Agile development of data science projects | Part 1 Anubhav Dhiman
This document discusses agile development of data science projects. It begins by defining data science as focusing on predicting, prescribing, or explaining something, distinct from business intelligence which focuses on reporting past events. It notes data science encompasses quantitative research, advanced analytics, predictive modeling, and machine learning. It then discusses how reliably data science teams can deliver value, showing a data science readiness level chart ranging from algorithm design to proven systems. The rest of the document discusses collaborating across teams and organizations to move from initial concepts to specific, integrated predictive systems.
How a global manufacturing company built a data science capability from scratchCarlo Torniai
In less than a year, Pirelli, a global manufacturing company best known for high-performance tires and motorsports, grew an impactful data science capability from the ground up. I am sharing a how-to guide for doing the same in your organization, equipping you with arguments to marshal and concrete tips to follow, while calling out pitfalls to watch out for along the way.
Abhishank Gaba has a BASc in Mechatronics Engineering from the University of Waterloo with a GPA of 4.0. He has experience leading projects involving machine learning and computer vision to detect critical points in pipes and identify tissue patterns. His relevant work experience includes product management and software development roles at startups focused on ignition interlock devices and smart underwear. He also has experience in quality assurance and software development.
Freenome's Biological Machine Learning PlatformBrandon White
The document discusses the challenges of applying machine learning to biological data, including noisy data, biases, and confounding factors. It argues that building a machine learning platform can help accelerate model development by allowing researchers to focus on their specialty rather than infrastructure, easily reproduce and build upon each other's work, and uniformly apply robust interpretation techniques. The platform would make common workflows like exploring new preprocessing methods, data types, validation schemes, or models a simple one-step process by leveraging existing shared components.
The challenges of Analytical Data Management in R&DLaura Berry
Presented at the Global Pharma R&D Informatics Congress. To find out more, visit:
www.global-engage.com
Analytical data is at the heart of pharmaceutical research, yet many organisations struggle with the variety of different formats, instrument vendors, and search and retrieval of data. In this presentation, Hans de Bie from ACD/Labs discusses automated capture, exchange formats, integrity, and next generation management systems.
The document provides an overview of the field of data science, discussing what data science entails, the roles of data analysts, engineers, and scientists, as well as the key skills involved which include programming languages like Python and R, tools like Spark and SQL, statistics, machine learning, and domain expertise. It also briefly touches on debates around tools and languages and the relationship between statistics and computer science in data science work.
Tech talk by Serena Signorelli (https://www.linkedin.com/in/serenasignorelli/) in the event ''Tensorflow and Sparklyr: Scaling Deep Learning and R to the Big Data ecosystem'', May 15, 2017 at ICTeam Grassobbio (BG). The event was part of the Data Science Milan Meetup (https://www.meetup.com/it-IT/Data-Science-Milan/).
Agile development of data science projects | Part 1 Anubhav Dhiman
This document discusses agile development of data science projects. It begins by defining data science as focusing on predicting, prescribing, or explaining something, distinct from business intelligence which focuses on reporting past events. It notes data science encompasses quantitative research, advanced analytics, predictive modeling, and machine learning. It then discusses how reliably data science teams can deliver value, showing a data science readiness level chart ranging from algorithm design to proven systems. The rest of the document discusses collaborating across teams and organizations to move from initial concepts to specific, integrated predictive systems.
How a global manufacturing company built a data science capability from scratchCarlo Torniai
In less than a year, Pirelli, a global manufacturing company best known for high-performance tires and motorsports, grew an impactful data science capability from the ground up. I am sharing a how-to guide for doing the same in your organization, equipping you with arguments to marshal and concrete tips to follow, while calling out pitfalls to watch out for along the way.
Abhishank Gaba has a BASc in Mechatronics Engineering from the University of Waterloo with a GPA of 4.0. He has experience leading projects involving machine learning and computer vision to detect critical points in pipes and identify tissue patterns. His relevant work experience includes product management and software development roles at startups focused on ignition interlock devices and smart underwear. He also has experience in quality assurance and software development.
Freenome's Biological Machine Learning PlatformBrandon White
The document discusses the challenges of applying machine learning to biological data, including noisy data, biases, and confounding factors. It argues that building a machine learning platform can help accelerate model development by allowing researchers to focus on their specialty rather than infrastructure, easily reproduce and build upon each other's work, and uniformly apply robust interpretation techniques. The platform would make common workflows like exploring new preprocessing methods, data types, validation schemes, or models a simple one-step process by leveraging existing shared components.
The challenges of Analytical Data Management in R&DLaura Berry
Presented at the Global Pharma R&D Informatics Congress. To find out more, visit:
www.global-engage.com
Analytical data is at the heart of pharmaceutical research, yet many organisations struggle with the variety of different formats, instrument vendors, and search and retrieval of data. In this presentation, Hans de Bie from ACD/Labs discusses automated capture, exchange formats, integrity, and next generation management systems.
The document provides an overview of the field of data science, discussing what data science entails, the roles of data analysts, engineers, and scientists, as well as the key skills involved which include programming languages like Python and R, tools like Spark and SQL, statistics, machine learning, and domain expertise. It also briefly touches on debates around tools and languages and the relationship between statistics and computer science in data science work.
Tech talk by Serena Signorelli (https://www.linkedin.com/in/serenasignorelli/) in the event ''Tensorflow and Sparklyr: Scaling Deep Learning and R to the Big Data ecosystem'', May 15, 2017 at ICTeam Grassobbio (BG). The event was part of the Data Science Milan Meetup (https://www.meetup.com/it-IT/Data-Science-Milan/).
