91���� is on a journey towards Sustainability 2.0, an agenda that reinvents sustainability under the compelling challenges of climate change and social inequity. This new agenda is driven by a remarkable combination of thought and action on 91����’s part with meaningful public-private-people partnerships. The Sustainability Report 2.0 is available .

The Need for a Sustainability Data Analytics Platform

Research by states that 86% of business leaders have invested in sustainable practices to protect their organizations from disruptions. However, reporting on sustainability initiatives and finding various data types is difficult for all relevant parties to access. In order to drive sustainability performance management, the data analytics platform is vital.

Why is Sustainability Data Analytics Platform Necessary?

• Unable to detect human errors during data collection – Leveraging AI-driven document processing methods
• Unable to trace and audit data – Ensuring end-to-end traceability by customizing system logs as user-friendly. Approvers can easily interpret and make approval decisions with sustainability data reviews
• Time-consuming while aggregating data – Introducing the power of data aggregating capabilities by customizing industry-standard data management tools
• Inconvenience while accessing sustainability reports – Enabling self-serving capability with ideal role-based access control for different stakeholders
• Difficulty in getting the approval of reported sustainability data – Leveraging integrated workflow for end-to-end automation of processes ranging from data preparation to approval
• Proactive methods needed for meeting sustainability targets – Introducing machine learning models with the help of predictive analytics tools for preventive actions to meet the defined sustainability goals
• No single source of truth for sustainability reports – Introducing a self-service platform for accessing sustainability artifacts

How Will the Sustainability Data Analytics Platform Help?

• Integrated platform with low environment code to address the above-mentioned pain points
• Single source of truth for stakeholders for complete transparency of sustainability reports
• Secured access to data across stakeholders, data providers, reviewers, sustainability officers, and external auditors
• Automated workflow for end-to-end data management and reporting
• Complete traceability of data preparation, reviews, corrections, and approvals
• Complex calculations to arrive the critical sustainability measures across environmental footprints – energy, emission, water, and waste
• Prebuilt data model to reduce the time of implementation
• Various connectors to extract data from scanned documents, PDFs, enterprise resource planning, and excel sheets

How is the 91���� Sustainability Data Analytics Platform different from other platforms?

Here are the key differentiators that make 91���� a strong player among others:

• Single Version of Truth
• Pre-built data model suitable for manufacturing and consumer product companies
• Automation of data consistency, accuracy, and completeness verification in the data collection process
• Inbuilt predictive insights for corrective actions to meet sustainability targets
• Role-Based Access to Data
• Accessible roll-out features across businesses
• Configuration options for third-party audits
• Full Stack Solution ensures data collection till sustainability reporting and analytics
• Easy and convenient to deploy across different business units and facilities
• The functional view of each of the components is depicted below:

The 91���� Sustainability Data Analytics Platform is based on cloud technology, favouring the resource-efficient use of IT resources, and enabling every company’s flexible and expandable growth. With integrated solutions, companies can introduce sustainability goals more confidently and cleanly into their daily activities and move strategically toward building more resilient, sustainable businesses.

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For decades the industry has been unable to interact directly with the customer, having to rely on its retail partners to provide insights into customer demands and buying habits. Now, it can use 91����’s digital Platforms of Intelligence to gain direct visibility into customers (profiles, buying behavior, propensities and demands). This spells an end to futile trade promotion spends of which 70%, frustratingly, go straight to the retailer’s coffers.

The traditional lack of customer insights has many downsides.

These include field sales representatives being unable to take the right decisions and frequent out of stock (OOS) situations that lead to lost sales opportunities. This scenario results in alienating customers who then take their business to competition. 91����’s Platforms of Intelligence for the CPG industry changes this. It provides valuable customer insights using supplier, retailer, customer, ERP, CRM, trade promotion and 3rd party data. The insights generated by the data can be applied to decisions around channel management, predicting demand, SKU and assortment management based on customer location/ price points, and help determine which retailers to work with. The outcome: Improvement in sales productivity, reduction in OOS, better demand forecast, and significant trade savings.

Compelling evidence available to support the effectiveness of the intelligent platform.

