the production process that leads to inefficiency in production. In other words, when a factory produces fewer products than it planned to during a certain period, it deals with throughput bottlenecks. 

These bottlenecks appear when there is a breakdown in the production machines or because of human errors or behaviors. Now, it’s not a new issue. Both factory workers and manufacturing management always tried to come up with things to do to remove them quickly or prevent them from occurring. 

In this article, we’ll talk about new solutions to remove bottlenecks from factories — in particular, about AI-driven bottleneck analysis in manufacturing and how it helps to detect them early, plan for them, and eliminate them. 

Dealing with Bottlenecks In the Output Systems

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Bottlenecks in the output system can result in unscheduled stops, unplanned breaks, or halt the manufacture entirely. 

There are short- and long-term bottlenecks. Short-term bottlenecks occur within a production cycle or shift; they hurt the output within said cycle or shift, once. Long-term bottlenecks are recurring, so they impact entire production. If there has been a shutdown and the fabric was left without electricity for half an hour, it’s a short-term bottleneck. If old factory equipment continuously underdelivers — there are blockages, long stop times, etc. — that’s a long-term bottleneck. 

Unscheduled stops hurt the profit margin of companies. Downtime costs manufacturers about $50 billion yearly, according to Delloite. This research says that a minute of downtime costs $22k for average manufacturing within the automotive industry. As you imagine, that hurts the bottom line terribly, which is why businesses aim to find out the cause of bottlenecks appearing and remove them. 

Bottleneck analysis in lean manufacturing suggests that the most common reasons for them are 

  • lost production capacity (re: broken, old, slow machinery), 
  • inventory issues that stem from inefficient inventory management, delays from deliveries across the supply chain, etc.), and 
  • lack of innovations (a bottleneck like a thorn in the side of businesses: when to innovate when something always breaks?) 

Plus, of course, you’re continuing to pay people when machines have stopped — that’s wasted labor. Diagnosing and identifying bottlenecks that bug your factories is the first step to removing them. Now, how can AI help with that? 

AI Technologies – a Modern Way to Diagnose and Solve the Problem of Bottlenecks

The deployment of AI in the manufacturing process and supply chain leads to a leaner and smarter manufacturing system. Internet of Things (IoT) system — sensors spread across the plant and tracking everything relevant that’s going on with your machine (relevant in a sense of “may affect the production”) and systems that collect this data — sends data to AI. Let’s talk about how AI is used in manufacturing industry. 

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Bottlenecks AI Identification

Bottleneck identification involves locating the point of constraint in the production. AI can identify recent bottlenecks in the process by looking for patterns in the historical data. 

After collecting real-world data, AI experts with domain knowledge in manufacturing choose a bottleneck detection method, pre-process, clean the data, and use a rule-based classification or extraction method to detect anomalies. 

The input data might contain durations of blockage and starvations (to calculate the average), active durations (to calculate the average and figure out confidence intervals between them), inter-departure durations, the time it takes for a machine to produce one unit or a series of units, takt time, etc. By analyzing this data, each machine in the factory is classified either as a long-term bottleneck or as a non-bottleneck.

Another example: this case study uses the time series generation method, and then hierarchical clustering to group machines according to their behaviors, e.g. in active durations. Visualization helps experts find machines that act uniquely — in other words, bottlenecks. 

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    Short-term bottlenecks can also be discovered via rule-based classification, but the system of detection, needed, might be more complex. The ML classification for the job would need different input: not event data like active duration, but, for instance, binary states of the machines during these events. Consequently, while engineers can build long-term bottlenecks identification models with data extracted from the machines, models to detect short-term ones would require additional sensor data. 

    Diagnosis of Bottlenecks and Their Further Prediction

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    The ability of ML to predict likely bottlenecks makes it indispensable to manufacturers. Now, to find the root cause of the bottlenecks with AI and build appropriate plans to remove them, AI specialists — with assistance from factory employees — build models to classify events in which bottlenecks are detected. This work, for example, uses K-means clustering (an unsupervised ML technique) to visualize clusters of unplanned stops. Then, maintenance practitioners use the insights from this diagnosis to prioritize the most pressing and disruptive bottlenecks. 

    Apart from making a factory more data-driven and helping employees make sense of the data and utilize it to increase productivity in the short term, AI models also predict bottlenecks in the long term. 

