- Paper is on AI solution used to optimize workload in multiple sales support centers of IBM across the globe. Presented in IIM Bangalore's prestigious supply chain conference.
- Overall 3000+ sales support professionals work in these sales support centers. AI solution was able to make plan and schedule daily work more effective for these 3000+ professionals.
- What do you think can be dollar benefits from this AI solution which can drive more effective daily workforce scheduling for 3000+ sales support employees?
IRJET- Credit Profile of E-Commerce CustomerIRJET Journal
This document proposes using RFM (Recency, Frequency, Monetary) variables and advanced k-means clustering to create positive and negative credit profiles for e-commerce customers. This will help minimize losses by identifying genuine versus fraudulent customers. The methodology calculates credit scores based on RFM and other factors. Advanced k-means clustering is then used to segment customers into clusters like excellent, good, average, and worst. Customers in different clusters will receive different benefits or restrictions based on their predicted reliability. The goal is to reduce losses from unwanted cancellations while retaining high value customers.
This document discusses predicting loan defaults through machine learning models. It begins by introducing the business problem of banks suffering losses from customer loan defaults. It then describes preprocessing the loan dataset, which includes handling missing data, label encoding categorical variables, and balancing the dataset using SMOTE and SMOTEENN techniques. Logistic regression, decision trees, AdaBoost and random forest algorithms are applied to both the original and balanced datasets. The random forest model on the balanced data using SMOTEENN achieved the best accuracy of 92%. The model is then pickled and integrated into a web application using Flask for users to predict loan defaults.
Bank Customer Segmentation & Insurance Claim PredictionIRJET Journal
This document summarizes a research project that aims to help a bank segment their customers and help an insurance company predict insurance claims. The project uses data mining techniques like clustering and predictive modeling with machine learning algorithms. For the bank customer segmentation problem, the document describes applying hierarchical and k-means clustering on customer credit card usage data to identify customer segments. For the insurance claim prediction problem, the document outlines applying classification models like CART, random forest and artificial neural networks on historical claims data to predict future claims and compares their performance. The results from both problems can provide business insights like tailored promotional strategies for different customer segments and recommendations to reduce claim frequency and improve sales for the insurance company.
Quant Foundry Labs - Low Probability DefaultsDavidkerrkelly
The Quant Foundry Labs division was approached to improve models for predicting low probability sovereign defaults. They developed a machine learning model that uses a large dataset of economic, financial, and governance indicators to predict sovereign credit ratings. The model was trained and tested on historical data, demonstrating improved accuracy over traditional statistical techniques. Explanatory tools also provide transparency into the model's predictions. The results represent an improvement in predicting low probability default events, which can help with regulatory requirements and risk management.
This document contains questions from assignments for various Master of Business Administration courses at SMU, including Production and Operation Management, Financial Management, Marketing Management, Management Information Systems, Operations Research, and Project Management. The questions cover a range of topics within each subject area, such as production strategies, capital budgeting, marketing mix, information systems implementation, linear programming, and project scheduling. Students are directed to a website for answers to the questions.
Pricing Optimization using Machine LearningIRJET Journal
This document discusses using machine learning algorithms to optimize pricing. Specifically:
1. It reviews previous research applying machine learning to price prediction and optimization in various industries like e-commerce, real estate, and insurance. Methods discussed include linear regression, clustering, random forests, and integer linear programming.
2. It then introduces using machine learning like regression trees and random forests to forecast demand and maximize revenue by setting optimal prices. Variables like holidays, promotions, and inventory are considered.
3. The goal of the paper is to develop a pricing algorithm that can predict and optimize daily prices in response to changing demand using machine learning techniques. Outcomes will demonstrate machine learning's ability to optimize pricing.
The sales process at ECR Technologies lacked efficiency due to a lack of communication between salespeople and low conversion rates. An information system was created to analyze sales pipeline data and generate insights. The data showed some sectors like e-commerce had low demo success. Recommendations included developing case studies for financial services and offering early bird discounts. The sales pipeline data improved sales processes and helped forecast sales. Recommendations for the financial sector increased client acquisition.
TierPoint white paper_How_to_Position_Cloud_ROI_2015sllongo3
Traditional ROI calculators do an ineffective job of measuring the value of cloud services. This white paper serves as a guide to calculating cloud ROI using seven metrics you may not have considered.
IRJET- Credit Profile of E-Commerce CustomerIRJET Journal
This document proposes using RFM (Recency, Frequency, Monetary) variables and advanced k-means clustering to create positive and negative credit profiles for e-commerce customers. This will help minimize losses by identifying genuine versus fraudulent customers. The methodology calculates credit scores based on RFM and other factors. Advanced k-means clustering is then used to segment customers into clusters like excellent, good, average, and worst. Customers in different clusters will receive different benefits or restrictions based on their predicted reliability. The goal is to reduce losses from unwanted cancellations while retaining high value customers.
This document discusses predicting loan defaults through machine learning models. It begins by introducing the business problem of banks suffering losses from customer loan defaults. It then describes preprocessing the loan dataset, which includes handling missing data, label encoding categorical variables, and balancing the dataset using SMOTE and SMOTEENN techniques. Logistic regression, decision trees, AdaBoost and random forest algorithms are applied to both the original and balanced datasets. The random forest model on the balanced data using SMOTEENN achieved the best accuracy of 92%. The model is then pickled and integrated into a web application using Flask for users to predict loan defaults.
Bank Customer Segmentation & Insurance Claim PredictionIRJET Journal
This document summarizes a research project that aims to help a bank segment their customers and help an insurance company predict insurance claims. The project uses data mining techniques like clustering and predictive modeling with machine learning algorithms. For the bank customer segmentation problem, the document describes applying hierarchical and k-means clustering on customer credit card usage data to identify customer segments. For the insurance claim prediction problem, the document outlines applying classification models like CART, random forest and artificial neural networks on historical claims data to predict future claims and compares their performance. The results from both problems can provide business insights like tailored promotional strategies for different customer segments and recommendations to reduce claim frequency and improve sales for the insurance company.
