Embrace Digital Transformation with Engineering Cloud for Tangible Business Values
Abhishek Goyal, Vice President & Global Practice Head for Digital Engineering at Infosys, explains the concept of engineering cloud and walks us through the trends, best practices, and solutions in the cloud for transforming engineering functions in product enterprises.
Global supply chains are highly complex and vulnerable by nature, but they play a critical role in the success of an organization. 2019 was a year of disruption with natural disasters and global geopolitical changes such as the trade war between the US and China and the Brexit impacting supply chains across the world. This was further compounded in 2020, when the Covid-19 pandemic brought logistical challenges driven by lockdowns and shortages in personnel, making the supply chain a macroeconomic concern. From scarcities in semiconductor chips in the automotive and hi-tech companies to a decline in global smartphone shipments, every industry faced unexpected and unwanted disruptions leading them to embrace ‘digital’ at scale. Product companies embarking on their digital transformation also began to invest in process transformation across the extended enterprise ecosystem.
The concepts of ‘design anywhere’, ‘produce anywhere’, ‘assemble anywhere’, ‘sell anywhere’ and finally ‘service anywhere’ have changed the paradigm of product enterprises. Digital transformation enabled by the cloud can bring these concepts into practice. Cloud brings flexibility, agility, scalability, and security for core engineering applications.
Cloud is transforming core engineering functions
Among the technological disruptions that have changed the way enterprises do business, the cloud is probably the most impactful. The pandemic further highlighted the need to adopt cloud computing with other digital technologies to enable remote work and support a hybrid workforce. The trend extended beyond digital-first businesses to traditional industries such as manufacturing that are increasingly embracing virtualization and cloud-enabled digital transformation through engineering cloud.
Engineering cloud refers to the use of the cloud for orchestrating and integrating an organization’s engineering functions. It differs from cloud engineering that relates to the engineering of the cloud infrastructure and tech stack.
Take the case of a large global elevator manufacturer that had grown both organically and in-organically and sought to consolidate its product data in a single Product Lifecycle Management (PLM) system with distributed file vaults across the globe. We recommended a cloud-based infrastructure to host their product data in a single, consolidated PLM system that allowed them to scale up the infrastructure on-demand and reduce the Total Cost of Ownership (TCO) of their PLM infrastructure by 30%. In addition, the site consolidation brought the design teams together, fostered collaboration among them, reduced the standard parts duplication by 25% across locations.
Similarly, a global forklift manufacturer implemented their entire Telematics platform on the cloud across product lines to help them seamlessly integrate the operators, site supervisors, dealers, and OEMs. There was an immediate velocity improvement of up to 15% while automation led to a roughly 30% reduction in testing time and 19% overall productivity improvement. Operator efficiency improved by 12%-15% for pilot customers, promising further improvement with every customer rollout.
These are two of the many instances where the engineering cloud has transformed the engineering functions of enterprises to drive tangible business value.
Important shifts in engineering transformation
‘Servitization’ is a new business model that drives business transformation in product enterprises. Products will no longer be sold as ‘products’. Customers will not find it necessary to invest in acquiring the whole product when they are given the choice to avail and pay only for the specific features/ functions that they need and for the duration they’ve availed. For example, a large ‘Cold Storage Chain’ can use the refrigeration capacity only during certain periods of a year, adjust the temperature as per the products that are stored, and pay to the refrigeration manufacturer only for the availed time. Servitization has thrown open new revenue generation models.
A drive towards Industry 4.0 with a clear objective of developing connected, smart products fueled by the technological disruptions of IoT and IIoT also demanded change in the approach taken by Product enterprises for their engineering departments. The main drivers of change in the industry are the need for real-time monitoring of products, switching over to predictive maintenance over preventive maintenance, and adopting sustainable product development such as software-driven cars, using the principles of Closed-Loop Lifecycle Management Integrated Software and hardware-driven product development.
To facilitate this new business model, the concept of digital thread becomes essential. A digital thread connects the product data from the time it is generated till its retirement – including its real-time performance. It captures the data flow across the extended enterprise – both in the physical and virtual form of the product.
