TLM: Evolutionary view of PLM in IoT Landscape
IoT system in Lifecycle Management (TLM)
“Things” Lifecycle Management (TLM) is an evolved PLM solution that can cater to product innovation requirements of a dynamic IoT system, says Ram Dwivedi.
The emergence of IoT and its dynamic structure has forced various existing enterprise systems to evolve so as to be able to meet its unique requirements. Product Lifecycle Management (PLM) is one such platform, which deals with the process of managing the entire lifecycle of a product from inception, through engineering design and manufacture, to service and disposal of manufactured products. In the process, a PLM platform deals with the assembly of devices too. When we look at an IoT system, we find a complex and ‘dynamic’ (ever changing) system of devices, along with additional challenges like connectivity, security protocols, etc. This additional degree of complexity makes it challenging for PLM professionals to cater to the IoT systems using current PLM products. As a result, current PLM products have to evolve in order to serve IoT space better. A futuristic PLM needs to be able to handle not just the product(s) design and development but many other issues like dynamic connectivity, security, privacy, analytics, etc.
Since ‘Product’ in Product Lifecycle Management (PLM) is comparable to ‘Things’ in “Internet of Things”, it would be appropriate to call the evolved PLM solution for IoT as “Things Lifecycle Management” (TLM).
This article is an attempt to establish guidance for the concept of TLM, and explore the related challenges in further details.
“Things Lifecycle Management” (TLM) – An Introduction
A typical Product Lifecycle Management (PLM) platform is not perceived as a very capable system in managing the dynamic IoT systems. In the IoT landscape, there are individual systems (let’s say household appliances or industrial units) that connect with other systems (sensors, mobile, storage, etc.). Once we include in other relevant factors like multiple industry standards, protocols, latency, security etc., we have a very complex, dynamic system that requires a much-advanced PLM system to manage product innovation. Definitely, current PLM systems have to evolve in order to accommodate requirements of an IoT system, and we call such evolved PLM systems as Things Lifecycle Management (TLM). Figure 1 illustrates the evolution of PLM to TLM in IoT landscape.
Fig. 1: Evolution of TLM for IoT Systems
In TLM, we still have the core PLM capabilities like collaboration, parts & document management, configuration management, etc. However, there are other layers that add up to this core PLM capability:
Connectivity: Connectivity refers to communication and processing units
Data: This layer refers to storage, buffering and access of data captured
Applications: Applications layer refers to various software like Reporting & Analytics tools that process the data for intended results, and
Processes & Collaboration: This layer refers to people and business processes.
Please note that Security and Privacy are two other factors that envelope core PLM capabilities along with all the above-mentioned layers. The aggregated architecture is what we refer to as TLM.
“Things Lifecycle Management” (TLM) – A Case in Point
A robust TLM system should be able to track a product feature in IoT landscape, trace the root cause in case of a failure, manage the solution, and be able to govern effectively with robust processes in place.
Let me share a case that shows the relevance and importance of a TLM system in a typical manufacturing environment. A shop floor had IoT system installed to relay the required production data at regular intervals. However this data from IoT system had no integration with product design data in PLM system. In one incidence, a Manager found a critical quality issue in the manufacturing line after a substantial delay, but he was not sure about how to request PLM system owners to make a design change. Such lengthy, manual correction processes cost a company lot of money, and may impact its brand image in the long term. An efficient integrated system between PLM and IoT could have helped this company deal with the problem more efficiently.
One big reason for this lapse was due to the inability of the existing PLM system to capture and process the IoT data. An efficient TLM would be able to capture and process the IoT data, and be able to find out the issue. It would then respond back to the shop floor with a work order, along with required drawings and other relevant product data (as illustrated in Figure 2). This problem would also be fed back to TLM quality and compliance modules. This data could also be used in future by engineering (product design), services (maintenance schedule, resource optimisation), and other business decisions like inventory management, supply chain management, etc. Thus an efficient TLM system could handle “trace-track-manage-govern” process without any manual intervention.
Fig. 2: An efficient data flow between TLM & IoT
The above case demonstrates just a small part of TLM utility. TLM can also take care of warranty and service issues, and can handle and account for the software updates to the system (equivalent of SLM in conventional systems). TLM systems can also collect and process performance data from an IoT system, and help with continuous system improvement and better future design. An efficient TLM system can work seamlessly with other systems like MES, ERP etc., to collect and process data, so as to help with efficient planning of demand, cost, pricing, etc.
