Industrial Automation Magazine India

The biggest challenge towards (IIoT) adoption is the culture readiness

Published on : Friday 08-10-2021

Sampath Kumar Venkataswamy, Senior Research Manager, IDC Asia/Pacific Manufacturing Insights.

Today, most industry stakeholders are aware of the many benefits of IIoT, yet are wary of joining the revolution. What could be the reasons?
The benefits for IIoT are indeed well documented and established but you do need to realise that the implementations are quite arduous and elaborate while requiring several layers of integrations across the enterprise. When manufacturers attempt a pilot, the number of variables and use cases that are considered are controlled and the benefits that are realised especially around overall equipment effectiveness (OEE) is quite substantial. But when organisations look at scaling these solutions, they invariably face issues around channelling multiple data streams while overlaying contextual information to accurately analyse the collected data. Creating these multiple logics at various entry points would be the first impediment, which if not designed properly could result in false positives and anomalies. The second major issue is around the cost and long deployment cycles – in the given business climate, organisations are quite keen to prioritise projects that have shorter implementation lifecycles, though the appetite towards technology spending has increased when compared to that of pre-Covid times. CIOs have also highlighted that the evolving nature of technologies and associated infrastructure has made the RoI estimates considerably difficult, which in turn limits the investment justifications and business cases.
 
What are the challenges manufacturers face in adopting IIoT solutions?
As with any disruptive technology, the biggest challenge towards adoption is the culture readiness. Ensuring that there are viable use cases and opportunities that are expected to bring in substantial financial gains and educating the internal stakeholders remain distinctly polar. Quite often data automation is equated to workforce redundancy, which can often dilute the core benefit of IIoT, i.e., data transparency and visibility. There are instances where the CIOs have routinely faced resistance from the worker unions as soon as they mention IoT.
 
The second major hurdle is around the existing process and organisational maturity which has a significant impact on the overall deployment outlook and success. There are instances wherein a shop floor supervisor upon realising that the daily production output has been frequently coming below the target resorts to a resource augmentation. There are two challenges here, one the move is short-sighted, and the organisation would incur substantial capex to set it up. The second major issue is that the existing inefficiencies are duplicated which pretty much eats into the margins. The expected progression should be around rolling out of a detailed root-cause analysis for identifying the bottlenecks across the value chain. Several organisations have put on their myopic lens and resorted to "stopgap" analysis to fix challenges, which in fact require a detailed analysis or a strategic shift in the problem-solving frameworks.
 
The third challenge is around the lack of communication between the line of businesses and the IT departments. Though the situation has allayed in the past few years, there are still visible silos that prevent a holistic data integration drive.
 
The last but not the least challenge is the trade-off between retrofits vs greenfield approaches. Some of the large manufacturers have production assets that are quite outdated and have no means of accessing the operational manuals. Installing sensors to capture run-time parameters can be arduous and would require further rationalisation drives with respect to core manufacturing processes which might disrupt existing operations resulting in substantial margin losses. The other option is around decommissioning assets, which also can be quite burdensome to an already cash strapped industry.
 
Is the manufacturing sector, especially the SMEs, constrained by the paucity of system integrators?
The integration layer remains crucial to the overall success of the modernisation or digital transformation drive. Within the ecosystem, organisations have the option of working directly with the independent software vendors (ISVs) or their channel partners. The introduction of partner certifications and levels have managed to address the domain and product gaps significantly. But knowledge around best practices and domestic industry limitations would be gained only through prior implementations around use cases in a same or similar industry. Manufacturers resort to outsourced models to reduce their TCO and the model would work best if the partners can understand the industry and the organisation to bring in aggregated benefits. Several CIOs have indicated that once a technology implementation is outsourced, they do not expect any of their internal resources to spend substantial amounts of time or effort towards the implementation drive. Lack of domain expertise and operational knowledge tend to offset the perceived advantages or benefits.
 
Experts believe lack of skills is one of the main reasons for low adoption. How true is this?
Lack of internal skills is true for the sustenance and future innovations, which is an incremental effort that is expected to contribute significantly to the organisational growth aspirations. Presence of digital skills internally can reduce the hesitance towards digital initiative rollouts or investments. Once an IoT implementation is completed, organisations need ‘digital engineers’ who have a strong knowledge of shop floor processes along with the ability to analyse the asset data for creating contextualised insights. Quite often, the lack of these convergent skills, more commonly known as the IT-OT (information technology-operational technology) can limit the extent to which industrial IoT related benefits could be realised.
 
In the Indian context, IIoT should actually resonate more with the solutions for brownfield plants, yet the response is slow. How can the manufacturing sector overcome these hurdles and arrive at a holistic approach?
Indian manufacturing organisations do lag their global peers in terms of industrial automation primarily because of lack of compelling use cases that are associated with significant monetary gains. The primary reasons are around the level of process reengineering efforts that are required to make the technology implementations worthwhile. With the existing productivity gaps, trying to modernise or attempt a technology infrastructure upgrade can be painstakingly slow and expensive.
 
Having said, investing in a greenfield is not always viable and would need substantial Capex. The solution could be in the form of creating a virtual greenfield within a brownfield with focus on measurable performance indicators that can make the investment decision more objective in nature.
•    Run gap analysis: Organisations should benchmark their existing processes and understand the gaps that have a large influence on the productivity metrics.
•    Map the gaps: Identify the process and technology dependencies for each of the gaps and create a matrix with associated priority levels which are determined by the current investment appetite, impact on shop floor efficiencies and product quality.
•    Reengineer your processes: Create or modify the existing processes to account for the losses or leakages while aligning underlying links to ensure a near seamless data stream that would act as tracers for future root cause investigations drives.
•    Implement automation: Data capture, storage and analysis should be automated to reduce subsequent errors, bearing in mind that the captured data would be the baseline on which future operations will be designed or refined.
•    Focus on integrations: Majority of the post-pilot projects fail because of an exception that gets triggered by a disjoined data link. A key step during the pilots should be an exercise around integrations that look at identifying all the data sets and sources that have a direct or an indirect impact on the shop floor productivity.
 
The above steps would aid in mitigating the DX challenges but in no way eliminate all the roadblocks towards a successful deployment. Digital initiatives need to be constantly monitored and managed while running continuous process improvements to innovate and stay relevant.
 
Sampath Kumar Venkataswamy is a senior Research Manager for IDC Asia/Pacific Manufacturing Insights based in Bangalore. He is responsible for research and analyses of key trends, best practices, and applications for manufacturing operations technologies. He is also responsible for delivering custom research that assists clients in making key technology investment decisions, advisory and competitive landscape for the next wave of manufacturing value chains. Mr Venkataswamy has over 13 years of manufacturing and consulting experience in TPM, six-sigma, supply chain planning and execution across the manufacturing space. He is also a co-holder of 2 engineering patents and prior to joining IDC, he worked as a supply chain and manufacturing operations consultant with ITC Infotech and was the chassis engineering lead for Tier-4 locomotives at GE Transportation.