Industrial IoT
From insights to action in your production
These new challenges and the ongoing pursuit of efficiency are a reality many companies face today.
Digitization is not a goal in itself, but it is a prerequisite for solving many of the new business challenges that production companies are met with. In the pursuit of efficiency, first priority is becoming 100 percent aware of the business challenges your company experiences today. Without this, projects are prone to becoming overly technology-driven, too big, and risk losing direction.
Smart factory: An optimization loop uncovers considerations, challenges, and ambition
As part of the technical conduct of our customers’ challenges, we work with an “optimization loop.” The goal is to discuss and uncover concerns, challenges, and ambitions for the project—from data collection to how data is to be utilized by the business, for example in reporting and comparison or push-back of configurations for machines.
Our experience tells us that creating a connection to the production units is a complex and costly area for most businesses. Connecting all elements of production from the start is therefore rarely profitable—also from a time perspective. Make sure you are set on a purpose and what specific business case you want to fulfill. Then use the time to find out if it is a line in production, comparison of the same machine across lines, measurement between machines, or something else that can fulfill your goal. Furthermore, you need to create an efficient way to “on-board”/connect new sensors, PLCs, Excel sheets, databases, and more. This may also be relevant for creating the right data base.
The first step of the optimization loop is to collect data from the production line on premise—meaning at the factory. Here we ensure:
- A uniform way/architecture to handle different data from different machines (most often from a wide set of PLCs)
- A uniform way to handle on-premise stakeholders as well as cloud stakeholders (e.g. mesh or event-based distribution of data or messages)
- An architecture and solution for test-infrastructure for testing, as well as configuration of production items at the production line
- Linking unit ID (serial number) on the production item with item configuration.
In connection with the second step of the optimization loop, we focus on how to make the solution transmit data to the Cloud, including:
- IoT scenarios where production units/machines send messages back, which are received with large capacity disconnected from storage itself.
- Handling of “non-connected” scenarios where production/factory is not connected to the Cloud
- Save/Cache data on premise so that offline scenarios are supported and production remains pristine even if a factory/production line is not connected to the Cloud
- Integration with diverse data sources as well as consistent onboarding for better control and quality
Step 3 – Preparation of the data platform
When we reach the third step in the optimization loop, we prepare the data platform, which includes:
- Saving raw data—as a source for creating use-specific datasets (or recreating the same in a recovery scenario)
- Provide data in a usable format for stakeholders (statistics, aggregation reporting, deeper analysis, and modeling)
- Today we typically see that we, within our solutions are moving towards an ELT (as opposed to ETL) architecture, where on the basis of raw data we only make a transformation when a stakeholder needs data for a specific purpose, such as reporting in PowerBI or for deeper analysis (statistical or ML etc).
- Being data driven often requires a “foundation” with a Data Platform that supports the digitization of the various business areas and processes. Data sources often lie in silos in a form that is inaccessible and not usable across the organization. Therefore, the data platform typically needs to be developed in parallel with a visible value creation and commissioning of data in the business.
The fourth step of the optimization loop is to focus on making data available and usable in production, including:
- Analysis of “live data”, which for instance detects an increase of discarded items (which in turn could give rise to technician calls)
- PowerBI reports that act as a dashboard to visualize “live production pulse” linked to supply chain
- Loop production data/configurations back to machines or line.
- Monitoring and follow-up on environmental goals such as CO2 and water consumption as well as recycling of waste.
Smart product: Create a digital world around your product
To extract value out of technology, you need to know what you want to use IoT for in relation to your customers, your organization, and the strategic direction of the company. There is basically an untapped potential in relation to new earnings opportunities and completely new customer segments. But this is only realized if sales, IT, and R&D/production work towards the same goal.
Maersk Container Industry (MCI) is a really good example of how IoT opens up new opportunities. But if the goal is to utilize IoT to create new business, it’s important to not only think about optimizing and automating existing processes but also about offering a service to customers that creates value for them.
In the long run, the solutions also create a completely new knowledge base, which gives our customers real insight into product use, product and component performance, durability and much more. Another business opportunity, which the insight gives you, is a more service-oriented subscription model as well as great opportunities to optimize and improve your company’s service business.