Quant Foundry Labs - Low Probability DefaultsDavidkerrkelly
The Quant Foundry Labs division was approached to improve models for predicting low probability sovereign defaults. They developed a machine learning model that uses a large dataset of economic, financial, and governance indicators to predict sovereign credit ratings. The model was trained and tested on historical data, demonstrating improved accuracy over traditional statistical techniques. Explanatory tools also provide transparency into the model's predictions. The results represent an improvement in predicting low probability default events, which can help with regulatory requirements and risk management.
This document contains questions from assignments for various Master of Business Administration courses at SMU, including Production and Operation Management, Financial Management, Marketing Management, Management Information Systems, Operations Research, and Project Management. The questions cover a range of topics within each subject area, such as production strategies, capital budgeting, marketing mix, information systems implementation, linear programming, and project scheduling. Students are directed to a website for answers to the questions.
Pricing Optimization using Machine LearningIRJET Journal
This document discusses using machine learning algorithms to optimize pricing. Specifically:
1. It reviews previous research applying machine learning to price prediction and optimization in various industries like e-commerce, real estate, and insurance. Methods discussed include linear regression, clustering, random forests, and integer linear programming.
2. It then introduces using machine learning like regression trees and random forests to forecast demand and maximize revenue by setting optimal prices. Variables like holidays, promotions, and inventory are considered.
3. The goal of the paper is to develop a pricing algorithm that can predict and optimize daily prices in response to changing demand using machine learning techniques. Outcomes will demonstrate machine learning's ability to optimize pricing.
The sales process at ECR Technologies lacked efficiency due to a lack of communication between salespeople and low conversion rates. An information system was created to analyze sales pipeline data and generate insights. The data showed some sectors like e-commerce had low demo success. Recommendations included developing case studies for financial services and offering early bird discounts. The sales pipeline data improved sales processes and helped forecast sales. Recommendations for the financial sector increased client acquisition.
TierPoint white paper_How_to_Position_Cloud_ROI_2015sllongo3
Traditional ROI calculators do an ineffective job of measuring the value of cloud services. This white paper serves as a guide to calculating cloud ROI using seven metrics you may not have considered.
Emerging Technologies - The Future Of Finance (CIMA Feb 2019)Michael Sadler
A presentation by IBM on the topic of "The Future Of Finance" examining emerging trends, and how accountants can to prepare for the transition from "running the numbers" to being value-adding partners to the business.
Imagine that you are Matt Chapman. Prepare a memo that recommends .docxwilcockiris
Imagine that you are Matt Chapman. Prepare a memo that recommends which strategy Workbrain should pursue and why.
Write a 5–7 page paper in which youdo the following
:
1. Discuss if the company even needs to raise money.
2. Discuss what other financing alternatives are available.
3. Discuss whether Workbrain should prepare for an IPO.
4. Determine if now is the right time for an IPO.
5. Determine which exchange would serve the company better (TSX or NASDAQ) and why.
6. Determine if the shareholders would be better off if the company pursued potential acquirers rather than an IPO.
Your assignment must follow these formatting requirements
:
•Typed, double -spaced, using Times New Roman font (size 12) with one - inch margins on all sides;
• Include a cover page containing the title of the assignment, the student’s name, the professor’s name, the course title, and the date. The cover page and the reference page are not included in the required page length.
The following are specific course learning outcomes associated with this assignment:
•
Evaluate exit strategies for new business ventures
.
•Use technology and information resources to research issues in new business
ventures and entrepreneurship
.
•Write clearly and concisely about new business ventures and entrepreneurship
using proper writing mechanics.
Grading for this assignment will be based on answer quality, logic/organization of the paper, and language and writing skills, using the following grading criteria:
1. Discuss if the company even needs to raise money.
a. Completely discussed if the company even need to raise money.
2. Discuss what other financing alternative
Completely discussed what other financing alternatives are available.
3. Discuss whether Workbrain should prepare for an IPO
a. Completely discussed whether Workbrain should prepare for an IPO.
4. Determine if now is the right time for an IPO.
a. Completely determined if now is the right timefor an IPO.
5. Determine which exchange would serve the company better (TSX or NASDAQ) and why
a. Completely determined which exchange would serve the company better (TSX or NASDAQ) and why
6. Determine if the shareholders would be better off if the company pursued potential acquirers rather than an IPO.
a. Completely determined if the shareholders would be better off if the company pursued potential acquirers rather than an IPO
.
MBALN-736A CASE STUDY for Final Examination
New York City Cabbies Strike Over New Information System
New York City's Taxi & Limousine Commission has mandated that alt licensed city cab owners
install new information systems in their cabs. The new state-of-the-art system connects the cabs
to a wireless data network. The new system will not be used for dispatching cabs (most New York
City cabs are hailed from the curb) but will provide text messages informing cabbies of nearby
opportunities. It includes global positioning system technology that provides an interactive map
that.
Final Semester project on Leveraging Data Analysis for Sales Department using prescriptive and predictive analytics. Predictive analytics using Neural Network and Logistic Regression in R language.
Presentation on "A Complete Overview of Data Driven Decision Making in a Quickly Changing Business Environment" given by Isaac Aidoo, Head of Data Analytics, Zoona.
Case study-Strategic Evaluation for Launching SMS Channel on Mailchimp.pdfSubrat Kumar Dash
How Intuit can use SMS solution of Mailchimp to provide A2P messageing to SMB players and add another reveune stream.
In summary, the strategic evaluation presents a compelling opportunity to launch an SMS channel on Mailchimp, aligned with the company's mission and Intuit's purpose in acquiring Mailchimp. By addressing customer pain points and leveraging QuickBooks' customer base, the solution aims to enhance customer engagement, streamline financial processes, and drive revenue growth.
This document summarizes a research paper that predicts customer churn using logistic regression with regularization and optimization techniques. The paper applies these techniques to predict churn customers in the banking, e-commerce, and telecom sectors. It first discusses customer relationship management (CRM) and how data mining can be used for customer churn prediction. Then, it describes logistic regression and how the proposed method adds regularization and optimization to improve accuracy. The method is tested on datasets from the three sectors to classify customers as churners or non-churners. The paper finds that adding regularization and optimization to logistic regression enhances its performance in customer churn prediction.
