How Smart is Your Smart Factory?

As a core component of Industry 4.0, the Smart Factory promises significant productivity increases. But connecting a factory to the cloud & collecting data does not necessarily make it "smart."

Monica McDonnell
Monica McDonnell
7 avril 2021 4 min de lecture
Smart Factories and the Role of Data Analytics.
The automotive industry has been at the forefront of successive waves of transformation in manufacturing, from the introduction of the assembly line to global supply chains and the introduction of robotics. Now it is investing in the next big change – the Smart Factory. This phase will be characterised by connectivity, flexibility and production process steps that produce and utilise data at massive scale. As a core component of Industry 4.0, the Smart Factory promises significant productivity increases. But connecting a factory to the cloud and collecting data does not make it a Smart Factory. Creating a multi-purpose data and analytics foundation will increase net value delivered by Smart Factory projects.
Most initial ‘Smart Factory’ projects are, in truth delivered by small islands of data and analytics - delivering pockets of excellence. Managing these solutions on the cloud deliver significant cost and deployment advantages over on-premise solutions. However, without a strategy to build an integrated set of data to support all Smart Factory initiatives, the individual project approach will quickly lead to unforeseen, and unmanageable, costs – undermining all the benefits delivered on the shop floor.

New challenges, high expectations

An industry that prides itself on delivering exceptionally high-quality product with extreme efficiency is now realising that it must dynamically disrupt itself to meet the rapidly evolving demands of customers. The efficiency of a mass-market production line must be transformed to deliver mass-customised products, without adding back the costs or inefficiencies so effectively squeezed out over decades of progress. Flexible manufacturing principles, driven by Smart Factory capabilities are a core part of the answer.
The danger is that running individual, isolated Smart Factory projects will not deliver the step change in productivity anticipated. These projects are neither repeatable nor shareable and quickly incur costs that outweigh the benefit due to data and analytics management overhead.  In order to combat rising costs, analytics needs to be industrialised too. This challenge has been recognised by manufacturers as an industry-wide challenge applicable to all – and many industry leaders have formed an alliance - The Open Manufacturing Platform – to directly address the common challenge of enabling smart manufacturing at scale.
What’s needed is connected data foundation. Data which represent every process step for each individual vehicle can, and should, be linked in the digital realm to create a digital thread for each VIN number. This digital thread of data is essential to understand the interactions between as well as within every production step in the factory.  When used correctly, a digital thread can detect the impact of all changes in the production process. In turn, this allows for the global optimisation required for mass customisation.

Connect and reuse

Connected data enables reusable, shareable and portable analytic solutions to be created for enterprise or even eco-system wide ‘stores.’ Each project team collects and prepares data to build analytic models to address specific improvement opportunities. This is ‘published’, and so can be easily reused – in part, or completely – across the enterprise or eco-system. Since machine data is a common requirement for quality, production and maintenance analytics, the same data can be used to implement additional solutions – driving further value from largely the same data set. This is the premise of Volkswagen’s Industrial Cloud, on which Teradata is a proud a partner.

Reduce total cost base

Factories are cost centers, and so always must do more with less. Just as shared vehicle platforms have transformed the cost base and the efficiency of leading automotive firms in the past decades, so integrated data platforms can manage costs in the connected factory. All data sets bear a maintenance cost, as do analytic routines in order to ensure data quality does not drift, and analytics keep pace with the business.  AI and ML algorithms need regular retraining with large data sets – further adding to the cost of utilising this technology to improve manufacturing outcomes.
OEMs looking to drive increased productivity & flexibility while simultaneously lowering total production cost must consider an integrated data platform to support this effort. Without an integrated platform that feeds shared analytical models and processes, OEMs may find themselves simply transferring cost from the shop floor to the data centre, instead of achieving the full value of productivity improvements through analytical solutions. Due to the sheer volume of data and analytical routines involved in implementing the Smart Factory at scale, analytic and data management costs could ramp up quickly. Duplicating these data sets, and accompanying analytical routines, unnecessarily increases the maintenance costs of Smart Factory deployments.

Teradata Vantage’s proven analytic scale, combined with Teradata’s expertise in data ops and analytics ops, is an ideal foundation for a truly smart factory. The same code base is directly transferable to Teradata instances on premise, and across the leading cloud providers – ensuring longevity and reusability even if the analytic hosting platform strategy varies between countries or factories. Teradata can enable Smart Factory capabilities that reinforce the automotive industry’s leadership and commitment to highly efficient mass production, and mass customisation, of high-quality products.

À propos de Monica McDonnell

Monica McDonnell is a highly experienced consultant in the field of enterprise software, digital transformation and analytics. Her career has spanned Africa, the US and Europe with time spent on ERP and supply chain planning before focusing on delivering value from data. Monica advises on how to deliver business value by combining good data governance and advanced analytics technologies. Helping automotive companies understand how to release the full potential of Industry 4.0 technologies, and dramatically improve customer experience management as enabled by the connected vehicle is central to her role. Monica earned a BSc in Industrial Engineering from the University of Witwatersrand, and a MSc in Software Engineering from Oxford University. 

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