Your CPG business can benefit from ITC’s Platform of Intelligence if you need to:

• Address value-conscious customers and develop new products to suit their preferences
• Leverage new buying patterns such as subscription-based choices
• Understand why a customer prefers a rival brand
• Create agile and intelligent supply chain
• Create alignment between trade and supply chain
• Improve margins
• Improve the outcomes from trade promotion spends

An End-To-End Integrated Platform To Deliver Unmatched Intelligence.

91����’s solution delivers unmatched intelligence using an analytical engine for segmentation, predictive and optimization modeling, pre-built AI/ML models to improve marketing effectiveness (and for faster implementation), and modular plug and play technology components to augment your existing IT ecosystem. Our cloud-based, scalable and secure platform comes with data visualization for self-service reporting and an intelligent virtual assistant to support your field force with guided selling and greater consumer engagement. And, in keeping with our commitment to deliver excellent value, the platform is available in a low-risk outcome-based opex model. The platform represents a large step forward for the CPG industry that has been challenged for decades in understanding its customers and leveraging the insights to improve sales, margins and brand salience. Click here to know more about our solution.

Author:

Venkatesh Agarwal
Vice President, Digital Experience,
91����

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Today’s retail customers are spoilt for choice and are being drawn in by never-before convenience. As a result, customer loyalty is on the decline. Retailers have to constantly invest in product innovation, promos, expensive engagement and margin-eroding incentives— despite this, customer loyalty eludes most retailers. In the US, loyalty is severely fragmented. On an average, customers belong to 14.8 retail loyalty programs and are active in 6.7. The challenge before retail CMOs is to create meaningful offers that generate incremental repeat business. But CMOs don’t have adequate customer insights to create personalized and effective one-on-one interactions that are the bedrock for emotional connects with customers. This is why their loyalty programs show poor ROI. Now, technology has a powerful solution. ROI can be improved by leveraging Platform of Intelligence that accelerate customer centricity and drive loyalty.

A Platform of Intelligence for Retail CMOs��

It works to integrate siloed data across category and customer management and the plan, execute and monitor functions of retail trade promotion and sales. It uses analytics, algorithms, Artificial Intelligence and Machine Learning to create customer insights. These allow the platform to answer critical questions like “When, what and why does Customer X buy?” The platform then matches the customer insights to its library of personas and spews out next best actions. Using this, the marketing team can confidently take informed decisions in real time that result in increased customer loyalty and growing share of wallet.

Delivered Business Value for Retailers

Your retail business can benefit from 91����’s Platform of Intelligence if you have:

• Limited line of vision across touch points
• Suboptimal customer engagement across apps, websites and social media
• Distributed data across platforms��
• Long conversion cycles that impact profit

An End-to-End Integrated Platform for Marketing Leaders

The 91���� solution combines analytics, real time recommendations, real-time campaign automation and even a scalable, intelligent virtual assistant that creates outstanding customer engagement. As ancillary benefits, retailers can leverage the data generated by the platform to predict demand, prevent lost sales and manage inventory levels with precision. Plus, the solution comes in a risk-free, outcome-linked, zero-capex model. In effect, the platform delivers hassle-free loyalty management. Click here to know more about our solution.

Reference:

Author:

Amit Tulshiram Derkar
Senior Manager, DX Offerings & GTM,
91����

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Intelligent manufacturing, leveraging advanced technologies for end-to-end digitization and automation could be the answer. Let us delve deeper.

Siloed Data Ecosystems, Lack of Real-time Visibility and Reactive Approach Hinder Efficiency

A large number of manufacturing units run on aging equipment causing 45% unscheduled downtime. Operations are disrupted in factories as asset health is not regularly monitored. Maintenance of equipment becomes a major challenge and 80% of the time production managers and factory staff are fixing issues after the damage is done. The ability to proactively apprehend and resolve incidents is limited, leading one in three manufacturers to spend more than 10% of their operating budget on maintenance alone. At the enterprise level visibility on plant health, operating processes, and floor level performance is hindered with siloed data and access to actionable reports, on time. This creates an environment of vulnerability, posing challenges in meeting safety and regulatory compliance requirements. The end result is sub-optimal productivity and sub-par performance impacting the bottom line.

