AI FOR MANU­FACTURING - INTELLIGENT PROCESSES

Reliability and security throughout the entire lifecycle.

Embrace Artificial Intelligence.

Manufacturing industries working with large amounts of data have recognized the value of intelligent processes. By mining insights from this data – often in realtime – manufacturers can make better decisions without human intervention by using algorithms to build models that uncover connections.

Artificial Intelligence has become one of the most sought-after technologies for manufacturers. Unikie’s AI solutions provide an impulse for productivity improvement and help gain deeper insights for propelling the business to a higher level.

TOP-LEVEL AI EXPERTISE.

Our professional developers have an extensive experience in building end-to-end solutions for intelligent processes – from sensing and embedded software, all the way to cloud back-end and UI, including the connectivity and communication layer.

We offer top-level development and application specialists for the most challenging projects. Using our tech assets as accelerators, we deliver projects in an efficient way without compromising the quality.

REALTIME INTELLIGENCE.

We bring intelligence to your processes, production systems, devices, and machines by developing realtime robust and business critical solutions that run 24/7 even in harsh conditions.

Our AI based solutions cover forward-looking dashboards, maintenance schedules, alerts and recommendations, and realtime actions and control. We aim to increase manufacturing efficiency, safety, cost savings and accuracy as well as decrease downtime. We know how to develop and maintain solutions that are reliable and secure throughout their entire lifecycle.

We cover the whole lifecycle of intelligent processes.

1. Design and
develop

Due to our long experience on industrial R&D, we can develop production level software solutions from scratch or augment existing ones to be more intelligent.

Our approach is holistic, including specifying the requirements, selecting hardware and sensors, building data pipelines, developing algorithms and methods, and integrating everything into an functioning application.

Our way of working is agile, starting from an impactful but feasible MVP that is improved and extended in increments.

2. Deploy and
operate

We deploy the solutions to production in a highly robust manner. We utilize a CI/CD process suitable for industrial software, including testing, installation, calibration, and integration.

We can operate the solutions in production 24/7 with continuous performance monitoring, alerts and dashboards. We provide also Data and ML Ops to automate the operation of the complex data processing pipelines.

Thus we can minimize the need for human intervention and work for solution management and maintenance.

3. Rollout

We understand that rolling out solutions to an abundance of locations and facilities is not a trivial task. Every machine, production line, and environment is basically unique, and data therein vary and drift differently.

We can tackle the challenge by building the solutions so that they are by design easy to roll out to various setups and environments.

Furthermore, we will set up the required practices and processes to continuously monitor and automatically adapt solutions to changes in the production processes and environments.

Path to AI Driven Processes.

Resilient way of acquiring, transferring, synchronizing and preprocessing data is a mandatory prerequisite for all real-world intelligent solutions. In industrial setting they come in various shapes and forms. Data can be generated by both humans and machines and can vary from distributed, low level sensor data all the way to the records from centralized company level information systems. It is often a non-trivial and tedious task and hence a typical reason for failing in the solution deployment and operation.

Example
We provided a forestry company with an automatic raw material processing QA system that monitors the material quality in production visually, using AI based and classical machine vision algorithms. The solution can identify raw material features to qualify for production process, identify quality problems on different production phases, and perform quality check on the final product. We analyzed the production process and proposed the overall solution, starting from sensor and hardware selection and placement for optimal operation, all the way to the machine vision software implementation, testing, integration, and deployment.

Data as such are just a raw material with little value. Just like any raw material, they need to be refined and distributed to result in something useful. This can be achieved by building efficient data pipelines that carry out various data processing steps, often in a distributed manner in realtime. A typical data pipeline includes ingesting and merging data from different sources, cleaning and transforming them, applying algorithms and visualization methods on datasets, and finally transferring the results in timely and in right format to the one’s in need of them.

Example
We built an international manufacturing company an AI-based solution that provides information on the end product quality to the machine operators through a realtime dashboard. In addition to overall view of diverse quality measures, the solution shows trends, forecast of quality, as well as displays the key product parameters affecting quality. The solution was based on combining machine vision sensor data with production data, and analyzing them using diverse visualization approaches, as well as applying machine learning methods for anomaly detection, prediction and forecasting.

To make the gained knowledge actionable, inferences need to be made based on them to draw concrete conclusions. This is often a demanding task, even for a seasoned domain area expert, but still in many cases inferences can be made by advanced AI algorithms with little or no human interaction. Although fully automatic decision processes are something to desire for and increasing in number, usually at least the final conclusions are drawn by a human. In some cases that is even mandatory for regulative and/or security reasons.

Example
We helped an international mining company to build their predictive maintenance capabilities and products. The focus was in optimizing the methodologies and improving forecasting skills. Our experts have both ensured the data quality for its particular use, but also built predictive machine learning models and needed KPIs. This has been achieved by robust understanding about the needs and business. In practice the activities included building ML models for individual failure mechanisms, identifying data flaws that compromise the integrity of the results, and optimizing the overall data-to-value processes.

The last mile of the AI driven processes is taking the action. The final decisions are made based on the conclusions, which lead to either manual or automatic action. Like drawing conclusions, aim in the long run is to automate action-taking as much as possible. However, in industrial setting there usually is a human in the loop, to either actively carry out the procedure recommended by the AI, or at least to approve its execution. Closing the loop in a proper manner enables also self-learning systems that stabilize and improve over time.

Example
We developed an AI-based solution for an international manufacturing company for optimized quality control. A realtime dashboard provides the machine operators suggestions of possible process parameters, as well as other relevant information, before a manufacturing of a new batch is started. Operators can then adjust the process parameters accordingly to produce optimal quality regardless of their experience level. The solution is based on a data and complex ML-based digital twin of the production line that is used for predicting quality and generating recommendations on optimal machine setting.

INTELLIGENT PROCESSES FOR VARIOUS USE CASES.

Our services and solutions for intelligent processes can be utilized in different areas from sales and operations planning to production automation and optimization, as well as quality control and maintenance. Intelligent processes can cover an abundance of use cases such as:

  • Demand forecasting
  • Logistic planning and optimization
  • Digital twins
  • Raw material monitoring
  • Realtime manufacturing dashboards
  • Intelligent process control
  • Predictive maintenance and optimization
  • Quality monitoring and forecasting

OUR ASSETS ON INTELLIGENT PROCESSES.

CONTACT US.

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