From insights to action in your manufacturing
These new challenges, and the ongoing pursuit of efficiency, are a reality many companies face today.
Digitalization is not a goal in itself, but it is a prerequisite for solving many of the new business challenges manufacturing companies are met with. In the pursuit of efficiency, the first priority is to become fully 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 ambitions
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 manufacturing units is a complex and costly area for most businesses. For this reason, connecting all elements of manufacturing from the beginning is rarely profitable – neither from a time perspective. Make sure you are set on a purpose and what specific business case you want to fulfil. Then, spend the necessary time to find out if it is a line in manufacturing, comparison of the same machine across lines, measurement between machines, or something else that can fulfil your goal. Furthermore, you need to create an efficient way to “onboard”/connect new sensors, PLCs, Excel sheets, databases, and more. This may also be relevant for creating the right data foundation.
Step 1 – Gathering data from the manufacturing line
The first step of the optimization loop is to collect data from the on-premises manufacturing line, meaning at the factory. Here we ensure:
- A consistent way/architecture to handle different data from different machines (most often from a wide set of PLCs)
- A consistent way to handle on-premises stakeholders as well as cloud stakeholders (e.g., mesh or event-based distribution of data or messages)
- An architecture and solution for infrastructure for testing, as well as configuration of manufacturing items at the manufacturing line
- Linking unit ID (serial number) on the manufacturing 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 manufacturing units/machines send messages back, which are received with large capacity disconnected from storage itself.
- Handling of “non-connected” scenarios where manufacturing/factory is not connected to the cloud
- Save/cache data on-premises so offline scenarios are supported, and manufacturing remains pristine even if a factory/manufacturing 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)
- Providing data in a usable format for stakeholders (statistics, aggregation reporting, deeper analysis, and modeling)
- Today, we typically see that, within our solutions, we are moving towards an ELT (as opposed to ETL) architecture, where raw data is out foundation and 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 digitalization 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 manufacturing, including:
- Analysis of “live data”, which, for instance, detects an increase in discarded items (which in turn could give rise to calls for technical assistance)
- PowerBI reports that act as a dashboard to visualize “live manufacturing pulse” linked to supply chain
- Loop manufacturing data/configurations back to machines or lines.
- Monitoring and follow-up on environmental goals such as carbon and water footprint 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/manufacturing work towards the same goal.
Maersk Container Industry (MCI) is a prime 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 workflows 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.