Intelligence Powered Digital Next Manufacturing Accelerates Growth and Amplify Outcomes

The manufacturing sector has the opportunity to reinvent their production and supply chain, augment the workforce, and drive deeper engagement with their customers with advanced technology adoption. The ability to harmonize, analyze, and drive real-time insights from multiple data streams define the strength of a digital enterprise. No wonder, more than 50% of manufacturers are keen to invest in building Big Data capabilities. The need for real-time inline quality control and visibility into the end-to-end process is driving 80% of technology leaders in the sector to increase investments in digitization, Artificial Intelligence, and Advanced Analytics.

Cognitive supply chain and operations with AI-based advanced planning and automation improves the ability to pre-empt issues, sense the operational challenges to respond with agility and speed. This removes vulnerability from systems and improves the health of assets by over 40%, saving huge maintenance costs and reducing unplanned downtime by almost 50%.

Companies improve the bottom line with a 5-10% reduction in operating cost and increased earnings by up to 10%. The advantages are obvious, the question is how do manufacturers accelerate their journey to digital next future?

Roadmap to Digital Next Manufacturing��

As manufacturers embrace emerging technologies the focus must remain on creating a definitive roadmap to achieve desired goals. Here is an incremental approach, that equips technology leaders to chart a sustained path to digital enablement:

Roadmap for Digital Next Intelligent Manufacturing

Connect Plant and Enterprise – A��strong data foundation built on IT-OT convergence and integration across multiple data streams forms the bedrock of a connected organization. Implementing gateway solutions that provide multi-device support and build a machine to machine (M2M) connectivity enable real-time visibility, optimize capacity, and improve the quality of the output.

Conduct Prescriptive and Predictive Analytics –��Once the organizations gain the ability to harness data, they are enabled to leverage AI, ML, and advanced analytics to build predictive models and interactive dashboards. Real-time data management and AI/ML algorithms provide prescriptive analytics to improve overall equipment efficiency and reduce inventory. As they decrease the lead time for procurement there are substantial cost savings, equipping business leaders to optimize their budget.

Drive Cognitive Intelligence��– As manufacturers operationalize self-tuning AI/ML algorithms at the enterprise level, they gain the ability to contextualize intelligence. Processes are automated with a digital feedback loop and systems are equipped to take decisions with scripted algorithms. These advanced cognitive capabilities pave way for a responsive and flexible value chain and proactive risk management.

Augment Intelligence on Edge��–��Building on cognitive technologies, CIOs can adopt edge computing and advanced AI/ML modeling to connect products and services. The strength of their ML algorithms will create an ecosystem for autonomous decision making to keep the assets performing at peak. Augmented and Virtual Reality (AR/VR) adoption can bring them closer to floor level operations and create opportunities for product innovation. The technology prowess ultimately translates to enhanced productivity and efficiency, leading to improved margins.

Manufacturing Platforms of Intelligence

Under our , we offer Factory, Supply Chain, and Product Intelligence to equip manufacturers to build digital next manufacturing organizations that are cost-efficient, high performing, and more profitable. We can work with you to explore the use cases and assess the readiness with us through a discovery workshop and demonstrate value with a pilot MVP for a use case in 3 months.

91���� has been supporting manufacturers to gain streamline operations and gain a competitive advantage with Platforms of Intelligence. Our pedigree in CPG and manufacturing comes from our expertise and experience with our parent group ITC. We bring the advantages of Cloud, Big Data, Advanced Analytics, AI/ML, and Augmented Reality on a single platform, we accelerate the journey fully connected ecosystem. We leverage our various strategic partners like Microsoft, PTC ThingWorx, Anaplan, and Salesforce.

To know more, write to us.