There are many such possibilities with an efficient TLM platform, but we need to first address the associated challenges in developing such a system.
Evolution from PLM to TLM – Challenges
The following are some of the challenges we face for successful evolution of existing PLM solutions to TLM of future, and its adoption by the industry.
Dynamic Ecosystem of ‘Things’: In IoT ecosystem, size of the dynamic super-system, and as a result its complexity, changes constantly, based on devices connecting or disconnecting. Hardware (mechanical, electrical and electronics) and software (application development platforms, IoT applications, business intelligence, security, etc), are integrated efficiently to get the maximum out of the system. Additionally, we have further issues like connectivity, security, privacy etc. An efficient TLM system needs to handle this dynamic web of connected devices in a user-friendly way.
Integration with other Applications: A very likely scenario TLM is going to see is its cloud hosting, along with other allied platforms like ERP & CRM that help manufacturers connect, analyse and manage their product data. TLM would require seamless integration with such other interfacing platforms to harness the maximum benefits. Now, these allied platforms too will evolve with time as per IoT landscape and cloud hosting, resulting in a complex architectural design.
Multiple IoT Industry Standards Compliance: In order to leverage full benefits, a TLM system should be able to adopt/incorporate/handle other parts of IoT value chain besides just a particular device, which means integration of devices, delivery platforms, network, applications, and customers. This will involve incorporating leading industry standards (which unfortunately change for different vendors, markets, industry, etc).
Enhanced Data Analytics Capabilities: TLM is required not only to support all those connected devices but be able to process the data generated by them, and bring up meaningful information for its users. As a result, Data Analytics, Business Intelligence and Machine Learning are going to be the integral part of TLM offerings.
This capability will help the users through improved IoT system development time, reduced quality and compliance issues, better cost estimation, smarter system integration, etc.
Security & Privacy Concerns: Security is going to be of paramount importance. Since individual units in IoT system interact, collect data, and process information constantly, you want this integrated ecosystem to be completely secured.
Similarly from privacy perspective, IoT network shares data with a number of devices, networks, and individuals. TLM has to ensure what data is to be shared, with whom, and most important – what data should never be shared.
Strategic & Tactical Decisions in TLM Landscape
There are a few strategic & tactical points that TLM professionals should keep in mind while serving the clients.
TLM Consultants need to learn where to draw the line for a working system that does not go overboard in design and implementation. A very complex super-system may offer great capabilities but it should also be manageable, cost-effective, and user-friendly. It should provide required value to users, and align with a company’s strategic direction.
Business Consultants will have to be well aware of IoT architecture and TLM’s role at each process stage. They have to understand finer nuances like new business requirements (what data matters & why, frequency of data collection & processing, etc), cost (bandwidth and storage issues, solutions), data explosion issues, data model (new product data model for IoT), security and privacy standards, system’s scalability vis-à-vis ever-expanding data, etc.
TLM software vendors will have to decide on how much functionality they embed into their offerings. TLM software would probably start with few basic packages, with further add-on possibilities.
These strategic and operational requirements will see emergence of a new set of expertise within TLM domain.
Internet of Things (IoT) has finally come of age, and it is going to be a much larger market than some other like big data, predictive analytics, or cloud. A network of connected devices will be a lot more complex than the systems we handle through today’s PLM solutions. Need of the hour is to be ready with robust TLM software solutions as new opportunities arise due to enhanced IoT adoption in different industries.
A big hurdle to cross would be circumventing various industry and government standards to optimise TLM offerings. A mature TLM might have to wait for some more time before convergence of these standards takes place. Also, TLM software would have to ensure security and privacy concerns of the users. Going forward, TLM will also see the increased involvement of Artificial Intelligence (AI) and Machine Learning.
A robust TLM is still work in the process but have to reach the maturity fast. There is a whole new world of IoT devices in almost all the industries that will require help of TLM in its product innovation journey.
Ram Dwivedi is Senior Manager, Digital Business – Cognizant Business Consulting (CBC). The views expressed here are personal, and do not represent the employer, or any other organisation he is related to.