ANNUAL REPORT ANALYSIS WITH ADVANCED LANGUAGE MODELS: A STOCK INVESTMENT STRA...IRJET Journal
This document presents a strategy to enhance stock investment decisions by using large language models (LLMs) to analyze annual reports of public companies. The strategy involves using an LLM to generate features from company annual reports, then using those features to train a machine learning model to predict stock returns. The model is tested on a random sample of 500 stocks, and is shown to outperform the S&P 500 index when selecting the top 5 predicted stocks each year. The strategy provides a promising way to leverage LLM abilities to glean insights from lengthy annual reports and potentially improve investment returns.
The document provides information about business analytics in different industries including business analytics, automotive analytics, FMCG analytics, and e-commerce analytics. It discusses key components of business analytics including data aggregation, data mining, association/sequence identification, and forecasting. For automotive analytics, it outlines use cases for predictive analytics, data from sensors for traffic and insurance, and cost/financial tracking. Top FMCG analytics uses cases include inventory optimization, forecast optimization, and price/promotion analytics. E-commerce analytics focuses on functions like supply chain management, merchant analytics, product analytics, online marketing, and user experience analytics.
This document provides information about obtaining fully solved assignments from an assignment help service. It lists their contact email and phone number and provides an example assignment for the subject of Business Intelligence & Tools. The assignment contains 6 multiple part questions covering topics like similarity measures, OLAP vs OLTP, data extraction techniques, BI strategy implementation, content management systems, and how a footwear company could implement and make best use of business intelligence solutions. Students are encouraged to contact the assignment help service by email or call for assistance with their assignments.
This document provides information about obtaining fully solved assignments from an assignment help service. It lists their contact email and phone number and provides an example of an assignment question from the subject of Business Intelligence & Tools. The assignment question covers topics like similarity measures, data extraction techniques, OLAP vs OLTP, content management systems, and how to plan and implement a business intelligence solution for a footwear company. Students are encouraged to email their assignment needs to the provided address or call in an emergency.
Six Sigma Green Belt Project_GautamSinghGautam Singh
• Applied DMAIC technique to support staffing team of Tesla motors, by reducing time to hire contingent workers
• Root cause analysis was done to identify the worker shortage in production team and later improvements methods were applied, which led to efficiency in hiring from 44.85-71.77%
• Tools used: Design: Charter, Pareto, CTQ; Measure: SIPOC, Fish bone/Ishikawa, Statistics, XY-diagram; Analyse: Root cause analysis, FEMA, Hypothesis; Improve: 5 Why’s, VSM; Control: SPC or control chart
IRJET- Performance Analysis of Store Inventory Management (SIM) an Enterp...IRJET Journal
This document summarizes a performance analysis of a Store Inventory Management (SIM) system developed using Java. The analysis measured response times for key operations under different data loads. Response times were recorded for a PC client using EJBs and a mobile client using REST. Transfer shipments and inventory adjustments performed better on the PC client, while transfer deliveries were faster on the mobile client. Certain operations like canceling shipments took more time than others. The analysis identified operations that could be optimized to improve performance. Future work includes analyzing additional functions and optimizing poorly performing operations.
This document provides information about getting fully solved assignments from an assignment help service. It includes contact information for the service via email or phone call, and provides an example of an assignment question from the subject of Business Intelligence and Tools. The assignment question asks students to define and explain similarity measures and methods for determining similarity between objects, as well as the differences between OLTP and OLAP systems. It then provides multiple additional questions on topics related to data extraction techniques, aspects of a business intelligence strategy, content management systems, end user segmentation, basic reporting and querying, and OLAP.
British Airways implemented a Teradata CRM (TCRM) solution on their integrated commercial warehouse (ICW) to improve efficiency and reduce costs of their customer relationship management. TCRM provided a single view of customers and allowed for faster, more integrated marketing campaigns. This led to reducing the CRM team from 8 to 3 people while increasing campaign volume by 150%. TCRM also allowed more campaigns to be outsourced and executed in-house, reducing marketing costs from external vendors. The transition to using TCRM took only 1 month compared to 2 years to transition to a previous Oracle solution, due to the simpler data structure of ICW optimized for analytics. Over 600 campaigns have now reached 4000-5000 customer segments using TCRM
The document summarizes a technical white paper produced jointly by SAP and IBM that describes a proof of concept project testing the performance of SAP Convergent Invoicing software handling large data volumes. The project used an IBM enterprise architecture and demonstrated meeting key performance indicators for a telecommunications scenario, including uploading 1.5 billion items, billing 2.5 million customers, and invoicing in under 18 hours. Using IBM Easy Tier storage software reduced the processing time by over 30% and IBM Storwize V7000 storage eliminated performance bottlenecks.
This technical white paper describes a project to test the performance of the SAP Convergent Invoicing package in the scenario of a company having 50 million active customers. To know more about IBM’s portfolio of products for big data, visit http://bit.ly/T0NnOs.
This document presents a framework for estimating the value of cloud computing compared to conventional IT solutions. The framework consists of four main steps: (1) defining the business scenario including objectives, demand behavior, and technical requirements; (2) calculating the costs of fulfilling the scenario using cloud computing; (3) calculating the costs of using a reference IT solution; and (4) comparing the total costs of both approaches to estimate the value of cloud computing for that scenario. The document also provides two examples that illustrate how real-world use cases can be analyzed using this framework.
This whitepaper discusses the use of analytics to help companies retain customers during and after mergers and acquisitions (M&As). It describes the challenges of customer retention due to synergies from M&As impacting customers. A Customer Cockpit solution is proposed using data integration, machine learning and dashboards to identify at-risk customers, understand customer experience, and monitor key performance indicators. The solution aims to help companies measure performance, predict churn, and take actions to retain top customers and those likely to defect during M&As.
