Talkin’ bout a Revolution?
For many manufacturers, the path to building a Smart Factory is still confusing because of information overload and the lack of an overall digital transformation strategy. Amidst the excitement related to Industry 4.0, new technologies are being applied in manufacturing environments at a high pace, including IoT platforms, big data, machine learning and Autonomous Intelligent Vehicles (AIVs), among others.
As with any extensive company-wide transformation, trying to achieve the end goal too quickly can lead to wasted time and money. In order to overcome this challenge, manufacturers should view this transformation as a step-by-step approach allowing to progress through a natural evolution.
It All Starts with Integration
The Smart Factory evolution must be built on automating the collection of data from machines and processes, and transforming that data into immediate insights.
At the current stage, data is often available but difficult to use for decision making or implementing improvements. The data is in siloed systems, requiring manual work to integrate and translate into useful information. Problem solving at this level is possible but extremely time-consuming. But as the competitive landscape of manufacturing changed, and demand for mass customization increased, the industry has reached a point where these manual processes are no longer efficient.
Data integration alone can be a challenging task. The selection and proper enrichment of relevant data is, in many cases, not just a technical problem but requires a detailed and in-depth knowledge of the manufacturing steps to be analyzed and optimized. Even worse, critical data sources aren’t available due to lack of equipment integration for legacy tools, incomplete product quality monitoring, or deficiencies in material tracking. Eliminating these gaps in connectivity should be accompanied by the responsibility to “encapsulate” all the complexity caused by the different interfaces, and finally isolate those details from the higher processing layers.
With comprehensive integration of all the disparate data sources into one single version of truth – in one location and always available – problem solving becomes almost frictionless.
Spice up Manufacturing with Automation
A key enabler of highly automated manufacturing for the high tech industries is a factory-level automation architecture that supervises, coordinates and orchestrates the several factory systems such as MES, Dispatching and SPC as well as Process, Metrology and Logistics Equipment. This automation architecture reacts to different factory events that the systems generate (e.g. job completed; new order arrived; equipment down), keeps track of the state of every activity in the factory and handles errors and exceptions. Like it or not, the handling of errors and exceptions presents the greatest challenge, as the cost of automating the error handling can be prohibitively expensive.
Although in general the new technologies like AI and ML can be implemented in parallel to existing tracking and control software, the overall value these create is nothing but a fraction of what they could achieve if the solutions were conceived in combination to leverage each other’s capabilities. The companies that are best positioned to survive and thrive in the future are the ones that embark on the journey toward full automation, taking decisive steps, but one step at a time.
Transform from Reactive to Proactive
Building on a solid automation architecture, the adding of new technologies such as Machine Learning and Artificial Intelligence becomes feasible and beneficial. One key to successful real-time factory monitoring and optimization is not merely the presentation of the current state, but to relate the current state to the historical
context. Historical data analysis provides context and reveals deviations such as unexpected process time, uncommon material accumulations, or issues with material transport. Combined with swift control actions upon every new data point collected, manufacturing operations can shift from reactive problem solving to proactive analysis and improvements.
Digital maturity requires nothing less than a holistic, mid- to long-term digitalization strategy, originating from
the business strategy. There are no shortcuts that can move a manufacturer from Industry 3.0 to 4.0.
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