Authors:

Amit Tulshiram Derkar
Senior Manager, DX Offerings & GTM

References

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Adopt ML-based optimal booking strategy to maximize the yield and revenues

Overbooking flights is a common practice in the aviation industry based on the expectation that some fraction of the booked passengers will either cancel their booking at the eleventh hour or fail to show up at the departure gate (commonly known as no-show). Accurate forecasts of the number of no-show passengers for each flight can help the airline to accrue more revenue by simultaneously reducing the number of spoiled seats and lowering the cases of involuntary denied boarding which produce significant cost penalty. Optimal booking policies seek to maximize the yield as a tradeoff between the revenue due to additional sales offset by the cost of any denied boarding that might happen. Flights flying fully occupied are not only of economic interest but are of ecological importance as well.

Implementation of relevant statistical/machine learning based algorithms may ensure an advanced and robust prediction of the no-show numbers for each scheduled flight, which can aid the airlines company to accept optimum number of additional bookings over the inventory capacity. The solution comprised of two steps – prediction of cabin-level no show rates using specific information on the individual passengers booked on each flight and later employing these predicted no-show rates along with features extracted from historically similar flights to arrive at the final no-show estimate.

Build a robust prediction framework to identify no-shows

At the beginning, individual passenger details from historical flights were utilized to capture their booking trend and understand their travel pattern. Different classification models were employed to capture the different bias-variance structures and then ensembled together to come up with a more robust prediction framework. Identification of individual learners that will eventually lead to lower misclassification is the key. Below is the mis classification table.

Here B2 are the cases where a passenger predicted as flown have actually flown. A1 are the cases where one predicted as no-show didn’t turn up. The passengers who have been predicted to be flown passenger but in reality didn’t turn up are A2. B1 are the passengers who have been predicted to not turn up but eventually did. Though both A2 and B1 cases are misclassified, an increased number of B1 cases will result into higher instances of denied boarding which will escalate the cost in the form of penalty.

At the next step some generalized linear model concepts were employed to come up with the estimated no show numbers. A GLM model for the unique combination of i^th Flight Market Departure Date is of the form:

g(y_i) = f(x_i) + ε_i

where g() is the appropriate link function applied on the response variable, that would help to model it as a linear function of the explanatory variables. In this case, f() is a linear function and ε is the corresponding error vector.

While experts will agree that individual passenger information for a particular flight is of immense importance, airlines usually accept bookings till few hours before departure, and hence any model based on the same data will be too volatile, and always be based out of incomplete data. Rather, we assume that the pattern of travelers in a particular Flight- Market would be similar in certain Days of Week. So, we utilize the cabin level no show rate from the first model as an auxiliary variable for the passenger level information in a GLM (Generalized Linear Model).�� We used GLM over other because of following reasons

• The response variable is a count variable (more than 0 but less than a certain number, which may vary from Market to Market)
• The variance structure of the error will guide on the selection of the appropriate link function
• The response variable might also be taken to be of type ratio (rate of no show) and may guide to selection of a totally different link function

A GLM yielding lower RMSE (Root Mean Square Error) values is considered as an appropriate prediction equation.

The General workflow can be summarized by the below diagram:

Improved inventory visibility and intelligent network planning for airlines

Some benefits reaped by the Airlines those have implemented Optimal Overbooking Strategy are:

• Reduction of spoiled seats as well as the number of involuntarily denied boarding that not only maximized the revenue of the airlines due to additional bookings but also curtailed additional cost due to compensation due to DNBs and gave a boost to the reputation of the company
• Accurate prediction teamed with historical booking pattern helped the client in network planning and inventory management

How scenario changes post pandemic

COVID-19 has changed how industries operate and Aviation is no exception. Most of the airlines worldwide had been grounded and very few are operating across limited routes. Some Indian airlines have started their domestic and international operations, but the number of flights deployed is just a fraction of their pre-pandemic frequency. Center for Aviation (CAPA) India has indeed stated that, “Indian Aviation Sector may lose $4 billion in FY 21.”* Currently, with limited transportation options, airlines is the only viable mode of transportation as people stranded in places away from home has an urge to return to near and dear ones.

In such trying situations having an Optimal Booking Policy at place can truly help sustain airlines. Though prediction of overbooking becomes more difficult in such an unprecedented situation, the airlines deems it to be even more important to have a robust and accurate demand forecast along with an Overbooking Strategy. Smart deployment of inventory along with the decision to set the Optimum Booking Limit, might help in the yield management more than ever.