STOCK PRICE PREDICTION AND RECOMMENDATION USINGMACHINE LEARNING TECHNIQUES AN...IRJET Journal
This document discusses using machine learning techniques and sentiment analysis of Twitter data to predict stock prices and recommend buying or selling stocks. It evaluates ARIMA, LSTM, and linear regression models for stock price prediction and uses TextBlob to analyze the sentiment of recent tweets about a company and provide recommendations based on the overall sentiment polarity. For Apple stock, ARIMA had the lowest RMSE of 3.54, while LSTM achieved an RMSE of 5.64 after 30 epochs. Sentiment analysis of Apple tweets found an overall positive polarity. The models were also tested on Yes Bank stock.
Automated Contract Compliance using RAG based LLM solution-AbstractJyotishko Biswas
(Please note: This is abstract only, paper submitted to IEEE publication)
- Paper on Generative AI powered application to solve chronic business problem for Fortune 500 firms.
- Large firms has million+ pages of supplier contracts which are impossible to audit. It results in non-compliance driving losses of billion+ dollars.
- Our Gen AI application identifying contracts which are non compliant in few minutes only!
Winners of prestigious Global leadership program of HPJyotishko Biswas
- Myself and 5 other professionals won this prestigious award out of 3000+ employees.
- Happens only once in 2 years
- Coaches employees to become senior executives
Emerging Technologies - The Future Of Finance (CIMA Feb 2019)Michael Sadler
A presentation by IBM on the topic of "The Future Of Finance" examining emerging trends, and how accountants can to prepare for the transition from "running the numbers" to being value-adding partners to the business.
Imagine that you are Matt Chapman. Prepare a memo that recommends .docxwilcockiris
Imagine that you are Matt Chapman. Prepare a memo that recommends which strategy Workbrain should pursue and why.
Write a 5–7 page paper in which youdo the following
:
1. Discuss if the company even needs to raise money.
2. Discuss what other financing alternatives are available.
3. Discuss whether Workbrain should prepare for an IPO.
4. Determine if now is the right time for an IPO.
5. Determine which exchange would serve the company better (TSX or NASDAQ) and why.
6. Determine if the shareholders would be better off if the company pursued potential acquirers rather than an IPO.
Your assignment must follow these formatting requirements
:
•Typed, double -spaced, using Times New Roman font (size 12) with one - inch margins on all sides;
• Include a cover page containing the title of the assignment, the student’s name, the professor’s name, the course title, and the date. The cover page and the reference page are not included in the required page length.
The following are specific course learning outcomes associated with this assignment:
•
Evaluate exit strategies for new business ventures
.
•Use technology and information resources to research issues in new business
ventures and entrepreneurship
.
•Write clearly and concisely about new business ventures and entrepreneurship
using proper writing mechanics.
Grading for this assignment will be based on answer quality, logic/organization of the paper, and language and writing skills, using the following grading criteria:
1. Discuss if the company even needs to raise money.
a. Completely discussed if the company even need to raise money.
2. Discuss what other financing alternative
Completely discussed what other financing alternatives are available.
3. Discuss whether Workbrain should prepare for an IPO
a. Completely discussed whether Workbrain should prepare for an IPO.
4. Determine if now is the right time for an IPO.
a. Completely determined if now is the right timefor an IPO.
5. Determine which exchange would serve the company better (TSX or NASDAQ) and why
a. Completely determined which exchange would serve the company better (TSX or NASDAQ) and why
6. Determine if the shareholders would be better off if the company pursued potential acquirers rather than an IPO.
a. Completely determined if the shareholders would be better off if the company pursued potential acquirers rather than an IPO
.
MBALN-736A CASE STUDY for Final Examination
New York City Cabbies Strike Over New Information System
New York City's Taxi & Limousine Commission has mandated that alt licensed city cab owners
install new information systems in their cabs. The new state-of-the-art system connects the cabs
to a wireless data network. The new system will not be used for dispatching cabs (most New York
City cabs are hailed from the curb) but will provide text messages informing cabbies of nearby
opportunities. It includes global positioning system technology that provides an interactive map
that.
Final Semester project on Leveraging Data Analysis for Sales Department using prescriptive and predictive analytics. Predictive analytics using Neural Network and Logistic Regression in R language.
Presentation on "A Complete Overview of Data Driven Decision Making in a Quickly Changing Business Environment" given by Isaac Aidoo, Head of Data Analytics, Zoona.
Case study-Strategic Evaluation for Launching SMS Channel on Mailchimp.pdfSubrat Kumar Dash
How Intuit can use SMS solution of Mailchimp to provide A2P messageing to SMB players and add another reveune stream.
In summary, the strategic evaluation presents a compelling opportunity to launch an SMS channel on Mailchimp, aligned with the company's mission and Intuit's purpose in acquiring Mailchimp. By addressing customer pain points and leveraging QuickBooks' customer base, the solution aims to enhance customer engagement, streamline financial processes, and drive revenue growth.
This document summarizes a research paper that predicts customer churn using logistic regression with regularization and optimization techniques. The paper applies these techniques to predict churn customers in the banking, e-commerce, and telecom sectors. It first discusses customer relationship management (CRM) and how data mining can be used for customer churn prediction. Then, it describes logistic regression and how the proposed method adds regularization and optimization to improve accuracy. The method is tested on datasets from the three sectors to classify customers as churners or non-churners. The paper finds that adding regularization and optimization to logistic regression enhances its performance in customer churn prediction.
ANNUAL REPORT ANALYSIS WITH ADVANCED LANGUAGE MODELS: A STOCK INVESTMENT STRA...IRJET Journal
This document presents a strategy to enhance stock investment decisions by using large language models (LLMs) to analyze annual reports of public companies. The strategy involves using an LLM to generate features from company annual reports, then using those features to train a machine learning model to predict stock returns. The model is tested on a random sample of 500 stocks, and is shown to outperform the S&P 500 index when selecting the top 5 predicted stocks each year. The strategy provides a promising way to leverage LLM abilities to glean insights from lengthy annual reports and potentially improve investment returns.
The document provides information about business analytics in different industries including business analytics, automotive analytics, FMCG analytics, and e-commerce analytics. It discusses key components of business analytics including data aggregation, data mining, association/sequence identification, and forecasting. For automotive analytics, it outlines use cases for predictive analytics, data from sensors for traffic and insurance, and cost/financial tracking. Top FMCG analytics uses cases include inventory optimization, forecast optimization, and price/promotion analytics. E-commerce analytics focuses on functions like supply chain management, merchant analytics, product analytics, online marketing, and user experience analytics.
