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1Manufacturing

With its ability to automate, digitize, and optimize, AI is the perfect fit for manufacturing operations, from product development to quality control. The value generation potential by AI within manufacturing is estimated to be USD 870 billion globally per annum. Key use cases lie within predictive maintenance, forecasting, R&D, as well as throughput and logistics optimization. Unit8 keeps manufacturers one step ahead for example by implementing real time monitoring systems for production issues.

2.1

Industrial IoT (Internet of Things)

  • Predictive maintenance (e.g. prevention of machine failures or unplanned downtimes)
    Advanced machine learning models can recommend optimal times to conduct maintenance by analyzing root causes and identifying drivers of machine downtime. Implementing these recommendations will prevent future asset failures and unplanned downtimes, hence minimizing production losses while maximizing OEE (overall equipment effectiveness).
  • Automated quality assurance (e.g. continuous process monitoring, visual inspections and anomaly detection)
    Analyzing process parameters using Machine Learning techniques can help you predict and prevent quality issues by capturing patterns that could not be detected otherwise. The widespread availability of high-resolution cameras and cost-effective IoT devices, coupled with powerful image recognition technology, can significantly cut the cost of real time in-line inspection. Similarly, the reduced manual intervention and errors in quality checks can eventually increase the scale and scope of quality inspection.
  • Edge analytics/ smart factories
    Building a smart factory is all about increasing its autonomy in critical scenarios, for instance when you need real-time insights or when cloud connectivity is not optimal in an isolated location. Edge Computing can enable this, making machines extract insights and formulate actions in near real-time based on what is going on on the factory floor, without the need for human intervention.
2.2

Robotic applications

  • Warehouse automation (e.g. pick & place robots)
    Advantages of warehouse automation are manifold. By streamlining pick-and-place processes on a 24/7 schedule, AI-driven robots can help reduce the time and distance to move objects within the warehouse inventory. Also, robots can accurately learn from their surrounding environment thanks to IoT sensors coupled with computer vision, avoiding errors in warehouse operations.
  • Collaborative robots
    Collaborative robots are equipped with machine learning capabilities and have built-in safety mechanisms that reduce the need for external safety measures, such as fencing. They directly interact with human workers by offering them physical support – for example, by giving older workers or workers with restricted physical ability the assistance they need to be successful in manufacturing.
  • Flexible production (e.g. flexible grasping)
    Modern AI-trained robotic arms are able to sort through a messy arrangement of spare parts and tools inside a bin and select only the objects needed. This ability, called bin-picking, allows manufacturers to have robots bin pick parts and place them in a pre-assembly kit for use further down the assembly line.
  • Dark factories
    Dark Factories are a manufacturing concept entirely reliant on robots. The pursued outcome is the development of an end-to-end production process that would be entirely automatic and relying solely on robots and AI so that manufacturing takes place without human involvement on the shopfloor (hence the factory lights can be turned off).
  • Workers safety
    Your warehouse operations can be monitored on a near-real-time basis with AI-driven solutions, allowing you to assess accordingly the risk associated to each activity. Workers can then focus on the safest tasks, while high-risk activities can be delegated to robots, improving the overall safety of employees. As a complement, AR and VR devices can also be used to train employees in a risk-free environment, minimizing the risks of injuries.
2.3

Logistics & throughput

  • Supply chain (e.g. optimize routes and order quantities, anticipate delays)
    A challenging supply chain optimization problem is to accurately assess the right amount of orders delivered per vehicle, as well as the most efficient order for stops while minimizing idle time. As AI is continually retrieving data and learning from it, it can anticipate potential delays, reveal new routes and ensure faster delivery times, therefore reducing cost while increasing customer satisfaction.
  • Forecasting (e.g. sales, returns, inventory levels, raw material prices)
    Unsupervised learning and advanced time-series analytics are a must to predict effectively changes in sales, or variations in product demand or raw material prices. Besides, by getting a precise understanding these changes in sales patterns, you may eventually optimize product availability by decreasing out of stocks and spoilage.
  • Process & yield optimization (e.g. digital twins, process mining, energy & waste reduction)
    With the advancements of machine learning and optimisation techniques, a digital twin can be used to simulate the supply chain’s performance. By going deep into existing processess, it will then focus on detecting areas where volatility and uncertainty exist, as well as where optimisation is possible. The created end-to-end overview of your complex supply chain networks can then be leveraged to make data-driven decisions based on business needs and to respond better to external market pressures.
2.4

Product development

  • Generative design/ virtual prototyping
    Generative design leverages AI and machine learning to provide thousands of design solutions to a tedious engineering design problem. These next-generation algorithms can be trained to not only optimize a design based on usual engineering parameters, such as weight or durability, but also for commercial parameters, like production costs or aesthetic requirements. As a result, the engineer has more room to tackle challenges that require “common sense” or cannot be solved by computers.
  • Mass-customization (e.g. optimize additive manufacturing such as 3D printing)
    In combination with generative design, 3D printing makes it possible to quickly print products tailored to the specific needs of the client. Additionally, the combination of these two technologies allows for the reproduction of complex structures that traditional methods are unable to manufacture.
  • Propensity models (e.g. define product-line extensions/ add-ons)
    AI-driven analyses can predict how likely a person is to make a purchase based on their profile and under which circumstances, but also estimate what you expect the value of that customer to be. Including these findings in your design processes allows you to take into account a customer’s potential level of engagement when defining product-line extensions.
Fabian Schramm Client Engagement Manager
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