New consideration of factors like instances of relaxation or implementation of sudden lockdown due to increasing cases of Corona outbreak at respective cities are important as this may result into cancellation of tickets for some passengers due to lack of transportation facilities giving room for overbooking.

Though one might say that the future of aviation looks bleak in current scenario, with the right changes applied at the right time to the existing model, we have the potential to maximize the revenue of an airline today and in the future.

We are building capabilities like these using AI/ML for optimal overbooking strategy in our platform of intelligence for airlines. Our platform can help airlines accelerate the value realization – 2% incremental PLF, 4% savings in fuel 3% upside in crew utilization and 2% improvement in TAT – using airline intelligence platform.

Stay tuned to know more about for .

Authors:

Devangana Dasgupta
Data Scientist, DATA

Akshay Vijay Medhane
Junior Data Scientist, DATA

Anindya Neogi
GM, Chief Data Scientist, Digital Experience

References

• “Passenger Based Predictive Modelling of Airline No Show Rates” – Richard D. Lawrence, Se June Hong, Jacques Cherrier
• “Forecasting and Forecast Combination in Airline Revenue Management Applications” – Christian Lemke, Bogdan Gabrys
• “Cancellation Predictor for Revenue Management applied in the Hospitality Industry” – R. van Leeuwen
• Categorical Data Analysis, Second Edition – Andre Agresti

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Platform-enabled transformations

Customer life cycle management is increasingly moving towards the cloud which integrates various functions and provides a 360-degree view of customer financials, including channel transactions, account opening and closing, default, fraud and customer attrition. Contact & Lead Management and Loan/Deposit Workflows are going completely paperless and contactless without the need for customers to visit branches. Advanced Analytics is providing customers with instant information on products along with pricing recommendations/optimization. It also provides the bank the vital ability to cross-sell/up-sell, create customer churn predictions and improve performance.

New trends like incentivizing retail customers for deposit/loan referrals are picking up. Various personalized offers and promotions are being made to customers through the use of Loyalty Points. Mobile Apps are being developed for Loyalty Management and Loyalty Points Redemption. Offer Management, Campaign Planning and Audience Management are integrated with Cloud-Based Architectures.

Social media and sentiment analysis provide actionable insights and quantifiable predictions about customer behavior. It also assesses brand awareness and can measure the success/failure of campaigns. Unstructured data analytics incorporates information from online discussion forums, social networks and call scripts to determine customer sentiment or market opportunities.

Insights about customer behaviors like shopping patterns, lifestyle, eating habits, credit history, spending habits etc. can be uncovered through multivariate descriptive analytics, as well as through predictive analytics. It can help improve a bank’s ability to segment, target, acquire and retain customers. Additionally, improvements in risk management and understanding the customers enable digital next banks to maintain and grow a more profitable customer base.

Risk analytics for credit risks, market risks and operational risks such as digital credit assessment, advanced early-warning systems, next-generation stress testing, credit-collection analytics etc., are performed by applying risk indicators to large datasets to detect risks that would otherwise remain hidden.

• Natural-language analytics are used to understand questions, context and semantics and analyze terabytes of data to identify and rank likely answers
• Algorithms are created to measure their own accuracy and feed that information back into the model to create self-improving predictive analysis
• Real-time analysis of data sources, such as financial markets, stock exchanges, or news is performed to gain risk insights

Leading use cases for banks

Kick-starting an effective use case

Implementing platforms of intelligence across the bank can be a daunting and potentially expensive prospect due to:

• Complex, heterogeneous technology architectures
• Operationally optimized but siloed processes and systems
• Data fragmented across multiple databases
• Constrained investment budgets with competing agendas
• Lack of skilled resources
• Perception that the available data lacks the quality to support analysis

All of these are genuine obstacles. However, it should not be assumed that analytical insight cannot be extracted until all the obstacles have been overcome. That road leads either to major programs striving to create perfect data that can answer any question or to an acceptance that any such effort is futile. Organizations do not necessarily have to solve all these issues to initiate an intelligent platform project and put it on the path to success.

