NelsonHall recently had a briefing followed by a demo of Tech Mahindra’s latest offering in field service management: Yantr.ai, an AI and ML-based bolt-on solution that can sit on existing scheduling systems. Yantr.ai provides a control tower solution to field service management, focusing on delivering operational efficiency, enhanced productivity, and improved workflow control by leveraging advanced analytics, artificial intelligence, and machine learning models to enable real-time decision-making. With Yantr.ai at the center of all data points, it can provide end-to-end visibility of granular operations insights.

Launched in 2022, Yantr.ai is an offering under Tech Mahindra’s BPS business with BPaaS and SaaS options. Tech Mahindra has been focused on offering BPS solutions with a combination of platform enablement in niche areas as part of its growth strategy. NelsonHall expects to see further similar offerings from Tech Mahindra in the coming months.

Yantr.ai Overview

Yantr.ai focuses on industries such as telecom, utilities, retail, oil & gas, pharmaceutical, and other similar industries where there is a sizeable workforce in the field, and field service directly impacts the customer experience.

With today’s Amazon and Google-like experience, customer expectations evolve almost daily. However, while many innovation and technology interventions have happened, effective field service management remains a key challenge. Customers expect excellent product quality, effective communication, and seamless support; and field service management is vital to customer satisfaction.

Yantr.ai focuses on transforming field spend, improving CX, and improving field workforce productivity. It incorporates a digital twin and control tower for field services, and focuses on creating end-to-end visibility, data-driven decision-making, and scenario and capacity planning to provide actionable insights. Tech Mahindra claims to deliver benefits such as a 1-3% reduction in operational spend, up to 10% improvement in operational efficiency, and up to 15% improvement in customer experience.

Yantr.ai’s core modules are:

• Capacity planning: forecasts the demand trend by incorporating external factors such as weather and seasonality to empower planners to remain prepared and responsive
• Strategic planning: focuses on monthly and long-term (up to two years) demand optimization, identifying and addressing challenges such as unmet demands, skill shortages, demand reshaping, and permanent relocation
• Operational planning: focuses on demand optimization for the next 30 days, with daily demand, capacity planning, optimizations, and SLA visibility
• Jeopardy planning: offers real-time triage, jeopardy, and unscheduled work order management based on multiple dynamic factors and business rules
• Route planning and optimization: ensures all vehicles/trucks take optimized routes to save time and money, thus improving service efficiency
• Scenario modelling: simulation of distinct scenarios based on various economic and weather factors like wildfires, tornados, floods, etc.

Forecasting is supported by machine learning models covering ARIMA, exponential smoothing, Naïve, and neural network time series forecasting. Based on the data input and forecasting accuracy, Yantr.ai will dynamically select the best model for forecasting and plot different visualizations for actual demand, forecast demand, and variance between actual and forecast demand.

Client Cases

One example of the use of Yantr.ai for field service is for a major Australian telecom company where the benefits delivered by Tech Mahindra are:

• $15m to $20m savings to field operations spend
• ~4 % SLA improvement
• ~10 % to 15 % work being saved from external suppliers
• Truck roll reduced by 10%
• 40% FTE reduction.

The client’s business challenges were managing extensive field service operations, optimizing inefficient technician assignments, reducing response time, and improving customer satisfaction. The client also struggled with no real-time insights or dynamic scheduling capabilities. Tech Mahindra deployed Yantr.ai to optimize technician assignments, provide real-time job insights, and improve resource utilization. Dynamic scheduling capabilities were used to help the client adapt more quickly to unforeseen challenges.

Beyond field service, Yantr.ai has also been used by a major Australian media company to address route planning. In this case, the company’s challenges were improving customer satisfaction by meeting SLAs for newspaper/magazine distribution to retailers and end customers, and optimizing routing to help in vendor negotiation and cost savings. Tech Mahindra implemented Yantr.ai to build an optimal routing model to reduce cost and improve delivery SLAs. Key benefits delivered to the client were:

• 15%+ higher accuracy in route planning
• Improved SLAs and customer experience, along with reduced travel cost
• Ability to do scenario modeling/analysis, including demand changes
• Profitability analysis of routes and optimization of remote delivery points
• “Right location of depot” suggestions.

Yantr.ai also offers data ingestion through standard connectors/APIs and a custom data ingestion pipeline for ingesting from different formats, including CSV flat files. The tool has an orchestration system to manage complex workflows, which can be modified based on the client systems and processes’ requirements or updated as the project progresses and requirements evolve.

Tech Mahindra claims it is the first company to build a planning engine and control tower for enterprises in field services. The tool offers intelligent DSR (decision support recommendation), on-the-day risk mitigation, routing optimization, and other demand and capacity planning capabilities.

Implementation, Pricing & Key Benefits

Tech Mahindra offers modular functionality deployment options for Yantr.ai. The pricing mechanism is a one-time implementation cost and ongoing license costs based on the number of workflows, infrastructure deployed, and consulting support. In the BPaaS service, Tech Mahindra also provides managed end-to-end field service management support.

The typical time required to implement Yantr.ai is estimated to be 6 to 9 months for a mid-size organization.

Tech Mahindra categorizes the key benefits clients can get by deploying Yantr.ai with the 3Ps (people, planet, and profits):

• “People” benefits include an increase in workforce productivity, improvement in CSAT and NPS, and an increase in technician reskilling
• “Planet” benefits include reduced fuel consumption and carbon footprint and improved decision-making and situation planning during natural calamities or other emergencies.
• “Profits” include savings in operational spend, reduced field technician expenses, truck rolls, and workflow management-related expenses.

Tech Mahindra continues to invest in Yantr.ai, focusing on partnerships with ServiceNow, IFS, isMobile, and Celonis.

NelsonHall expects Tech Mahindra to leverage the technology and architecture behind Yantr.ai to increasingly deliver solutions beyond field services in areas such as logistics, fleet management, and demand planning. Tech Mahindra will likely focus on the telecom, utilities, and oil & gas sectors.