This document provides information about obtaining fully solved assignments from an assignment help service. It lists their contact email and phone number and provides an example assignment for the subject of Business Intelligence & Tools. The assignment contains 6 multiple part questions covering topics like similarity measures, OLAP vs OLTP, data extraction techniques, BI strategy implementation, content management systems, and how a footwear company could implement and make best use of business intelligence solutions. Students are encouraged to contact the assignment help service by email or call for assistance with their assignments.
This document provides information about obtaining fully solved assignments from an assignment help service. It lists their contact email and phone number and provides an example of an assignment question from the subject of Business Intelligence & Tools. The assignment question covers topics like similarity measures, data extraction techniques, OLAP vs OLTP, content management systems, and how to plan and implement a business intelligence solution for a footwear company. Students are encouraged to email their assignment needs to the provided address or call in an emergency.
Six Sigma Green Belt Project_GautamSinghGautam Singh
• Applied DMAIC technique to support staffing team of Tesla motors, by reducing time to hire contingent workers
• Root cause analysis was done to identify the worker shortage in production team and later improvements methods were applied, which led to efficiency in hiring from 44.85-71.77%
• Tools used: Design: Charter, Pareto, CTQ; Measure: SIPOC, Fish bone/Ishikawa, Statistics, XY-diagram; Analyse: Root cause analysis, FEMA, Hypothesis; Improve: 5 Why’s, VSM; Control: SPC or control chart
IRJET- Performance Analysis of Store Inventory Management (SIM) an Enterp...IRJET Journal
This document summarizes a performance analysis of a Store Inventory Management (SIM) system developed using Java. The analysis measured response times for key operations under different data loads. Response times were recorded for a PC client using EJBs and a mobile client using REST. Transfer shipments and inventory adjustments performed better on the PC client, while transfer deliveries were faster on the mobile client. Certain operations like canceling shipments took more time than others. The analysis identified operations that could be optimized to improve performance. Future work includes analyzing additional functions and optimizing poorly performing operations.
This document provides information about getting fully solved assignments from an assignment help service. It includes contact information for the service via email or phone call, and provides an example of an assignment question from the subject of Business Intelligence and Tools. The assignment question asks students to define and explain similarity measures and methods for determining similarity between objects, as well as the differences between OLTP and OLAP systems. It then provides multiple additional questions on topics related to data extraction techniques, aspects of a business intelligence strategy, content management systems, end user segmentation, basic reporting and querying, and OLAP.
British Airways implemented a Teradata CRM (TCRM) solution on their integrated commercial warehouse (ICW) to improve efficiency and reduce costs of their customer relationship management. TCRM provided a single view of customers and allowed for faster, more integrated marketing campaigns. This led to reducing the CRM team from 8 to 3 people while increasing campaign volume by 150%. TCRM also allowed more campaigns to be outsourced and executed in-house, reducing marketing costs from external vendors. The transition to using TCRM took only 1 month compared to 2 years to transition to a previous Oracle solution, due to the simpler data structure of ICW optimized for analytics. Over 600 campaigns have now reached 4000-5000 customer segments using TCRM
The document summarizes a technical white paper produced jointly by SAP and IBM that describes a proof of concept project testing the performance of SAP Convergent Invoicing software handling large data volumes. The project used an IBM enterprise architecture and demonstrated meeting key performance indicators for a telecommunications scenario, including uploading 1.5 billion items, billing 2.5 million customers, and invoicing in under 18 hours. Using IBM Easy Tier storage software reduced the processing time by over 30% and IBM Storwize V7000 storage eliminated performance bottlenecks.
This technical white paper describes a project to test the performance of the SAP Convergent Invoicing package in the scenario of a company having 50 million active customers. To know more about IBM’s portfolio of products for big data, visit http://bit.ly/T0NnOs.
This document presents a framework for estimating the value of cloud computing compared to conventional IT solutions. The framework consists of four main steps: (1) defining the business scenario including objectives, demand behavior, and technical requirements; (2) calculating the costs of fulfilling the scenario using cloud computing; (3) calculating the costs of using a reference IT solution; and (4) comparing the total costs of both approaches to estimate the value of cloud computing for that scenario. The document also provides two examples that illustrate how real-world use cases can be analyzed using this framework.
This whitepaper discusses the use of analytics to help companies retain customers during and after mergers and acquisitions (M&As). It describes the challenges of customer retention due to synergies from M&As impacting customers. A Customer Cockpit solution is proposed using data integration, machine learning and dashboards to identify at-risk customers, understand customer experience, and monitor key performance indicators. The solution aims to help companies measure performance, predict churn, and take actions to retain top customers and those likely to defect during M&As.
STOCK PRICE PREDICTION AND RECOMMENDATION USINGMACHINE LEARNING TECHNIQUES AN...IRJET Journal
This document discusses using machine learning techniques and sentiment analysis of Twitter data to predict stock prices and recommend buying or selling stocks. It evaluates ARIMA, LSTM, and linear regression models for stock price prediction and uses TextBlob to analyze the sentiment of recent tweets about a company and provide recommendations based on the overall sentiment polarity. For Apple stock, ARIMA had the lowest RMSE of 3.54, while LSTM achieved an RMSE of 5.64 after 30 epochs. Sentiment analysis of Apple tweets found an overall positive polarity. The models were also tested on Yes Bank stock.
Automated Contract Compliance using RAG based LLM solution-AbstractJyotishko Biswas
(Please note: This is abstract only, paper submitted to IEEE publication)
- Paper on Generative AI powered application to solve chronic business problem for Fortune 500 firms.
- Large firms has million+ pages of supplier contracts which are impossible to audit. It results in non-compliance driving losses of billion+ dollars.
- Our Gen AI application identifying contracts which are non compliant in few minutes only!
Winners of prestigious Global leadership program of HPJyotishko Biswas
- Myself and 5 other professionals won this prestigious award out of 3000+ employees.
