Agile and Intelligent Manufacturing
What does it mean to have agile and intelligent manufacturing?
Manufacturing organizations, which have their core competencies reimagined around digital capabilities and rapid adoption of digital technologies such as moving to cloud, embracing automation, robotics and harnessing the abundance of data to drive organization cultural change and enable ecosystem, have led collaborative growth. Such organizations are set to be on the path to agile and intelligent manufacturing – such as automotive OEMs who have now embraced digital technologies within the product, across the value chain and are building purpose driven ecosystem partnerships with an aim to provide their customers with mobility solutions.
What is the best strategy to become agile and have intelligent processes?
Manufacturers need to rethink their systems, processes, products and services in this new paradigm. A good starting point is to keep in mind the outcome that customers would like to achieve and build your business proposition around the customers. For example, personalization of nutrition and wellness is a rising trend. While there are several value chain players providing point solutions, how can supplement manufacturers tap into this trend to increase their wallet share? This requires a holistic view of tapping into the intelligent and connected capabilities to not just reimagine the processes but also rethink their entire value chain and the ecosystems in which they operate.
What are the benefits of having agile and intelligent processes?
We have seen that companies having adopted the agile and intelligent processes are better positioned to repivot their organizations adapt to the changing business needs and deal with the economic impact of the same. Equipped with a near real time view of their business health and all their processes they are able to conduct a good impact assessment and take informed decisions with confidence. Such as a chemical company set up a command center to create an end-to-end view from order receipt to fulfillment and related stock orders movements to meet the demand. This system collected and analyzed data across multiple processes and deployed machine learning models to provide early indicators and prescribe actions, resulting in significant improvement in their schedule adherence and subsequently CSAT (customer satisfaction). With improved visibility, the order lead-time was also reduced, thus freeing up working capital.
What are the challenges of striving to create agile and intelligent processes?
Driving this paradigm shift requires a strategic vision and commitment from the executive leadership. They need to drive the cultural change and the shift from a traditional engineering culture to infuse a digital mindset across all the stakeholders including influencing the ecosystem partners. Finding the right talent and skillset to drive the agile & intelligent manufacturing remains elusive. The skills required are multidimensional, we need a business partner who is not just technology savvy but has a good understanding and contextual knowledge of the business.
Another big challenge is the data itself – ranging from data lifecycle management to defining a comprehensive framework of data exchange between ecosystem partners accounting for ownership, rights, data privacy & security concerns, industry standards for interoperability for real-time actionable data exchange. Other challenges include, managing complexity and infrastructure requirements that comes with increased usage of technology and operationalizing the IT/OT processes. Training of existing workforce to rapidly adapt technology and adapt to new ways of working.
How can industry 4.0 revolutionize manufacturing?
The combinatorial effect of the Industry 4.0 technologies is revolutionizing the operating model. As more things are being connected and in turn generating vast amounts of data, it allows real time monitoring of the assets and its context in which it is operating. Analysis of data provides deep insights into the operating conditions of the assets. Typical outcome of asset performance monitoring and failure prediction can be further explored as new service lines to the Customer/ Service Centers to be monetized in the form of service contracts and extended warranties. Analysis of the service history and real time asset performance provides further opportunities for personalized cross sell / upsell offers on spare parts, upgrade and resale.
What are the current trends within manufacturing?
Manufacturing Enterprises today are tending towards purpose driven, service oriented, powered by digital technologies, data and ecosystem.
Some of the key trends that we see are:
1. The traditional value chain has been disrupted, driven by the need to be agile with shorter or agile supply chains with improved visibility, track and trace based solutions, and rise in on-demand localized manufacturing.
2. New entrants such as technology providers have changed the competitive scenario – Uber with its disruptive models, Waymo with autonomous cars and Sony launching electric concept car. Enterprises are now reinventing and creating value added services driven business models with Purpose centric Ecosystem partners.
