AI-DRIVEN EARLY WARNING SIGNAL / PREDICTIVE ANALYTICS TOOL

 Our Consumer Goods, Retail, Pharma  clients were  facing the following challenges - 

•What impact will seasonal changes have on the  purchase volume?  

•How will pricing affect the sales of new product  and product segments?  

•What are some key metrics that will help identify  Early Warning Signals for the sales?  

•What is the impact of external macroeconomic  indicators on the sales as well as inventory  levels? 

We collected data from multiple sources (external  and internal, public and private, competitors, paid  and unpaid) to identify KPIs which would be  relevant to predict the early warning signals which  are directly linked to sales 

• Data Extraction Tool  

• Data Warehousing  

• Predictive Analytics Tool 

• Stock-outs and Overstocks improved by ~15%  

• EWS helped save over 1,000 hours per month for the  forecasting team 

Our E-commerce, Logistics, Manufacturing,  Automotive and Supply Chain  clients were facing  the following challenges -  

•How to improve logistical efficiency using optimal  resources?  

•How to reduce delivery time by using the best  possible route? 

 •How to use minimum number of vehicles to  deliver maximum efficiency?  

•How to scale same-day delivery order volume  without losing unit profitability? 

We built an ML-powered platform and API service  that calculates the best route for delivering packages considering real-time factors like traffic,  fleet size, delivery vehicle type and time window. 

• Reduced errors from manual intervention in order  management process saved 12% in ops costs.  

• Our fleet optimization solution cut down fuel costs  and delivery vehicles consumption by 20%.  

• Reduced errors from manual intervention in order  management process saved 12% in ops costs.  

• Our fleet optimization solution cut down fuel costs  and delivery vehicles consumption by 20%.  

 Our E-commerce and Retail clients were facing the  following challenges with their customers - 

 •How to find right product while searching on  their website/app?  

•How to get the market match on quality & price?  

•How to improve the current rule-based ‘mapping  and matching’ process to improve searchability?  

•How can we assist first timers in getting the right  accessories, just-in items & sales items?  

•How to get the multi-lingual support to improve  our Scandinavian sales? 

We built an AI model to automate the end-to-end  process of mapping & matching the client’s products  with the competitors. We designed multi-lingual AI &  ML models for language support & predictive models. 

• Individual & Competitors Pricing AI Model  

•Multi-lingual AI platform  

• Real time Computer Vision Tool  

• AI-driven Product Catalog Match 

•High accuracy search resulted in 8% revenue growth  

• Automated enrichment of product attributes  

• Improved employee efficiency by over 10%  

Our E-commerce and Retail clients were facing the  following challenges with their customers -  

•How to sort the customer complaints into key  categories with high accuracy?  

•How to structure and automate the pre determined rules without losing accuracy?  •How to gain a consolidated view of all complaints  to take educated decisions?  •How to resolve disputes faster and improve CSAT  scores? 

 We built an GenAI solution to digitize the end-to-end  process of categorizing the customer complaints and  tag the right vendor. We designed GenAI LLM to  rephrase/correct/complete the complaints. We  processed ambiguities and balanced seasonal data. 

• Straight through processing using GenAI Model  

•GenAI LLMs to categorize, direct & resolve disputes 

• 80% reduction in complaint processing time  

• 92% accuracy in categorizing the complaints  

•Over 30% increase in dispute resolution 

 Our Consumer Goods and Retail clients were facing  the following challenges with their customers -  

•How to ensure the new items & upgraded /  updated items match the item master?  •How to effectively use AI to match the vendor  generated and company generated item masters  every time a new product category is added?  

•How to use AI to do auto-checks on mis classification of items and lack of business  validation? 

We built AI-enabled ‘human in the loop’ system to  validate item master attributes based on predefined  rules for enrichment, labelling & publishing. 

 • Item Master Enrichment using AI Model  

• AI LLMs for business level validation  

• Integration of enriched items with internal  applications used for merch planning & allocation 

 • Automation of over 1M items review & audit  

• 98% accuracy on item classification (20%  improvement)  

Our E-commerce, Pharma and Retail/CPG clients  were facing the following challenges -  

•How to understand the customer's sentiment  towards specific products?  

•Which products to place prominently in shelves  for increased sales?  

•Which product categories do customers spend  the highest and lowest on?  

•How do customers react to events like discounts,  coupons?  

•How do companies monitize based on customer  behavior patterns? 

We built cross-platform Consumer Insights suite that  collects and analyzes customer behavior data from  various sources like chatbots, online surveys, in person interactions and video streams. 

•Cross platform Chatbots 

•Multi-platform Voice-bots  

• Real time video stream analytics platform  

•NLP driven sentiment analytics tool 

•CSAT increased by 10%  

• Tangible consumer insights led to 8% revenue  growth  

• Actionable data for improving customer experience 

 Our Ecommerce, Pharma and Retail/CPG clients  were facing the following challenges -  

•Client struggled to balance inventory for  thousands of SKUs across warehouses  

• Stockouts of viral products  

•Overstock of slower-moving items  

• Reverse logistics cost  •Huge transportation cost  

• Stock freshness 

Client deployed an AI-powered demand forecasting  platform integrating machine learning and predictive  analytics. It analyzed historic sales, forecast, stock at  warehouse and transit to predict product movement. 

• Daily Stock Transfer Plan  

• FIFO Stock Out  

• Truck Load Plan  

• Dispatch plan 

• Zero Reverse logistics  

• 99% Stock Availability  

• 99% Truck occupancy  

• 83% Stock Freshness in the market 

 A Tech CPG Client required a robust, digital solution  to streamline and centralize its RFx (RFI/RFQ)  management and procurement processes, which  previously suffered  • from manual effort,  

• fragmented communications,  

• inconsistent version control, and  

• limited traceability  

• The lack of transparency and standardized  workflows made collaboration difficult across  engineering, sourcing, and vendor teams, causing  delays and missed opportunities for cost  optimization and strategic sourcing. 

  AI-powered analytics to rank bids using historical data, cost  breakdowns, and predefined metrics to identify optimal  quotes and negotiate better terms. Portal for Global Sourcing  teams, vendors and engineering teams 

• A Predictive Vendor Scoring using ML algorithms  assessing vendor reliability and past performance  trends  

• recommending preferred suppliers and flagging risk  areas. AI generated smart procurement reports 

 • 30% Reduction in Procurement Cycle Time  

• 25% Improved Cost Savings  

• 40% Lower Manual Errors  

• Increased Transparency & Auditability  

A CPG Client required Data scientists and analysts to  analyse the demand forecast and related supply  data. Identify  

1. Anomalies  

2. Patterns and  

3. Trends.  

Need to come up with corrective actions and  suggestions. 

AI tool with various agents –  EDA agent – Analyse the data and identify the anomalies.  

Fix the gaps and clean data.

Hypothesis agent – Do deep analysis on the data and  generate hypothesis.  Narrator agent – Prepare visual analysis with description.

Executor agent – To write its own python code for any of  the agents.  

 Detail report with key findings, visuals, corrective  actions and approach used for the analysis. 

 • Improved analysis accuracy  

• Reduced turn around time by 80%  

• Reduced human limitation dependency