- Happens only once in 2 years
- Coaches employees to become senior executives
- Recognised for Contra transformation initiative, one of top 3 projects of Global CFO, leading HP's Finance of 3000 employees.
- Heart of the solution is an AI predictive engine which is expected to release 150-250M USD of cash. One of the largest dollar benefits achieved from AI project.
AI Lecture in University (JK Lakshmipat University, India)Jyotishko Biswas
- Discussed automated Contract Compliance solution built using Generative AI technologies. Objective was to explain students how multiple technologies are applied to solve real business problems in industry
- Let us know what you think about this solution? Do you think RAG can be used here? Can we create a customised vector database for contracts?
Recognised in quarterly HP Finance all employee meeting for leading global initiative to drive digital awareness in multiple sites across globe for HP’s Finance function of 3000+ employees.
This presentation by Yong Lim, Professor of Economic Law at Seoul National University School of Law, was made during the discussion “Artificial Intelligence, Data and Competition” held at the 143rd meeting of the OECD Competition Committee on 12 June 2024. More papers and presentations on the topic can be found at oe.cd/aicomp.
This presentation was uploaded with the author’s consent.
Suzanne Lagerweij - Influence Without Power - Why Empathy is Your Best Friend...Suzanne Lagerweij
This is a workshop about communication and collaboration. We will experience how we can analyze the reasons for resistance to change (exercise 1) and practice how to improve our conversation style and be more in control and effective in the way we communicate (exercise 2).
This session will use Dave Gray’s Empathy Mapping, Argyris’ Ladder of Inference and The Four Rs from Agile Conversations (Squirrel and Fredrick).
Abstract:
Let’s talk about powerful conversations! We all know how to lead a constructive conversation, right? Then why is it so difficult to have those conversations with people at work, especially those in powerful positions that show resistance to change?
Learning to control and direct conversations takes understanding and practice.
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1. SHAPING WORKLOAD IN SALES SUPPORT AND CUSTOMER
FULFILLMENT USING BUSINESS ANALYTICS
Jyotishko Biswas1
and Pitipong Lin2
1
Smarter Supply Chain Analytics, Integrated Supply Chain, IBM Corporation
Embassy Golf Links, Bangalore KA 560071 India
Phone: (91) 9620559998 E-mail: jybiswas@in.ibm.com
2
Senior Technical Staff Member (STSM), Integrated Supply Chain, IBM Corporation
One Rogers Street, Cambridge, MA 02142 USA
Phone: (617) 510-7777 E-mail: pitipong@us.ibm.com
Abstract—This is an IBM Integrated Supply Chain (ISC)
business analytics project to enable a better distribution of
workload across a fiscal quarter for the Global Sales Support
organization. The objective is to improve quality of Sales
Support, reduce overtime costs, and achieve higher service levels.
This allows us to identify sales opportunities having higher
chance of getting progressed to higher stages. To achieve that,
we applied CHAID and Markov Chain models to predict
probability of sales opportunities to be in different stages of
maturity in the coming future. This allows the sales team to
initiate and progress prospective opportunities, thus workload to
Sales Support, that would otherwise peak towards month-end or
quarter-end periods.
Keywords: Sales Support, Customer Fulfillment, Workforce
Analytics, Markov Chain, CHAID
I. INTRODUCTION
Sales Support and Customer Fulfillment (CF) play an
essential role in supporting the many pre-sales and post sales
needs of IBM sellers, Business Partners and clients around
the world. Whether helping to develop a proposal or qualify
a reference during the pre-sales process, or managing the
post-sales order and invoice process, these teams touch a high
volume of transactions every day.
With a merger of Sales Support and CF teams to build an
end-to-end sales transaction support in the ISC, we began
applying analytics to enable a process for “single seller touch
points” through the life of a transaction. For sellers and
business partners, this means spending less time with sales
support activity, reduced cycle time for sales transactions and
fewer hand-offs. For clients, this means a reduction in overall
cycle time from opportunity through order, and consistency
across quotes, contracts and invoices.
The Sales Support and CF personnel consistently experience
a high level of workload at end-of-month or end-of-quarter,
when majority of the deals progress through sales stages.
This can cause the organization to incur high overtime
expenses. In addition, workloads that are highly skewed to
the end of the quarter can be over-baring for the employees,
finally culminating into higher staff turnover.
We have devised a methodology to shape the workload by
allowing sellers to initiate sales opportunity (i.e. create
proposal) with a client earlier in the quarter.
The model identifies deals that have higher likelihood to
progress to higher stages of maturity in the quarter. Sales
Support work typically comes at higher stages. Using these
predictions Sellers can drive progression of opportunities to
higher stages, and hence initiate workload to Sales Support,
before month and quarter end.
We analyze attributes of an opportunity like dollar value,
country of origin, sales channel and its progression across
sales stages till now to predict future stage of the opportunity.
This work at same time provides sales pipeline visibility.
Sales organization understands where the opportunities can be
in near future, using which they can take necessary steps to
improve sales.
Workload shaping to better manage hardware workload has
been happening. Kim and Mvulla [7] proposed an energy
consumption optimization of the cloud environment. Using
task buffers and schedulers they implemented a workload
shaping technique in cloud infrastructure. Mountrouidou et al.
[9] proposed workload shaping techniques for power saving
on disk drives. However, workload shaping where we are
trying to modify volume of work coming into multiple global
centers, to enable better staffing of resources is relatively
new.
The review of literature is in section II. Section III discusses
how CHAID segmentation technique is used to identify
factors which impact time of an opportunity to close. For
example, factors like country in which opportunity originated,
whether opportunity managed by IBM Sales Team or
2. Business Partners impact time an opportunity takes to
progress through the sales pipeline. In section IV, we address
an approach to build a Markov Chain model on IBM closed
opportunities. Section V focuses on multiple uses of the
analytics solution to the sales and sales support organizations.
Finally, section VI offers concluding remarks and suggestions
for future research.
II. REVIEW OF LITERATURE
This section reviews literature in workforce predictive
analytics.
