Businesses increasingly focus on brand loyalty and customer engagement as they move towards more customer-centric strategies. Besides end consumers, sellers also form a key customer base for eCommerce marketplaces. Bad customer experience can lead to negative brand perception and drive down NPS (Net Promoter Score). This study was to help an eCommerce Marketplace scientifically identify key experience areas that were leading to a drastic decline in Seller NPS scores across regions over eight months. This would help businesses focus on the most important levers, prioritize budgets and improve NPS. In this study, we modelled customer survey data on brand perceptions and experiences, with historical transactional data and company policy information, to determine drivers of NPS. It was important to understand the inter-relations among various aspects of customer data, such as brand perceptions, issue resolution, customer service and macro factors, to provide holistic and actionable recommendations. We used a path model approach with a Bayesian network to determine causality between the key factors driving NPS and the interrelation among each factor. Structural Equation Modeling followed this to quantify the relative impact of each factor on NPS. The study found that sudden policy changes without effective communication, poor customer service and lack of protection for sellers from false buyer claims led to a poor selling experience. Sellers perceived the site as unfair to them and unsafe to sell. These insights were actioned upon by the company in an organization-wide cross-team initiative which helped address the seller’s dissatisfaction and eventually brought NPS back on an upward path.
Audi 3
Today’s data centres have thousands of servers that belong to multiple vendors, and they vary vividly in terms of technology, model, generation and numerous peripherals like firmware, software, and other hardware components. It is a challenge for the data centre administrators to track activities being executed across these servers and the resource consumption at various stages of deployment, delivery, installation, and upgrades. This leads to a subsequent ripple where the data centre administrators find it tough to foresee GPU availability and/or consumption in their data centres. This poses a risk when they need resources to be available for critical activity in the future, but because of an overburdened data centre, the resources are unavailable, and the critical tasks fail. On the other hand, if the data centre always remains underutilized, the unused servers in the data centre become a liability and bring the ROI down. This not only leads to unmitigable risks but also has a monetary impact on the profitability of the organization. This also leads to poor resource planning because of the lack of visibility of future needs. This paper focuses on designing a forecasting solution for data centre admins. The solution includes performing data explorations and developing enough understanding of the various metrics of the servers in the data centre like CPU, IO, Power etc., and developing univariate/multivariate forecasting solutions using the time series data sets of these metrics. This paper proposes to explore TDM as well as GAM models for predicting behaviors of various factors of data centres and CSPs. This solution would assist the data centre admins to have clearer visibility of the future load/availability of the GPU in their data centres. By having so, they would be in a better state to understand the utilization metrics for their data centres and better financial planning for future investments based on the clear visibility of under/over-utilized resources. This will also lead to better data centre resource optimization and rationalization.
A brain tumor can be deadly and may cause many severe issues and even death if not diagnosed and treated at the early stages. So, early detection of brain tumors is of prime importance. Meningioma, Glioma, and Pituitary tumors are the most common, and 73% (1) of brain tumors are diagnosed as one of these types. Brain MRI image is one of the crucial methods to diagnose a brain tumor. Manual reading of MRIs could be time-consuming, and interpretation may vary based on the reader’s expertise. So an AI-based automated computer-aided diagnosis (CAD) can help identify and classify various brain tumors. The extensive potentiality of AI has successfully solved innumerable complex problems in medicine. However, the lack of transparency persists due to the increased complexity of advanced Deep Learning models. The black-box nature of the models complicates the decisions of AI applications in clinical use. Thus to perceive the underlying high-stake decision-making processes and to make the models interpretable, explainable AI (XAI) can be used. This study has proposed a classification model based on EfficientNet- B5 with attention-based weighted Global Average Pooling (GAP) to classify three brain tumors. It also demonstrated the use of Explainable AI to visualize the affected region or area of interest identified by our black-box model. Transfer Learning has been used, and specific layers of pre-trained EfficientNet- B5 have been fine-tuned with an attention-based GAP layer. The proposed model achieved 93.73% validation accuracy in multi-class classification, which was 2% higher than the EfficientNet B5+GAP without the Attention layer. We have used Grad CAM to implement the visual interpretability of our classification model. Our model achieved a macro-F1 score of .94 on the validation dataset, indicating very high macro-Recall and macro-Precision. Still, if there’s any case of false positive or false negative, that can be identified by looking into the visual interpretation of the model predictions. The proposed method outperformed many similar studies and effectively explained the region of interest identified by our black-box model using Explainable AI.
From Una in Himachal Pradesh to Tuticorin in Tamil Nadu and Mundra in Gujarat to Guwahati in Assam, the 15,113 km based massive network of pipelines in IndianOil is engaged in the safe and sustainable transport of crude oil, petroleum products and natural gas. Apart from the natural vulnerabilities, including corrosion and leakages, the pipelines are also subject to various intrusions by miscreants in an attempt to steal products. Each of these vandalism attempts could account for damages in crores. Apart from economic losses, any pilferage-led spill endangers human lives and the ecological viability of the surroundings, along with threatening the energy security of the nation. Catering to the prevention needs of pipeline integrity against pilferages, a machine learning-based model, the Pipeline Vulnerability Management System (PVMS), has been built. PVMS is an ensemble-based classification model that identifies certain stretches of pipelines to be more vulnerable (Red Zones) to pilferage attacks than the rest. The limited security personnel can be optimally deployed in the Red Zones to prevent pilferage events. The solution assures extensibility to various other industrial applications (Heatmapping Conveyer belt maintenance, logistic supply chains in FMCG), and further research can help enhance assessment at a more granular level.
