Enterprise industrial IoT platform: 20-30% higher productivity with smart factory app development

Enterprise industrial IoT platform for manufacturing that increases production efficiency using sensor-collected data

Customer

Industry

Manufacturing

Region

Europe

Client since

2020

The customer is a large EU manufacturing enterprise, a representative of the heavy industry. The company produces machine tools for multiple manufacturers worldwide.

Detailed information about the client cannot be disclosed under the provisions of the NDA.

Challenge

Under the company’s long-term development plan, the client needed to create an IIot (Industrial Internet of Things) ecosystem from scratch. According to the plan, it would streamline production processes, inventory management, quality control, predictive maintenance, and data management. The ecosystem was expected to become an integral part of the production management system of one of the client’s machine factories.
The client commissioned Innowise to build the software part of the corporate IIoT ecosystem. Based on the full-process outsourcing model, our company has assembled a development team to create a complex smart factory application that would collect, process, store, and analyze data from sensors. The app was to employ predefined and machine learning algorithms to complete the full range of operations.

Solution

Innowise has developed and implemented a smart factory web application for a manufacturing company. The app is a part of the IIoT-based ecosystem aiming to facilitate working processes and improve the efficiency of performances on various indicators.

The smart factory web application consists of interconnected functional blocks to manage certain processes within the client’s largest machine factory. The solution has been developed to improve production efficiency, increase cost-effectiveness, optimize workflows, and streamline operations.

Production efficiency
Based on the sensor-collected data, the system calculates OEE (overall equipment effectiveness) for all production departments and machines. Users can view the key indicators that affect the level of OEE, identify possible problems, and perform certain operations to solve them. The app allows monitoring performance indicators for a certain period of time. Hence, operators can monitor key manufacturing metrics’ evolution.

Quality control

Within the IIoT-based platform, one can control the amount and quality of parts produced per shift or any other time period. Users can view detailed information on each manufactured piece: production line, number of rejected pieces, statistics, etc. Based on this data, system operators can identify production patterns, pinpoint the causes of increased scrap rates, and promptly take appropriate actions to increase the quality performance indicators.

Predictive maintenance

All pieces of machinery have some sensors installed. In real-time, sensors read and send the data on equipment temperature and vibration level. The smart factory app alerts operators if an abnormal increase in the temperature occurs. Thus, the IIoT-based solution helps avoid automatic shutdowns. Then, algorithms analyze the machinery’s maintenance history and offer to assign a non-scheduled maintenance check to prevent breakdowns.

Environmental control

Sensors detect various indicators that affect the comfort and safety of the factory’s employees. They are temperature, humidity, and noise level. Also, the smart factory app monitors the level of air pollution in industrial premises and the level of emissions. In case of exceeding the norms, the system notifies the operators and offers preset algorithms to overcome the situation.

Technologies & tools

Backend

C#, ASP. NET MVC, .NET Core 3, .Net 5, Web API

Frontend

JavaScript, JQuery, TypeScript

JUnit, Mockk, Roboletric, Espresso, XCTest, XCUITest, JMeter, ApacheBench, Gatling

Cloud services

Azure AD, custom UI for Azure Active Directory (AAD) admin center, Azure DevOps

Process

The client company opted for the turnkey development outsourcing model, which presumed that Innowise would execute all the stages of the software development process. They assigned a project manager and several technologists to monitor our team’s performance and support solution engineering. Our development team worked under the Scrum methodology (two-week sprints) and provided every day reports on the project’s progress. During the project, we worked closely with the client’s representatives and visited the plant as well as specialized conferences to fulfill the tasks as efficiently as possible.

Team

Full-stack developers

Project manager

QA engineers

Solution architect

UI/UX designer

DevOps engineer

Results

Innowise has delivered the smart factory web application, which maintains all the features of an IIoT production management system. It aims at increasing manufacturing productivity, improving quality control, and eliminating production asset loss. Our team has met all the deadlines and successfully launched the resulting IIoT solution. After the roll-out, our IT professionals continue to provide the solution maintenance and support services. The client has been satisfied with the results and the impact of the industrial IoT application on manufacturing performance with 20-30% higher productivity. That’s why they are planning to expand the smart factory app with new features and implement the enterprise industrial IoT platform in other facilities of the enterprise.

Project duration

2020 - Ongoing
The project is still ongoing, at this stage we support the app and implement new functionality

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