Heching and Lin [1] developed a workforce analytics
solution that enabled IBM to efficiently manage and plan
resources for its global customer fulfillment transaction
centers. The paper described use cases that focus on demand
forecasting, which applied predictive analytics to support
decisions on workload change and the number of resources
required to support the workload.
Gresh et al. [2} developed Resource Capacity Planning
Optimizer which applies supply chain management
techniques to better plan IBM’s need for skilled labor for
consulting, application development and other areas.
Asuink et al. [3] used a linear programming model for
personnel staffing strategies, estimating anticipated changes
in the transformation of the army as the IT systems and
infrastructure expand using trends in the shadow workforce.
Aldore-Noiman et al. [4] introduced an arrival count model
which is based on a mixed Poisson process, applying data
from a call center. A call center, requiring high customer
service levels, could use the quality-efficiency driven
technique's square-root staffing rule to balance the workload
per staff.
Gmach et al. [5] looked at the ability to pool resources in an
enterprise data center environment due to advances in
virtualization technology. The capacity planning model used
demand inputs such as types of workload demand patterns
and generation of synthetic workloads that predict future
demands based on patterns.
Eiselt and Marianov [6] studied a methodology to assign
tasks to employees using a workload distribution to generate
results.
III. SEGMENTATION USING CHAID
CHAID is a classification technique used to build decision
trees. We used CHAID to identify the key factors which
impact time taken by an opportunity to mature. This is
important, since in our solution we want to treat opportunities
which move fast differently than opportunities which move
slowly.
Closed in =< "X" days
Closed in > "X" days
Closed in =< "X" days
Closed in > "X" days
Closed in =< "X" days
Closed in > "X" days Closed in =< "X" days
Closed in > "X" days
Closed in =< "X" days
Closed in > "X" days
Closed in =< "X" days
Closed in > "X" days
All Opportunities
1452
1442
Country A and B
340
452
Country C and D
199
380
Country J
218
63
Country E,F and G
504
363
Country H and I
191
184
Fig. 1. First split with Country
Closed in =< "X" days
Closed in > "X" days
Closed in =< "X" days
Closed in > "X" days
Closed in =< "X" days
Closed in > "X" days
Online
118
75
Country A and B
340
452
Direct and BP
222
377
Fig. 2. Country A and B split by Channel
There are different factors which impact time taken for an
opportunity to progress. For example opportunity of
countries E, F, G, H, I and J progress across stages quickly,
than time taken by opportunities of other countries. We used
CHAID to identify these factors.
An opportunity closes in average “X” days; it is constant for
all decision trees built. We use X to represent a constant due
to the sensitivity of proprietary information.
Below are details of the decision tree model:
• SPSS Statistics used to build decision tree.
• Model successfully validated on test data.
• Pearson Chi Square test used to identify most significant
factor to split dependent variable.
• Pearson Chi Square test used to identify similar classes
within factor which need to be merged.
• Bonferroni adjustment of the significance values done.
3. IBM sells its products and services through different sales
channels like Direct, Business Partners (BP) and Online.
Sales Channel is impacting time taken by opportunity to
mature (Fig. 2).
In our problem we wanted to find out factors that impact
time taken by an opportunity to progress to higher sales
stages. Factors that have higher impact are:
• Country of Origin: Some countries take more time to
mature and opportunities of some countries mature
considerably quickly.
• Sales Channel: Opportunities managed online can close
faster than opportunities owned by Business Partners or
IBM sales.
• Opportunity Won or Lost: Time taken for an
opportunity to close is less for won opportunities.
• Opportunity Value: Opportunity takes more time to
close if its value is high.
IV. TRANSITION PROBABILITIES OF SECOND ORDER
MARKOV CHAIN
We use second order Markov Chain to predict movement
of opportunities across various stages over time.
We have seen in Section III that country significantly
impacts time taken by an opportunity to mature. Hence we
have separate Markov chain models for opportunities of
countries which taken more time to mature and countries
where opportunities move fast. Country group A has countries
whose opportunities move slowly and country group B has
countries whose opportunities move fast.
In Table 2 Second order Markov chain transition
probabilities tell us probability of opportunity to be in
“Accomplished” stage or won in next 28 days. These
transition probabilities have been built on data of opportunity
progression across stages of all opportunities which got
closed in 2011. In “Accomplished” stage proposal creation,
generating price quotes and other activities are done by Sales
Support organization.
Markov Chains have been used in diverse areas when it is
believed that the current condition is dependent on previous
conditions. Shamshad et al. [10] had predicted future rainfall
using First and Second order Markov Chains. Saibeni [8]
predicted account receivable collections using Markov Chain.
Lu [11] used discrete and continuous time Markov Chain
models to predict credit risk of Chiao Tung Bank in Taiwan.
Let us illustrate the different stages that an opportunity
goes through before they are won or lost (Fig. 3).
Won
Acknowledged
Accomplished
Confirmed
Recognized
Fig. 3. Example of Sales Stages
Table 2. Probability of Opportunities to Move to Higher
Stages
Movement across Stages
Country
Group A
Country
Group B
Recognized to Acknowledged to Won 24% 46%
Confirmed to Acknowledged to Won 25% 42%
Accomplished to Acknowledged to Won 26% 27%
Acknowledged to Acknowledged to Won 27% 22%
Accomplished to Accomplished to Won 13% 9%
Confirmed to Accomplished to Won 21% 20%
Recognized to Accomplished to Won 19% 22%
Confirmed to Confirmed to Won 5% 5%
Recognized to Confirmed to Won 7% 9%
Recognized to Recognized to Won 1% 2%
Recognized to Recognized to Accomplished 8% 9%
Confirmed to Confirmed to Accomplished 32% 38%
Recognized to Confirmed to Accomplished 24% 29%
Transition Probabilities used to get Probability to Reach Higher
Stages - 28 days between 2 time periods
We see that an opportunity after reaching higher stage if
doesn’t progress further for some time, then it’s chance for
further progression comes down. In comparison an
opportunity which just reached high stage from lower stage
will have more chance to progress further. This is observed
from transition probabilities when each time period is 28
days. An opportunity in “Acknowledged” stage in present and
previous time period will be won in next period with 22%
probability. However opportunity which moved from
“Recognized” stage in previous period to “Acknowledged”
stage in current period will have 42% probability to be won in
next period (Table 2).
