Developing Strong 'Digital Threads' To Enable Real-Time Maintenance

In today’s market it’s becoming increasingly important to keep the profit margins for the equipment competitive, so much so that vendors are relying heavily on service contracts with their customers.  However, it gets tricky to balance service contract on top of the equipment sales as it could lead to customer dissatisfaction and hence lower demand.

Mnet 194421 Maintenance
Sivakumar 'Siva' MuthukrishnanSivakumar "Siva" Muthukrishnan

In today’s market it’s becoming increasingly important to keep the profit margins for the equipment competitive, so much so that vendors are relying heavily on service contracts with their customers.  However, it gets tricky to balance service contract on top of the equipment sales as it could lead to customer dissatisfaction and hence lower demand.

Original Equipment Manufacturers (OEMs) sell equipment and receive services revenue from after sales service contract. With ever-reducing margins on the equipment sales, services revenue are seen as an attractive business. From an OEMs’ business operations standpoint, they need to keep highly skilled maintenance staff and spares inventory with high safety stock to mitigate risk of end Customer’s unplanned stoppages. A slight variation in performance of the machine could lead to quality issues of the end product being produced.

Because of this, real-time insights into equipment/machinery performance are necessary for both OEMs and customers alike, and new technology is helping both OEMs and customers alike predict and manage potential issues. With a smooth flow of information across supply chain using technologies like IoT and big data, OEMs and their customers benefit by receiving real-time data collection about their critical equipment parameters, analyzing them using machine learning algorithms and deriving predictive analytics for those equipment/machinery.

With the current advances in technology, we can train data model on similar type of equipment data for specific industry/usage pattern and fine tune it over the period of time. This data is then used holistically along with other data from business operations to derive actionable insights and automate actions based on artificial intelligence technologies.

The best strategies companies can implement to best manage service contracts, as well as their customer relationships include developing (1) a digital thread to create a seamless flow of data, and (2) enabling your business processes with IoT capabilities.

No. 1 - Developing your “Digital Thread”: This is a thread that creates seamless flow of data right from conceptualizing a product to design to manufacturing to installation to repair to disposal and back to product design, that ties all of these together increasing automation, reducing latency and errors, creating new capabilities, and improving performance.

Figure 1.Figure 1.

No. 2 - IoT Enabled Business Processes: It’s important for OEMs to “IoT enable” business process to achieve desired business outcome, as every process step generates information that can be leveraged across the supply chain.

Managing the Risk - With availability of “Digital Twins” technologies (virtual model of process, product or services) it is possible to prove the solution would work in real-life scenario before investing big bucks into creating physical networks/infrastructure. Also, one can train the field service personnel on specific equipment or it can be used to simulate virtual factory.

And what about the infrastructure for IoT? Gartner predicts 20BN connected devices that will be in use by 2020. The basic thing required for IoT enablement is ability to identity things uniquely over the network. With the adoption of IPV6 protocol we have capability to identify things uniquely over the internet (2^128 IP addresses).

Therefore, it is possible to uniquely identify connected sensors/devices way beyond 20BN and many years beyond. Virtually no limitation on connections, every small or large products are set to be manufactured with built in smart sensors to emit data. Old products, however, will need external sensors to be mounted for specific use. IoT has arrived and set to grow unbound.

In the current market scenario there are hundreds of vendors who provide IoT technologies across infrastructure, platform and application architecture layer. A niche few vendors provide near end to end solution.

Figure 2. IoT Value ChainFigure 2. IoT Value Chain

From the IoT Value Chain (Fig.2), it is quite clear that a very different technology stack is required compared to that of a normal business IT. Therefore, this would be viewed as a new or additional investment on part of businesses. Also, niche skill is required to manage this stack. To overcome these challenges, companies are looking for readily available platform and solutions on the cloud which would do away with initial capital investments and help rapidly in going to market.

However, from an OEM perspective creating and managing the digital thread (Fig1) across business operating systems is crucial. Because of this, it’s beneficial to look for vendors who provide an integrated stack across IoT value chain with flexibility to extend, customize and integrate in context of OEM’s business.

Enterprise application players focusing on developing complementary IoT applications with built-in IoT platform technologies along with Digital Twins, big data platform, machine learning, mobility, etc., as depicted in fig.2, which are integrated with existing on premise or cloud based applications with partner ecosystems for infrastructure layer (such as devices, connectivity etc.) would emerge as a primary choice of technology providers for IoT deployment and stand a good chance to lead the pack.

Without real-time monitoring of machine parameters often quality issues are known at the time of quality inspection of the finished products, by the time, considerable production already underway, hence it could lead to rejection of the entire batch, thus cost of quality is very high (specifically in high price precision items) and in such scenario manufacturer (OEM) being consumer of machinery need to monitor machine performance real-time.

Sivakumar "Siva" Muthukrishnan is global alliance manager at Tata Consultancy Services (TCS).

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