    AI engineers use various forecasting methods on classified input data from the manufacturer to build predictive models. One group of forecasting methods uses only data extracted from machines and another adds contextual information to the pool. The second type models are more accurate but more time-consuming to build. 

    The predictive process goes like this. 

    First, AI detects patterns in historical data, processes these data, and forecasts the predictions using processing results. Statistical tools help capture linear dependencies (when X increases, pressure on the Y increases), while ML tools define non-linear ones. 

    Then, rule-based classification helps divide machines into bottlenecks and non-bottlenecks (recognizes dynamics in the increase of unplanned stops, for example), and builds forecasts for each machine. An example: this work uses a neural network that processes historical floor data and production plans to detect bottlenecks in real-time and project the captured data in the future. 

    Finding Solutions

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    First and foremost, every complex technology described in the paragraph above aims to solve the downtime issue that cost manufacturers a lot. Insights from AI help to formulate a proactive maintenance schedule and predict the breakdowns, which makes budgeting repairs, logistics activities, etc. easier. 

    Scheduling and resolving issues might be done manually — by managers and maintenance experts in the factory, — or automatically, again, with the help of prescriptive AI. So, apart from predictive maintenance, AI in manufacturing can help with: 

    1. Quality Assessment. AI-based systems can detect disruptions and defects invisible to factory employees. The use of high-resolution cameras reveals the tiniest error in the quality of a product. For example, BMW uses iQ Press, an AI solution to monitor quality-related parameters in its manufacturing plant.
    1. Warehousing and Logistics. By collecting relevant data on manufacturing and demand patterns, machine learning helps companies know when to stock up on a product. Companies also can deploy robots to lift and sort products in warehouses. Through AI, the progress of delivery can be tracked across the supply chains. (Apart from supply and demand, AI can also be trained to notice scarcity — like semiconductor shortages we’re facing right now.) 
    1. Automation. AIl of the above makes up for people’s fatigue, stress, and lack of focus. AI on manufacture isn’t a replacement for employees — with automated detection of potential disruptions, maintenance schedules, and so on, AI is an assistant and helper. 
    1. Disruption management on supply chains. Through predicting the best delivery routes and factoring in weather, traffic, etc. AI saves costs across the supply chains. A McKinsey report predicts that AI-powered supply chain management will cut down forecasting errors up to 50%, reduce loss of sales by almost 65%, and cut overstocking by 50%.
    1. Safety. Working with IoT-enabled sensors and cameras, AI quickly detects leaks, smoke, or eruption on the manufacturing floor and helps enact protocols to contain them. 

    Beyond the above, AI has lots of applications in the production process and can be used to design new products to meet demand.

    AI Smart Tools Value

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    Sensors are invaluable in AI systems. They monitor pressure, vibration, temperature, sounds, and weight on the floor. Changes in preset conditioning indicate a problem, an anomaly, that’s instantly captured by the algorithms. 

    Apart from that, sensors can monitor employees’ health and other safety measures like smoke, fire, or a gas leak to ensure a safe working environment.

    Sensors are also deployed to monitor the movement of products in the supply chain. They gather data that is useful for future operations and meeting regulatory requirements (e.g. expiration dates, air humidity, etc.). 

    The Implementation of AI for Manufacturing Companies is a Step Towards Productivity

    Let’s summarize how the introduction of AI for manufacturing companies increases efficiency and productivity:

    1. Safety. Injuries in manufacturing plants feature in the top 10 workplace injuries per year. Embedding AI in the manufacturing process reduces this risk and provides a safer workplace, which reduces stress and improves employees’ job satisfaction. 
    2. Lower costs. ML ensures that downtime is reduced to the bare minimum through predictive analysis which saves costs and positively impacts the bottom line.  
    3. Quick decision-making. AI analyzes a large volume of data and suggests rapid solutions to problems thereby shortening the time required to address the issues on the floor. 
    4. Recordkeeping. AI helps in storing useful information and to archive data for future solutions.

    Infusing AI solutions to analyze throughput bottlenecks in manufacturing enables a leaner manufacturing model that is more efficient, cost-effective, and safe. Apart from that, the time employees would otherwise spend on resolving challenges with machines can be used to come up with new approaches to production or new products. 

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