Jul 17, 2020

AVEVA: The future of manufacturing post COVID-19

Supply Chain
Michael Schwarz
5 min
Michael Schwarz at AVEVA shares views on how manufacturers can adapt to respond to business disruptions
Michael Schwarz at AVEVA shares views on how manufacturers can adapt to respond to business disruptions...

The global supply chain has experienced an unprecedented level of disruption and forced manufacturers to think about their operations in an entirely new way. Manufacturers are still under a significant amount of pressure to produce essential products like hand sanitizer, protective face shields, respirators and ventilators. Despite all these efforts, it has proven difficult to keep up with rapidly changing demand.

Digital transformation in the supply chain has to be about more than just cost-effectiveness. The ability to continue operating and fulfilling customer and partner obligations even under the most unexpected and challenging market conditions is equally important in an age where agility and resilience are paramount.  

According to McKinsey1, digital manufacturing technologies will transform every link in the manufacturing value chain, from research and development, supply chain, and factory operations to marketing, sales, and service, and “advances in virtual and augmented reality, next-level interfaces, advanced robotics, and additive manufacturing are all opening the gates to digital disruption.” 

Manufacturing Operations Transformation (MOT)

Many industrial manufacturing companies have started their transformation journey with plant and machine automation and the gains of productivity and process repeatability that brings. Plant equipment automation minimizes the amount of manual operations and maximizes the physical throughput. To further improve the utilization of equipment, plant operations have matured into using IT and software applications as the basis for improvement strategies such as replacing paper-based work instructions and data collection.

The use of software and IT, such as manufacturing execution systems (MES) has provided more benefits than increased operational efficiency through core application functionality. Historical data and modern big data analytics offer additional payback opportunities by providing optimization insights and facilitation of continuous improvement. 

Visibility of operational execution and inventory status based on automatic data exchange with enterprise systems in near real-time enables better decision making and collaboration between plant and enterprise functions – and this advancement is also helping manufacturers to make faster changes and to adapt. 

Collaboration across people and systems

A key factor for future manufacturing operations improvements is the effective collaboration of people and systems in a digital, automated and integrated fashion. Digital transformation of operational processes captures and transforms best practices into electronic workflows, to connect assets and systems, establish systematic people and system collaboration and to empower the mobile and next generation workforce. 

Connecting systems together can also orchestrate process across functional domains (horizontal integration) and can integrate with business functions (vertical integration). Enforcing consistency of operational procedures and the automation of workflows with electronic records of manufacturing execution activities and data preserves the investments in existing plant systems while offering significant operational efficiency improvement potentials.

Adaptability through data

The primary enabler of an effective multi-site Manufacturing Operations Transformation is the enterprise-wide standardization of operational processes, enabled through the standardization of information technologies. Such IT harmonization is the foundation to digitally model, integrate, execute, and govern operational processes and related information flow consistently across multiple plants. Standardization of operational processes is possible with the following components: 

  • An open engineering and runtime platform, hardened for industrial use and designed for enabling integration of business, manufacturing operations and production processes and data. 
  • A broad suite of industrial applications scaling from rapid ROI equipment performance optimization to full manufacturing operations management functionality.
  • A reusable operations process modelling approach, which standardizes all operations, simplifies deployment of processes to equipment, systems and people.

The role of a manufacturing IT platform is to provide adaptability to local plant nuances and a plant asset model which applications can use to blend human and automated activity in the execution of standardized processes and business rules. The platform adapts to individual local physical equipment and automation, while maintaining the data and information models of the processes and flow of data to other applications and towards the enterprise.  

This transformation establishes a sustainable digital twin of the manufacturing plant which offers to further optimize execution of demand which is allowing manufacturers to shorten their lead times and reduce inventory and improve supply chain performance.

When consistently implemented across multiple sites the digital twin of the plant becomes the enabler for agility, velocity and traceability that can support the new business models, product and customer engagement that digital transformation is enabling at the business level.

Enabling empowerment and future proofing

From optimizing asset performance, to raising productivity, to elevating quality, there are countless reasons to pursue Manufacturing Operations Transformation. It is the continuation of transformational activities that align manufacturing IT systems across the business to provide both operational and business improvements. Improvement requires changing processes and systems, and continually training the workforce. This often requires replacing paper and legacy systems with software that provides automation, and ensures work processes are in-line with targets.

Modern Manufacturing Execution Systems deliver the platform for this transformation with agnostic connectivity to business systems, data lakes and automation and IIoT systems to deliver the next level of benefits though predictive analytics and prescriptive planning and scheduling.

While this digital transformation of the $10-trillion-plus global manufacturing sector will play out over a decade or more, pioneers are moving to drive bottom-line and top-line impact in the near term. Cloud computing–based tools also will allow suppliers to collaborate faster and more efficiently: an engine maker can share three-dimensional models of component design within its network, and each supplier in turn can share information about price, production timelines, quality and delivery.

Connectivity is the future – and available now. Having a built-in connectivity to existing plant floor systems, devices and equipment will eliminate inefficiencies, maximize profitability, and empowers your team to respond to future external disruptions.

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May 11, 2021

5 Minutes With PwC on AI and Big Data in Manufacturing

Georgia Wilson
6 min
PwC | Smart Manufacturing | Artificial Intelligence (AI) | Big Data | Analytics | Technology | Digital Factory | Connected Factory | Digital Transfromation
Manufacturing Global speaks to Kaveh Vessali, PwC Middle East Partner (Digital, Data & AI) on the application of AI and Big Data in Manufacturing

Please could you define what artificial intelligence is, and what Big Data is?

AI is the ability of a machine to perceive its environment and perform tasks that normally require human intelligence, and it’s a whole field of different technologies, techniques and applications. 