Query Autocompletion (QAC) is a common feature for input text-based applications where a user’s partially typed prefix input is completed. It has primarily been studied for applications involving search-based queries that are short sequences or phrases. We present a novel approach to QAC for a Question Answering system in an Augmented Analytics platform where queries are essentially business and analytical questions in natural language. Our approach involves a combination of semantic search and natural language generation via beam search for completing questions. To enable generative completion in natural language and handle unseen prefixes, we use a pretrained distilgpt2 model that is fine-tuned for question completion tasks. In addition, we describe a method to synthesize training data from limited available past queries for fine-tuning the model and generating quality results for completion. We evaluate the proposed method using two datasets from different business domains based on fluency, style of text and business sense of the application with perplexity and precision. Experimental results demonstrate that our framework is effective and generalizes well on unseen prefixes with acceptable latency using suitable optimizations.
Inventory or stocks are the goods held by any business organisation for the purpose of consumption, production, or sale. These are the non-capitalized assets of the organisation. Inventory can be classified into four categories – finished goods, raw materials, work-in-progress inventory and spare parts inventory (also called as plant maintenance, repairs and operations (MRO) inventory). Spare parts inventory is leveraged in the running and maintenance of the plants or the manufacturing units. In general, inventory management is the process of balancing the inventory thereby – having the right amount of inventory in the right place at the right time. Though it looks easy, it is one of the most challenging problems that industries deal with. In this paper, we will discuss what are the different challenges organisations face during inventory management and how we solve them using artificial intelligence (AI). We have built an AI-enabled dynamic inventory management application across all the inventory categories. Dynamic inventory for finished goods application optimises the inventory using stock, order, product, location and historical sales data across the business. The application can accurately forecast demand to get the right level of inventory, in the right locations, to meet service-level targets. Similarly, dynamic inventory for raw materials application applies AI models to inventory, sales, bill of materials, supplier, work-in-progress and finished goods data from across your business – so you can optimise raw material inventory levels to meet demand and reduce costs. The dynamic inventory for spare parts inventory management application applies AI models to plant data like location and capacity, and inventory data like stores, spares and consumables across the business. We predict demand across stores, spares, and consumables on a granular level. Then, we use forecasted demand to optimise inventory across each plant which will save cost while meeting the required service levels. We will also show how businesses can achieve a good ROI by deriving value from our AI-based inventory management applications using some use-case examples.
In information extraction, not only extracting the entities but also identifying the relationships between the entities can lead to better hidden insights. Triples are used to represent the relationships between clinical entities. Triples are made up of two concepts and a relationship between them:
Lookalike audience generation is an effective way to increase the audience base in online advertising. Segregating the lookalike audience into multiple priority levels gives greater flexibility to the advertiser in selecting their user reach. This paper explains a novel approach to lookalike audience generation in multiple priority levels on large-scale data with millions of users and thousands of audience segments. An automated system combining custom models to generate similar audience segments and group lookalike audiences into priority levels using Spark Scala and the Hadoop ecosystem is developed. The experimental results comparing different approaches show that our proposed model outperforms others in reach, scalability, and speed.
Traditional numerical methods of classification of both univariate and multivariate time series have shown to be challenging due to their problematic features, including the inability to preserve temporal correlation, lack of pre-trained models, difficulty in training Timeseries models and finally, Timeseries tend to act incorrectly when presented with multiple input. Decades have shown immense importance in applying deep learning methods to image and video data to solve real-world issues. The authors are motivated by recent accomplishments of supervised learning approaches in computer vision, so they have explored various time series image encoding techniques to enable machines to visually identify, classify, and learn structures and patterns by leveraging state-of-the-art Deep learning and computer vision. The Authors demonstrated rapid spot groupings of series with specific features. Later, these series were classified and subjected to comparison analysis using multiple cutting-edge deep neural networks to their maximum performance potential. Finally, the comparison results were demonstrated.
Retail manufacturers are grappling to predict customer demand for each product at superior forecast accuracy levels with the ability to refresh the forecasts at weekly intervals due to computational expenses. The overall cost of demand forecasting is determined by adding product shortage cost (loss of missed customer demand), excess product cost (inventory holding costs), product pilferage cost (expired goods cost), and computational cost (involved in forecast models training). While the first three cost components can be attributed to the forecast accuracy levels, the computational cost lever can be controlled through computationally light forecasting systems. The motivation of this research is driven by very limited empirical evidence on the adoption of unsupervised techniques to identify groups of products with similar levels, trends, and seasonality patterns for the development of computationally light forecasting systems. This study aims to decrease the forecast model training cycles by leveraging unsupervised techniques like K-means Clustering and Hierarchical Clustering to identify clusters of products with similar customer purchasing behavior. This research introduces a Clustering-based Demand Forecasting Framework that determines the best forecasting algorithm for each cluster. The experimental approach utilized this framework to predict the customer demand for more than 500 dairy products for the next 8 weeks. A comparative study on computational time across product-level model training and cluster-level model training is presented for better realization of relaxation in computational costs. The Weighted Accuracy Percentage Error (WAPE) based product level forecast accuracy from the Clustering-based Demand Forecasting Framework was found to be superior to the Standard Demand Forecasting Framework. Further, this research provides better interpretability of clusters by providing a product category-specific name to each cluster through Natural Language Processing (NLP) techniques. Holistically, this research effort provides empirical evidence to retail manufacturers for enterprise-wide adoption of the computationally light short-term forecasting system.
