Interview with Sandeep Raghuwanshi

Sandeep Raghuwanshi
Sandeep Raghuwanshi
CEO & Founder
ESG Robo
ESG Robo

The disruption has brought the faultlines to the open
ESG Robo is a start-up that implements IoT and blockchain solutions for fashion brands. It implements IoT-based sensors that assess production efficiency and work conditions for workers. The data analytics and strategic advice provided allow manufacturers and brands to address some of today's most pressing challenges in the integration of sustainability information into practice and unearthing opportunities. Sandeep Raghuwanshi, CEO and founder of ESG Robo, talks tech.

Tell us more about how IoT and blockchain solutions can help textile/apparel companies in today's dismal situation?

COVID-19 has completely frozen the supply chain for the textiles and garment industry across the world. This industry is as global as it gets and the impact on the industry is obviously very high. This business disruption has brought the main faultlines of the apparel industry in stark view which needs to be addressed both in short term and long term.

First, even during business as usual, factories compete for orders globally and need to be able to respond quickly to customer demands while maximising their productivity and efficiency. This requires close attention to every aspect of the assembly line and eliminate all the inefficiencies and bottlenecks. For most factories, the amount of time the needle runs is only 25-30 per cent of total productive time available. After adjusting for allowance of various other activities of work, there is still a significant amount of productive time that is lost resulting in low profitability.

Second, the fashion brands and retailers are under constant spotlight by various human rights groups regarding the work conditions at their supplier facilities. To provide comfort to the clients, factories undergo certifications, audits and provide data records. It is a complicated process due to lack of standardisation and different brands have different requirements. Furthermore, they receive disclosure requests from various other stakeholders also including social groups and industry groups. All of these are expensive both from a time and resources perspective.

We have studied these issues in detail and have developed a targeted solution to address the highest impact problems at the garment factory floor.

The first step for efficiency improvement is automated, granular and reliable measurement of every minute of activity of each machine on the production floor by deploying sensors at each work desk. These sensors capture details of machine's availability, needle running status and reason codes for lost time. Additional data capture mechanisms are provided to add needle breakage and other machine breakdown events. Besides these, sensors also collect ambient environment data such as temperature, air quality, illumination, noise levels, etc. The data is then transmitted to a central server where it is accumulated and prepared for analysis. Factory managers and supervisors get access to interactive reports and dashboards that help them identify incremental or radical improvement opportunities. With detailed history being available, managers can get auto updated latest operator skill matrix, man-machine status for line setting planning and other critical details for efficient production planning. This detailed analysis allows to achieve an increased throughput of 20-25 per cent easily, and since the profitability of the factory operations are highly dependent on the production efficiency, this will have a multiplier effect on profitability of the orders.

To address the concerns of brands and other stakeholders, factories can easily share selected data using blockchain smart contracts. Trust in the data integrity is established by blockchain and smart contracts allow rapid and easy sharing of data while maintaining strict control and ownership.

By combination of IoT, cloud and blockchain, the factories can solve highest impact challenges that directly uplift profitability and enable them to win and retain clients.

Since the backbone of India's (as also global) industry are countless MSMEs, how expensive would these solutions be?

MSMEs in general face many challenges in technology adoption and these go beyond just the initial deployment costs of sensors. Companies lack enabling IT infrastructure, skilled resources and have various data security concerns. For the apparel industry in particular, the companies have additional concerns of uncertain order pipeline and thin profit margins. 

To address these concerns, ESG Robo has created a model that eliminates all upfront investment requirements from factories. We invest in the entire data infrastructure and factories can just plug into our ecosystem and start deriving benefits. An MSME can get started as a partner by starting for as little as a few dozen machines of a single line. Our installation engineers would visit and will set up the system within a few hours without any disruption to the ongoing work. There is no integration requirement with the company's IT system and the sensors will form their own network. Our engineers will configure user apps and accounts and after a very short training, the system will be ready for use. 

The companies need to pay for each desk on a very affordable pay-as-you-go model. They can easily scale up when they feel ready to utilise the full power of data from all desks being connected.

Have you come up with custom solutions for the present scenario?

We have developed our solution keeping in mind the relevant sustainability challenges of the global fashion industry. Over the last 20 years, Fast Fashion has led to a rapid growth of the industry. On the one hand it has helped create tens of millions of jobs and growth of export-oriented factories. On the other hand, there are concerns being raised by environmental and social groups on the vast footprint of the industry on both counts. While the social organisations have been doing a great service by bringing these issues to light, what is generally missing from the discourse are the solutions. Our effort is to solve the Achilles heel of the value chain by addressing the root of the problem and making all the stakeholders as part of the solution.

Could you share a case study wherein a factory/company has started reaping benefits?

While we are at an initial stage of adoption, there is a large body of research and studies that point to the benefits of scientific efficiency measure methods. A study conducted by Fair Wear Foundation titled Productivity: The Key to Funding Living Wages highlights one of the primary causes of lower wages are inefficient factory operations. A study conducted by USAID on Cambodia titled Measuring Competitiveness And Labour Productivity In Cambodia's Garment Industry highlights that in spite of much lower wages, factories of Portugal perform better in overall production cost and Mexico, Brazil and Turkey are comparable. The study identified primary factors to be inefficient operation of machines, lack of time measurement, weak management information systems and poor training. Similarly, there are various studies conducted on large scale all pointing to significant profitability and wage rise potential from removing the inefficiencies of garment factories. Several industries employing batch processing methods such as food & beverage, pharmaceuticals, cosmetics and others have achieved efficiency improvements in excess of 20 per cent over already well managed operations. 

So far, the garment manufacturing industry has not been able to benefit from the knowledge and parallels in other industries because of a primary difference. The cost of a sewing machine in a garment factory is on average less than $1,000 per unit compared with machinery in other industries which are much higher. This makes most of the systems available out of reach for the industry.
A portion of this interview was first published in the June 2020 edition of the print magazine.
Published on: 07/07/2020

DISCLAIMER: All views and opinions expressed in this column are solely of the interviewee, and they do not reflect in any way the opinion of

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