3. There is an increased focus on driving personalization of products and services. OEMs are seeking to engage with their customers to more involved B2B2C model on lines of B2C, disrupting the dealer and distributor channel. Honeywell Aerospace Marketplace, an ecommerce platform for used aerospace parts is a great example of how B2B companies are unlocking the potential and adopting new ways of doing business.
4. With a renewed push on meeting the sustainability goals and the consequent push for Circular Economy, there is increased pressure to secure smart packaging, reducing inventories and minimize logistics to cut down carbon footprint. Most automotive companies have taken up self -goals to reduce carbon footprint e.g. Mercedes Benz aims to have its carbon neutral passenger car fleet by 2039 while also striving for a carbon neutral supply chain and production.
What are the current technological trends within manufacturing?
With the increasing level of software in assets, products and operations, technology has become an integral part of Manufacturing to an extent where several manufacturing companies have started to rebrand themselves as technology companies. Key technology trends disrupting the traditional models are:
IoT and Edge computing which has put intelligence and decision making at the asset level on the shop floor
On demand compute power is empowering companies to build Digital Twin for the assets and processes without the need to invest infrastructure
AI / ML and advanced analytics technologies are enabling enterprises to harnessing the power of data to drive customer centricity, efficiencies at scale, driving pricing strategies and enabling new service led business models
Adoption of AR/VR technology to train and upskilling of workforce and remote access to expert guidance to conduct repairs and maintenance
Further 5G technology is well on the path to revolutionize the Connected & Autonomous car
How can technology help an organisation become agile and intelligent? (Such as: artificial intelligence, machine learning, robotics, automation, big data, analytics, predictive technology, blockchain etc.)
Technology capability is the core foundation for agile and intelligent manufacturing. The adoption of digital technologies has helped organizations in more ways than ever - connected products, connected workforce, connected supply chain and connected customers
Technology adoption in the Product i.e. Embedded products / sensorization of assets enable track and trace for packaging. Analysis of the asset performance data with advanced analytics provides capabilities such as early warning system for failure prediction. Timely action and maintenance will reduce downtime and improve customer experience. This also provides for opportunities for personalized cross sell/ upsell offers on spare parts, upgrade and resale. For e.g. Linde’s cylinders are embedded with sensors, generating data with the help of which they are able to track the exact location and predict the need for replenishment.
Technology adoption to drive Customer Engagement focuses on the shift from B2B to B2B2C with a deep understanding of the customer behaviors and preferences using AI/ML technologies. Driving Immersive customer experiences and engagement through Digital Dealerships using AR/VR and Gamification strategies. Enabling customer self-service using Conversational AI / Chatbots in After Sales service area. Most OEMs such as JLR, BMW, Audi, Volkswagen have piloted AR/VR and Gamification for virtual car dealership experience – allowing customers to configure the vehicle, get a 360 degree view of the car , virtually test drive with motion synchronized journey’s in virtual world.
Technology adoption in IT infrastructure such as Cloud computing helps organizations be agile and nimble to rapidly scale infrastructure and making end user computing faster, cheaper and seamless with a great user experience, employing effective cost models; business process agility. Volkswagen Automotive Cloud is going beyond the connected car services to offer value added services to its customers such as smart home connectivity, media streaming and personal assistants.
Technology adoption in the workforce has changed the way we work – social collaboration platforms have enhanced workplace collaboration and connected supply chain. On the shop floor, Robotics has played a big role in automating tasks , mobility has enabled paperless manufacturing, IoT sensors are helping meet the rigorous safety and sustainability goals and enabled EHS compliance. AR/VR technologies are helping close the gap in the skill deficit by enhanced training and enabling remote guidance by experts for repair and maintenance. GE has been training its field service engineers using simulations through a VR headset in a conference room setting, which not only saves time and money but is a lot safer too.
Combinatorial power of technologies is likely to disrupt existing business models and create new opportunities for value creation. For example, advancements in Chemical Informatics along with AI based technologies and higher computing power enabled by Quantum computing can shorten material discovery lead times by a factor of ten - from 20 years to less than 2 years. The use of additive manufacturing technologies, some capable of producing composite or multi-material components will be an enabler for light-weighting.
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