Similarly we have transition probabilities which tell us
probability of opportunity being accomplished or won in next
14 days.
V. PREDICTIONS USING MARKOV CHAIN, HOW ARE
PREDICTIONS USED AND THEIR ACCURACY
A. Probability of an opportunity being accomplished in
coming 14 and 28 days.
4. Transition probabilities of Markov Chain used to predict the
probability of opportunities to be in higher stages in the
coming 14 days and 28 days. We use stage in which
opportunity is in present and previous time period to predict
stage in which opportunity will be in next period.
For example, suppose today is 29th Feb 2012. An
opportunity from Country A has been in the “Confirmed”
stage from 29th Feb till 16th Feb (the current time period)
and was in the “Recognized” stage from 15th Feb till 2nd Feb
(previous time period). Country A's opportunities take time to
close. Hence we have to use transition probabilities of
countries whose opportunities mature at slower pace.
Transition probability of an opportunity to move to the
“Accomplished” stage between 1st March and 14th March
(next time period), when presently it is in “Confirmed” stage,
and has been in “Recognized” stage in the previous time
period is 21%. Hence, probability of this opportunity to be
“Accomplished” between 1st March and 14th March is 21%.
Here we have considered each time period’s duration as two
weeks (Table 3).
In the same way we have predicted the probability of this
opportunity to progress to accomplished stage between1st
March and 28th March (Table 4). The difference would be
the duration of each time period would be four weeks. Hence,
we will look into the stage this opportunity is in between 2nd
Feb till 29th Feb (present time period) and also the stage in
which the opportunity was between 5th Jan and 1st Feb
(previous time period).
Opportunities are generally put in three probability buckets,
high, medium and low. Group “high” comprises top 33
percent opportunities having highest probability. 33 percent
of opportunities having lowest probability fall in group “low”.
Remaining opportunities fall in “medium” group.
B. Probability of opportunities to be won stage in the coming
14 and 28 days
Similarly using transition probabilities, we predicted the
probability of the opportunity to be won in next 14 days when
it is other sales stages in current and previous period (Table
5).
Table 3. Probability of Opportunity being “Accomplished” in coming 14 days
Opportunity
Number
Country Channel
Probability of being Won b/w 1st
and 14th March
Probability
Bucket
Stage from 16th Feb
till 29th Feb
Stage from 2nd Feb
till 15th Feb
Opportunity a Country A BP 21% High Confirmed Recognized
Opportunity b Country A Direct 21% High Confirmed Recognized
Opportunity c Country A Direct 21% High Confirmed Recognized
Opportunity d Country A Direct 21% High Confirmed Recognized
Table 4. Probability of Opportunity being “Accomplished” in coming 28 days
Opportunity
Number
Country Channel
Being Accomplished b/w 1st and
28th March
Probability
Bucket
Stage from 2nd Feb
till 29th Feb
Stage from 5th Jan
till 1st Feb
Opportunity 5 Country A Direct 8% Low Recognized Recognized
Opportunity 6 Country A Direct 8% Low Recognized Recognized
Opportunity 7 Country A Direct 8% Low Recognized Recognized
Opportunity 8 Country A ibm.com 8% Low Recognized Recognized
Table 5. Probability of Opportunity being won in coming 28 days
Opportunity
Number
Country Channel
Probability of being Won b/w 1st
and 28th March
Probability
Bucket
Stage from 2nd Feb
till 29th Feb
Stage from 5th Jan
till 1st Feb
Opportunity 9 Country A Direct 26% High Acknowledged Accomplished
Opportunity 10 Country A Direct 26% High Acknowledged Accomplished
Opportunity 11 Country A Direct 26% High Acknowledged Accomplished
Opportunity 12 Country A Direct 26% High Acknowledged Accomplished
5. Table 6. Accuracy of Results of Country A
Initial Stage Actual Predicted Error
Confirmed 54 72 33%
Number of Opportunities Accomplished in 14 days
Table 7. Accuracy of Results of Country A
Initial Stage Actual Predicted Error
Recognized 4 5 25%
Confirmed 30 19 37%
Accomplished 23 24 4%
Acknowledged 58 63 9%
Number of Opportunities Won in 14 days
Table 8. Accuracy of Results of Country B
Initial Stage Actual Predicted Error
Confirmed 24 24 0.0%
Number of Opportunities Accomplished in 14 days
Table 9. Accuracy of Results of Country B
Initial Stage Actual Predicted Error
Recognized 9 6 33%
Confirmed 4 5 25%
Accomplished 26 13 50%
Acknowledged 43 22 49%
Number of Opportunities Won in 14 days
C. Accuracy and Use of the Predictions
To measure accuracy of our predictions, we compare
number of opportunities which actually arrived in a specific
stage with what we predicted. For example in Country A, we
predicted 63 “Acknowledged” opportunities were won in 14
days. In reality, 58 “Acknowledged” opportunities were won.
Our predictions had an error of 9%.
Driving analytics into the fabric of the business, coupled
with process change management, is key to balancing
workload of Sales Support organization. This is the proposed
process change. First, Sellers are provided list of
opportunities having more chance to progress to higher stages
by end of month/quarter. Next, Sellers are asked to focus on
moving some of these opportunities faster to higher stages
before end of month/quarter, resulting in better distribution of
workload.
VI. CONCLUSION
We predicted future stage of opportunities in coming 14 and
28 days in proof of concept. Using our models we predicted
stages of opportunities during month end and quarter end.
We then start our dialogue to first validate our predictions
with sellers. After validation sellers progress some
opportunities to higher stages before month and quarter end.
It is beneficial for sellers to serve as subject matter experts to
verify our results.
To predict future stage of opportunities, we used Second
Order Markov Chain model. We found Markov Chain
models can predict progression of opportunities across
various stages with an acceptable level of accuracy. We also
built First Order Markov chain models, however Second
Order Markov Chain predictions were more accurate.
In the future research we will explore the dependency of
progression of opportunities on client’s behavior and revenue
targets.
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