Big data is a set of tools and capabilities for working with, for processing, extremely large sets of data. 

How does AI and Big Data work together?

Big data is just one of the enablers of AI, though as we see increasing volumes of data, it’s one of the most important 

How can this be applied to a manufacturing setting?

Broadly speaking, there are many benefits of AI, and the use of data, which include reducing costs, minimising human error, and increasing productivity and efficiency. The important thing to consider is any setting - for the use of any technology - is what is the problem you are trying to solve? Be it merely automating repetitive tasks or to reinventing the nature of work in factories by having humans and machines collaborate in order to make better and faster decisions.  

Why should manufacturers use AI and Big Data when adopting smart manufacturing capabilities, what is the value for manufacturers?

One view is, again, the economic benefits of AI, which come in manufacturing as a result of: 

1. Productivity gains from automating processes and augmenting the work of existing labour forces with various applications of AI technologies. 

2. Increased consumer demand due to the increased ability to personalise and tailor manufactured products, along with higher-quality digital and AI-enhanced products and services. 

Manufacturing (and construction industries) are by nature capital intensive, and in our 2018 report, “The potential impact of AI in the Middle East,” we estimated that the adoption of AI applications could increase the sectors’ contribution to GDP gains by more than 12.4% by 2030. 

How can AI and Big Data help manufacturers to evolve in the Industry 4.0 revolution? What about those already looking at Industry 5.0?

It’s really about the investment you make now, in order to futureproof your business. 

We typically see two broad strategies or approaches to the adoption of AI. There are things that we can do immediately, without any recourse to Big Data - which is to adopt technologies we describe as Sensing, those involving computer vision, for example. There are plenty of use cases where these can be used immediately in manufacturing, such as for automatic fault detection. However, there is a longer term play which requires investing in data - getting the right collection mechanisms in place, storage, data governance, Big Data capabilities etc - in order to develop increasingly valuable machine learning driven AI use cases. This is absolutely necessary for long term adoption success. 

What is the best strategy for organisations looking to realise the value of AI and Big Data in manufacturing?  

AI and Big Data are only one part of a successful smart factory. The organisations that lead on AI adoption are those who have already made the most progress in digitising core business processes. In order get ahead in using AI solutions at scale, there are a number of technology investments and organisational decisions to be made, including: 

1. Digitising processes ultimately leads to improved ability to generate data, and in the manufacturing setting - with many 100s of sensors generating 1000s of measurements in real time, the result is Big Data. Data is key to building AI so reliable and accurate data acquisition, management and governance are key. The production line and factories play a critical and direct role in the data-acquisition process. 

2. AI strategy, both long and short term, begins with the use cases, the business applications. Manufacturers need to ask where they want to use AI and gather these use cases together and prioritising projects based on a balance of expected impact and complexity of implementation. 

Of course, in addition to technology and business processes, people are at the heart of any successful technology adoption. AI teams need to be composed not only of data scientists, also data engineers and solution architects to enable their work, data stewards to ensure accuracy, and increasingly so call “Analytics/AI translators” who are able to communicate with business leaders and technology experts. Culture is also key, and manufacturers need to enable a data and AI-driven culture, building trust in data and algorithms by educating their workforce about AI and its capabilities, how best to extract value. It’s not just the positive of course, but also the risks and limitations, as these when encountered without expectations having been set, can significantly impact willingness to invest. 

What are the challenges when it comes to adopting AI and Big Data in manufacturing?

PwC research has shown that one of the major challenges to implementing AI is uncertainty around return on investment (ROI). As I said, there is significant investment required for a long term data and AI strategy to be successful, and expectations around the time to see tangible returns must be set realistically. 

Many companies also struggle with the data side: collecting and supplying the data that an AI system needs to operate, and ensuring that it is accurate. Again, this speaks to the bigger investments required in digitisation. 

Some of the main challenges for manufacturing companies with implementing AI at a scale from our research include:  

  • 40% → Technologies not mature  
  • 40% → Workforce lacks skills to implement and manage AI  
  • 36% → Uncertain of return on investment  
  • 33% → Data is not mature yet 
  • 32% → lack of transparency and trust  
  • 24% → Work councils and labour unions  
  • 22% → Regulatory hurdles in home & important markets  

One element highlighted here, particularly around lack of trust, and labour unions, is that AI is typically misrepresented in the media as “replacing” workers, and taking jobs. Yes, there are efficiency gains to be made from automation, as there have been since the first industrial revolution. But we believe that Data and AI are at their most valuable when they are used to augment workers, enhancing their abilities and the products being manufactured. 

Another challenge we’re starting to see emerge is cyberattacks increasingly targeting interconnected equipment and machinery in smart factories. PwC recently hosted a webcast, in cooperation with the National Association of Manufacturers in the US and Microsoft to discuss this. 

What are the current trends in AI and Big Data in manufacturing?  

  • We see companies putting slightly more focus on adding AI solutions to core production processes such as the engineering, and assembly and quality testing 
  • Safety is of significant importance, with techniques adopted in protocol adherence capabilities (for example maintaining safe distance from specific machinery) being adopted in more every day scenarios for COVID-19 protocol adherence 
  • There is considerable interest in predictive maintenance for large machinery involved in manufacturing processes, and also supply-chain optimisation

What do you see happening in the AI and Big Data industry in manufacturing in the next 12-18 months? 

Honestly, I think we’ll see a continuance of where we’ve already been going for the last 12- 18 months. AI and data are already being used in manufacturing but this use doesn’t get as much attention in the media as, say, healthcare, but the success stories are there, and they will continue as operations continue their digital journeys. 

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