A more pragmatic approach starts with selecting a critical question or objective, identifying the necessary data and recognizing that the data is not perfect. This allows the business to derive answers and information correlations with a corresponding confidence level. This approach does not replace the strategic architecture investment required to reach accuracy, but it provides a framework for business owners to control the level of their expenditures in a way that is proportionate to the benefit unlocked.

AI/ML based platforms of intelligence is the way forward for digital next banks across the world which can transform how banks analyze data, breaking down siloed decision making and improving overall customer experience. It can enable banks to respond quickly to disruption, propel the business forward and outmaneuver competitors in the market.

Author:

Abhiram Shankara
Lead Consultant, DATA

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With the aging segment (>65 years) expected to be ~686 million (11.8% of the total population) by 2023, and health care spending that continues to rise (~6% every year) there is an absolute need for an efficient ecosystem with the ability to consolidate millions of patient records and integrate various healthcare functions, to ensure optimum clinical decision support, patient experience, and value-based care enablement with transparency and lower cost.

91���� has the domain and data expertise, in addition to strategic tech partnerships, that are critical to healthcare organizations in order to make the transition from traditional to the transformed way of delivering business. We do this through a 4-pronged approach:

a. Technology enablement

The current ISVs have various on-the-fly solutions along with customization options to cater to the Healthcare Industry. Salesforce is one such sophisticated healthcare CRM. It weaves together multiple sources of data such as consumer and patient demographics, psychographics, social, behavioral, clinical, financial, website, call center, and provider credentialing, to provide comprehensive insights and behavior of providers and payers. Salesforce, with a customized vertical Health Cloud and its sales and service modules, enables healthcare organizations to facilitate outstanding care services with a simplified interactive user experience and multi-channel support. It enables intelligent persona-driven profile management coupled with a rich User Experience and a 360-degree view of patients to provide a one-stop shop for schedulers/agents.

b. Enhanced engagement �� �� �� �� �� �� �� ��

i. Patients

In healthcare, providers have been collecting and analyzing data for longer than most industries. To administer the correct treatment, doctors and other medical professionals need to have access to detailed patient history as well as other relevant patient data. As part of the tech transformation, 91���� enables improved communication (both internally and externally with patients), enhanced service, optimized team collaborations, and advanced data analysis. The integrated ecosystem provides an enhanced interface for medical professionals to view patient information and prioritize time-sensitive actions. Agents can also segment patient records for more targeted communication, thereby providing relevant patient experiences at the right moments.

ii. Providers

Engaging with patients on any device and collaborating across the entire healthcare network, from care coordinators to service agents and family members, is an absolute must. A well-implemented solution makes the life of providers easier by enabling access to patient data and creating connects with their patients on all engagements. A 360-degree view of the patient history including care plans, timelines, clinical and nonclinical data records, are made available to physicians in a matter of a few clicks. This ensures providers can deliver patient-centric care while streamlining operations.

iii. Payers

Healthcare payers face a highly competitive market where members and providers expect connected and personalized experiences. As healthcare costs shift to members, payers must respond to the growing demand for better services or risk lower enrolment. In the transformed state, payers will be able to create a 360-degree view of policyholders, making it easier for service and sales representatives to categorize and approach every policy holder uniquely and efficiently. By creating a unified ecosystem, payers will be able to optimize agent workflows, overcome challenges with poor visibility and data tracking, streamline authorization requests with utilization management, and promote collaboration, to achieve business outcomes

c. Artificial Intelligence (AI) and Machine Learning (ML) Driven Full Digital Business Capability (Platforms of Intelligence)

In the current scenario, AI applications using data-driven ML, Deep Learning, and Reinforcement Learning play a crucial role. For example, for providers and payers, AI can provide appropriate training resources, anticipate issues based on patient or member history, and guide call center agents with the right response to an inquiry and next-best-action recommendations. Predictive and prescriptive analytics can identify at-risk populations and recommend next-best actions for timely interventions. Einstein Analytics for Healthcare lets care coordinators, utilization managers, and referral managers gain critical insights and visualize key metrics about their patient populations. Payers and providers can use actionable insights from the app’s dashboards to drive intelligent engagement and improve care effectiveness and efficiency