The role of 5G in the cloud journey
5G and network topologies are expected to enhance data transfer rate with reduced latency making real-time experiences highly feasible. The industry 4.0 revolution, digital transformation, and automation strategies with artificial intelligence engines across industry value chains will become more realistic with 5G. Not only will the green field industry with digital transformation plans gain from it but brownfield requirements will also gain mileage through networking and connectivity infrastructure migration with 5G. Combined with IoT and smart manufacturing, 5G can support connectivity with remote access for controlling and optimizing operational parameters. Cloudification with 5G, can make digital platforms ready for servitization, bringing new business and revenue models that result in a great end customer experience.
A consortium with a leading university and an agricultural equipment manufacturing company initiated a SWARM Intelligence program to implement swarm harvesting robots on a test field that were networked through 5G. An autonomous harvesting process with multiple robots that can be managed by a single user was deployed. The robots needed a latency time of less than 10 milliseconds for the data to be sent to the cloud after being processed. Thus, 5G has the potential to boost regional food production with high biodiversity.
How can Product Enterprises change their landscape into digital and adopt the cloud?
For many historical reasons, cloud adoption has been slow in engineering, but the above-mentioned business drivers and the pandemic have created a shift in the paradigm for product enterprises.
In my view, engineering cloud adoption is like a journey, and it can’t be done overnight. Product enterprises need to think through multiple business priorities, challenges, and choose the right partner, and Hyperscalers to proceed on this path. From our experience, the following pointers need to be considered:
- Business priorities: Defining short, medium, and long-term priorities
- Cross-functional organization needs and integration points: Choosing between applications on the Cloud vs applications on-premises
- Spending and financial models: Considering using the cloud to convert the one-time huge Capital Expenditure (Capex) model to regular Operating Expenditure (Opex) model
- Security Requirements: While cloud brings a lot of additional security, organizations need to think through additional security aspects when they extend the applications on the cloud to their partners/vendors/suppliers
- Internal Readiness: Whilst organizations do away with their data centers via cloud adoption in the long run, the interim scenario of managing both cloud and on-premises needs to be managed effectively. This needs a cloud-ready internal team.
- Partner/Vendor Ecosystem: It is important to select the appropriate Hyperscalers and certified service providers for product enterprises. Their technical expertise and service experience make cloud adoption successful.
- Enterprise Architecture: Instead of siloed application view, product enterprises need to think through their enterprise ecosystem needs and drive their cloud strategy. For large enterprises, it is going to be multi/poly cloud environments in the future
The challenges and risks to look out for while migrating to the Engineering Cloud
Engineering applications are extremely sensitive. They hold important, proprietary product data. In the current global scenario, the hidden cost of an IP breach is more than what it is perceived. In our experience of working with clients, this loss of information can lead to losing more than 50% of the business and/ or can cause close to 40% of issues around operations. The cloud offers several security features, but product enterprises need to engage proper mechanisms to implement these features to protect their business interests.
Often, product enterprises think that a simple ‘lift & shift’ model holds good when it comes to migration to the cloud. In our experience, it is not that simple. For example, a large industrial products manufacturer in North America failed twice to migrate their Product Lifecycle Management application from a software vendor-provided SaaS model to an enterprise cloud. The team on the ground repeatedly overlooked the performance need. The nuances of SaaS, PaaS, IaaS need to be considered before hosting the applications on the cloud.
Network bandwidth plays an important role. Deciding which architecture to use on the cloud requires careful consideration of the cloud server hosting locations (from hyperscalers), the end-user locations, and current latency. Engineering applications such as PLM, CAD, CPQ, IoT, MES, IIoT applications differ in the way they are hosted in the cloud when compared to enterprise applications such as ERP, CRM, HCM applications. The former is not only sensitive but performance intensive.
Organizational change management is a highly overlooked element in most engineering cloud adoption programs. Engineering applications are performance-hungry, and the cloud architecture needs to be properly designed as per the performance need.
Whilst the end-users shouldn’t be impacted (on-premises over cloud), the organizations need to manage their expectations and business priorities such as what’s the right mechanism for data transfer or who will own the data in the interim.
As product enterprises tend to over-engineer their cloud implementation, they often lose sight of realizing real business benefits. As I mentioned earlier, it is a journey, the high scale of flexibility that the cloud offers must be leveraged well and constantly monitored to ensure maximum advantage.