Uncertainty in the market forces manufacturing companies to increase the flexibility of their production. Industrial leaders have already implemented automated processes and connected high tech equipment to perform live data analytics. But the transactional data generated during production are often only used for short-term analysis to detect issues and are then deleted from enterprise systems due to high storage costs. This report explains our best practice how to optimize the digitalization of industrial production lines with Big Data technologies.
Analysing data directly in the database of a running production line might cause production downtime and outages. Apart from this, many companies are assembling more sensors to gain a comprehensive understanding of each production line. For example, sensors to detect temperature, air humidity and vibration strength, are applied to measure how environment factors affect yield and product quality. Collecting more and more data with current enterprise systems not only leads to higher storage costs but also proves to be a huge burden on the computation power of an enterprise system. Accordingly, there is no capacity for advanced analysis left. Although manufacturing data can potentially generate high value information, it has to be abandoned. To solve these problems, industrial companies need to adopt analysis and processing tools for the complete digitalization of industrial production lines.
A digital production line integrates an organization’s business, design and operations to improve the production process.
On the one hand it accelerates the production process by leveraging robots and detecting failures earlier, and on the other hand, it makes the production design more flexible and thus generates capabilities for new product designs and geometries. To digitalize an existing automated production process, industrial companies need to go through the following steps:
According to our on-site experiences with clients, step 3 to step 4 is usually ignored despite the existence of automated and connected production lines in factories. Companies are stuck in the previous steps because they lack the necessary know-how of building a Data Lake. From a technical perspective, deploying a Big Data cluster requires skills in programming, managing data structures, machine learning, analytics and cloud computing.
Finding a team that is competent in all these aspects is a challenging task. In addition, to truly exert the value of a Data Lake, indices for production efficiency need to be implemented, for example OEE (Overall Equipment Effectiveness), Production Performance and Quality indices. Most corporations have not applied these indicators on production lines. Therefore, experts with insights on both supply chains and business management are required to design intuitive and role-based KPI dashboards.
FEATURES OF DIGITAL PRODUCTION LINES
A good example is the transition from Preventive Maintenance to Predictive Maintenance, enabled by a digital production line. Both approaches aim to prevent unplanned downtime and expensive cost from equipment failure. Preventive Maintenance corresponds with the average or expected downtime for the equipment lifecycle. Meanwhile, Predictive Maintenance is based on the actual equipment condition and information from the whole production process. This approach coordinates information from both the up- and the downstream of a supply chain and offers comprehensive insights for the usage of production lines. As a result, Predictive Maintenance promises cost saving for just-in-time maintenance actions and minimizes disruptions of system operations.
A Data Lake is a repository that stores all structured and unstructured data at a large scale. It provides a platform to analyze and visualize data with different methods, such as machine learning tools, real-time analytics and reporting dashboards.
Although many enterprise system vendors now offer cloud storage and data analysis solutions, the high storage cost and vendor lock-effects are - for many of our customers – still main drivers to choose an open-source Data Lake.
Compared to enterprise systems a Data Lake has the following advantages:
To sum up: Sensors and robots construct the physical part of a digital production line, a Data Lake further empowers the production line to implement advanced analysis. The physical equipment and information form a joint ecosystem. This ecosystem allows the factory to achieve higher efficiency without affecting the normal production line and also frees up the companies from vendor effects with added benefit of low storage cost.
The first step is to reinforce the physical deployment with sensors, RFID-Tags and actuators, such as robots. Different types of sensors help to collect data regarding transaction time, temperature, air humidity etc. and to save the data in enterprise systems.
The next step is to set up a cluster in the cloud and to analyze the production data.
For example in the Deloitte Digital Factory we deployed a Cloudera cluster based on Amazon Web Services (AWS). As a cloud service provider AWS offers a pay-per-use model that encourages companies to produce more demo and testing projects with minimal budget. Cloudera significantly reduces the time for deploying a cluster of servers and provides a platform for various analysis tools. After deploying a cluster, companies are able to move the historical and current production data from expensive enterpris systems into the cluster in the cloud. One way to do this is to use an ETL (Extract, Transform, Load) software. In the Digital Factory we chose Talend as our ETL tool. Talend has more than 900 connectors, it allows users to extract data from databases and enterprise systems and to send the data into a Data Lake. Moreover it provides users with a graphic interface to intuitively design the data pipeline. The ETL can be optimized by applying an appropriate CDC (Change Data Capture). Change Data Capture is a software set that accesses the database logs of insertion, update and deletion commands.
By applying CDC records in the Data Lake, companies can keep the Data Lake up-to-date with minimal impact on the original systems. At this point the data are fully integrated in the cluster but their value is not yet retrieved. In order to do this, data engineers and data scientists are working together to clean, analyze and transform the unstructured sensor data into decision support information.
The last step is to provide an intuitive insight derived from the data while the visual analytic group designs automated reports and KPI dashboards. The visualized information and insights are then sent back to the factory, so that managers and workers can detect flaws of the product and subsequently eliminate the inefficiencies.
Digital production lines are the base of a smart factory. In our vision, a factory can only be smart if it and its components are:
Digital production lines allow this vision to become real: Visualization tools present operational statuses in dashboards and allow workers to quickly detect anomalies and track those issues precisely. KPI reports provide managers with a timely overview of the factories and thus accelerate decision-making.
Now companies can not only combine the historical and the current production data but apply Predictive Maintenance that significantly enhances the automation level in factories. Routine operations such as restocking and replenishment are done automatically with minimal human interaction. Predictive maintenance also helps to identify supplier and quality issues in advance, so that factories can react faster, and therefore improve their production efficiency.
Information reduces the risk of re-planning production lines. Factory supervisors are now able to adapt the factory layout and equipment more flexibly to various product types and are able to determine the most efficient design with the help of machine learning models. As a result, adaptable scheduling and changeovers mitigate the impairment from changing market trends and allow companies to deliver their best within short time.
As a global leader in Analytics, Deloitte has more than 15.000 consultants with extensive analytics expertise and has successfully implemented more than 4.000 Analytics projects worldwide. Our vast industry expertise and partnership constitutes our capability to embed analytical decision-making. By developing strategies tailored to specific requirements of your business, we help you turn everyday information in your company into actionable insights.
To reduce our clients’ burden from the unexpected risks of digital transformation and to show untapped potential of a smart factory, Deloitte's Digital Factory delivers its own digital vision. It provides our clients with a tangible digital production line that demonstrates the combination of the old and new supply chain world and manufacturing operations.
The digitalization of industrial production lines has already been realized at the Digital Factory. It consists of four stations. Each of them integrates seamlessly with robots, sensors, RFID-Tags and other digital technologies such as smart glasses and 3D printers. All data generated during production are sent to a cluster of servers in the cloud and then cleaned and analyzed with trending Big Data tools.
Seit mehr als 10 Jahren ist Andreas Staffen verantwortlich für das Aufsetzen und Durchführen von globalen “State of the Art” Manufacturing Execution und Business Intelligence Programmen sowie der Einführung der dafür notwendigen IT Prozesse.
Florian Ploner has more than 18 years experience in consulting, focusing on manufacturing industries. He has helped his clients across Europe, the US & Asia with global transformation programs and digital transformations, enabling new business models as well as optimizing business processes by leveraging the latest technologies. Florian Ploner leads Industrials in Consulting and is Partner in Technology Strategy & Transformation practice.