The Healthcare Industry uses predictive and prescriptive analytics in different ways to improve operations, quality and to minimize risk. ��ML has proven to be irreplaceable in identifying risk factors, predicting risk scores, and treatment outcomes. Our ‘Platform of intelligence’ solution leverages the power of data science and scientific algorithms to accelerate and automate the complex analytical use-cases as follows:

A statistical/ML modeling framework for patient no-show and an engagement model is depicted in the figure below. The framework provides a robust view of necessary ML model development life-cycle stages right from defining the business problem, quantifying the same into equivalent analytical form, deep data mining and exploratory data analysis, algorithm selection, model development, validation, monitoring, and production of the model.

d. Success delivered

One of our clients, a leading US-based healthcare provider, had major challenges in managing provider, employees, and patient data across multiple systems and applications. Additionally, low visibility on contact center agents’ productivity and application adoption was affecting the leadership team’s ability to take in-time decisions.

91���� delivered a unified data model-based Salesforce CRM solution with a personalized experience to solve the problem. The solution was developed with Community cloud and Patient 360 view to improve user experience and patient care services by several degrees. Analytical data-driven decision-making dashboards and reports were used to boost agent efficiency. Salesforce, MDM, and real-time integrations supported synchronized application services and data accuracy. The solution enabled multi-channel capabilities to reduce call waiting and mute time during patient care services.

For healthcare organizations that want an IT partner who can walk the talk to deliver personalized experiences and improved operational efficiency while ensuring competitive advantage, reach out to us ��at contact.us@itcinfotech.com

Author:

Praveen Ramachandra
Salesforce – General Manager and Principal Architect, Customer Experience

Avinash Sethi
Lead Consultant, DATA

Lakshmi Repakula
Sr. Project Manager, Customer Experience

Ramapavan. S
Sr. Project Manager, Customer Experience

Karthik Matam
Lead Consultant, Customer Experience

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In today’s context COVID-19 is the invisible enemy that has changed the world. Almost every aspect of human life has been hit hard by the virus. Businesses in different sectors have shut down as people are being forced to pause their normal life and stay indoors to avoid getting infected.

OECD (Organization of Economic Co-operation and Development) in its interim has forecasted that global economic growth will be just 2.4% this year, down from 2.9% in 2019. The worst hit industries are Aviation, Travel, Hospitality and Tourism. Other industries such as Manufacturing, Food, Education, Media, Energy, etc. have suffered major setbacks as well. This sudden confinement of human resources has changed how business is done. We now need innovation at an unprecedented pace to counter the slump.

Even as the pandemic spreads, many economies are re-opening for business. They cannot afford to let economic activity grind to a complete halt. This increases the need for innovative solutions that also ensure the safety and well-being of employees. What is required are solutions that harness modern tools and technology and can stand the test of time. The solutions also need to be efficient and agile so that they can be applied to different sets of problems arising from the spread of the pandemic.

Build a data-driven solution using Location Intelligence (LI) to optimize resource allocation and safety

Here we will discuss and showcase a unique data-driven algorithmic solution that helps connect and communicate with various resources in different geographies. The solution has helped a client by optimizing resource allocation based on geo-spatial dimensions like location through spatial co-ordinates, distance metrics and real time data about the spread of COVID-19 along with key business metrics.

Before we discuss the solution, it would help to understand the context. Imagine that you own a consumer goods business and have agencies that conduct market research on your behalf. Now, due to the pandemic lockdowns are forcing you to shut down your major operations.

With unpredictable and inconsistent lockdowns and the phased opening of markets, it becomes a business priority to study the impact of this on consumer behavior and understand the new needs and demands. This, in turn, helps create new products. The foremost question would be:

what should the ideal marketing mix be? The wrong answer could lead to a loss of consumers and a perpetual loss of revenue.

The answer lies in a massive data collection exercise from various touchpoints across the operational regions, collating them, analyzing them and then arriving at decisions. But how do you mobilize resources to conduct the market research while keeping researchers safe in the field? How do you guide sales without the knowledge of areas that are open for business and those that are not on any specific day?

Adopt adaptive data science models for scalability and faster decision making

The solution is to integrate data from various sources, both in-house and third party, process the data via Adaptive Data Science models to enable autonomous scalability and construct various business intelligence (BI) layers as per the need of stakeholders. This enables easy consumption of data and expedites decision making at every level of engagement.

In the world of BI, it is said that intelligent planning and execution requires intelligent and informed knowledge about one’s resources. At 91���� we have engineered a solution aimed at fighting the unseen enemy. It provides customers with deeper, pandemic-related visibility for each of its assets. The model has 2 layers that interact with each other and enable accurate decision making.

The solution delivers a hierarchical resource allocation model in different geographies with the shortest path between the assets and containment zone status of their location as the primary constraints. It is a Heuristic Mathematical Pattern Search that utilizes live GPS information of the resources and allocates the nearest resources to each other in areas with no active cases of COVID-19. An automated data mining engine retrieves live COVID-19 information from our 3rd party vendor using API endpoints and sends it across with other relevant information about our assets for allocation. Information such as GPS location and the reporting hierarchy of the assets is sent to the Machine Learning Model, which allocates the resources in the form of a Local Network for areas with no active cases of COVID-19.

Mobilize resources using real-time LI of safe zones

The schematic diagram below depicts resource mobilization based on real time information on COVID-19 collected through APIs and spatial coordinates for resources across hierarchies. Tier 5 (T5), being the governing body, controls the other tier members with updated information in order to enable quicker mobilization of resources through informed decisions. Sufficient provisions have also been made so that the assets at each level can interact with each other, thus enabling efficient exception handling. The result is a tree-like structure where each asset at a particular level is connected to another asset on a different level with the shortest geographical path that has no active COVID-19 cases.

Build asset LI dashboard for an integrated view

Once the solution’s analytical structure was developed, the next target was to enable visualization of the solution for different stakeholders. To accomplish this we used Power BI as the primary visualization tool. Microsoft Azure SQL was used as the managed database service to develop the dashboards. This helped achieve the desired scalability, availability and uptime. The dashboards showcased COVID-19 related information for each asset’s location, including the zone color, containment status and also their operational status (active or closed). A snapshot of the visualization is provided below.

Modular solution for faster deployment

Keeping the urgency and need in mind, the team was able to integrate, automate and deploy the solution within a few weeks. This minimized the impact of the lockdown on operations as each day of delay resulted in lost revenue. The critical piece of information around which the solution revolves is the real time geo-coordinates of the assets and their operational readiness status (active or not) post the lockdown. To facilitate this, the team created a mobile application which captured and recorded the latitude – longitude and status of each asset.

The model was further re-engineered so that it could be adopted as a stand-alone solution or could be integrated as a module to a larger solution. At its core, the models in the solution use the principles of the LI which can enable or expedite decision making in businesses across different verticals and solve various problems.

Location Intelligence as a Service

LI is the methodology for transforming your location data (spatial coordinated and features) into business outcomes. LI empowers you to leverage the vast repositories of data built over the years through new methodologies that enrich business data with location, help visualize the result, and merge traditional temporal analytics with spatial analytics. LI has layers of�� Enrichment,�� Analysis,�� Visualization�� and Application.

The industries that can benefit from LI are CPG, Financial Services, Healthcare & Pharma, Logistics, Marketing & Advertising, Retail, Telecom, Transportation, Utilities and even state and local government bodies. The solution may be a component of the broader Fleet Planning, Site Selection, Geo-Marketing, Fleet Management and Supply Chain Optimization solutions.

91���� is using LI for several CPG clients as an aid to overcoming the COVID-19 situation and improving their operational efficiency, employee safety as well as the ability to adhere to government regulations and guidelines in near real-time. For more details, connect with us on how your business can leverage LI to restore operational workflows during these trying times.

Author:
1. Indranil Seal
�� �� Associate Data Scientist

2. Prateek Bansal
Lead Consultant, Business Consulting

3. Divya Chawla
Lead Consultant, DATA

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