Data analytics and software sensors for single use bioprocessingExputec
This document discusses data analytics and software sensors for single-use bioprocessing. It provides an overview of Exputec, which offers solutions to improve biopharmaceutical development and production processes using data analytics. Software sensors are described as using models to monitor process variables like substrates and metabolites, complementing hardware sensors. Challenges of data analytics include data integration from collaborators and dealing with outliers. The document advocates for using robust multivariate statistics rather than removing outliers to avoid incorrect results from data analytics.
QuTrack: Model Life Cycle Management for AI and ML models using a Blockchain ...QuantUniversity
As the complexity in AI and Machine Learning processes increases, robust data pipelines need to be
developed for industrial scale model development and deployment. . In regulated industries such as
Finance, Healthcare etc. where automated decision making is increasingly becoming used, tracking
design of experiments and from inception to deployment is critical to ensure a robust process is
adopted. Model Life-cycle management solutions are proposed to track experiments, design robust
experiments for hyper parameter tuning, optimization and selection of models and for monitoring.
The number of choices and the parameters that need to be tracked makes is significantly
challenging to trace experiments and to address reproducibility concerns.
In this talk, we discuss QuTrack, a Blockchain-based approach to track experiment and model
changes primarily for AI and ML models. In addition, we discuss how change analytics can be used
for process improvement and to enhance the model development and deployment processes.
Xiaodan Chen is a data science professional with over 2 years of research experience in quantitative analysis and machine learning as well as 8 months of industry experience. Her skills include Python, SQL, AWS, and machine learning algorithms. She has worked on projects involving NLP, fraud detection, recommendation systems, A/B testing, and more.
Data Science as a Service: Intersection of Cloud Computing and Data SciencePouria Amirian
Dr. Pouria Amirian explains data science, steps in a data science workflow and show some experiments in AzureML. He also mentions about big data issues in a data science project and solutions to them.
This document is a resume for Aastha Grover, who is seeking a full-time data science position starting in June 2017. She has a Master's degree in Information Systems from Northeastern University and is proficient in programming languages like R, Java, Python and databases like Neo4j, SQL Server and MongoDB. She has work experience in data science roles at Fidelity Investments and as a programmer analyst at Cognizant. She has also completed academic projects involving predictive modeling, big data analysis and recommendation systems.
Kaushik Shakkari is a graduate student at USC seeking a Master's degree in Computer Science with a focus on Data Science. He has work experience as a grader, team leader, and database intern. His research as an undergraduate focused on analyzing user browsing behavior. His skills include Python, Java, SQL, Tableau, Hadoop, and machine learning libraries. Some of his projects include clustering machines to detect malware, analyzing tablet sales data with Tableau, predicting customer churn for a bank, and optimizing plant growth recommendations. He has received awards for outstanding student and project contributions.
MOCHA Challenge was hosted by European Semantic Web Conference ESWC, 3-7 June 2018, held in Heraklion, Crete, Greece (Aldemar Knossos Royal & Royal Villa).
This work was supported by grants from the EU H2020 Framework Programme provided for the project HOBBIT (GA no. 688227).
Kristof Coussement - The Debate: the Future of (Big) Data Analytics SoftwareBAQMaR
The document discusses trends in analytical software such as big data, data science, and the growing popularity and use of the R programming language. It provides an overview of Revolution Analytics as the leading commercial provider of software and services based on the open source R language. The presentation discusses challenges in big data analysis and how tools like R and Revolution R can help address these challenges by enabling faster analytics on larger and more varied data sources.
Data analytics is the process of examining data sets to draw conclusions from the information. There are three main types: descriptive analytics which creates summaries of historical data; predictive analytics which is used to make predictions about future events; and prescriptive analytics which is dedicated to finding the best course of action for a given situation. The processes involved in data analytics are data requirement, collection, processing, cleaning, transformation, modeling. Google Analytics allows for data collection, processing, and reporting on acquisition, behavior, and conversion metrics to analyze website traffic and marketing effectiveness.
The document describes Oracle's G-Log Transportation Management software. It provides high-level overviews of the software's key capabilities including transportation optimization, collaboration with partners, automation, visibility, and management. Customers cite benefits like cost savings, improved on-time delivery, and streamlined processes.
This document discusses secure benchmarking and its applications. Secure benchmarking allows companies to anonymously share and compare sensitive performance data through encryption techniques. This allows benchmarking without revealing raw confidential data. The document provides examples of secure benchmarking for transportation procurement bids and shipment costs. Regression analysis is performed on encrypted data to identify cost structures and compare performance between companies and markets over time. Insights from benchmarking can guide strategy and value creation for shippers and carriers.
Smart ERP is a web bases solution with mobility having 20 + modules , for trading services and manufacturing concerns. On-premises and SAAS based deployment.
Smart Margin Analytics: Why Bolting on a Margin Assurance Capability to an Existing Revenue Assurance System can Deliver Big Savings - Efrat Nissimov, Director of Revenue Assurance Product Management, cVidya, in the Telecom Analytics 2013 Conference in Atlanta, January 30-31, 2013
Smart Margin Analytics: Why Bolting on a Margin Assurance Capability to an Ex...cVidya Networks
Smart Margin Analytics: Why Bolting on a Margin Assurance Capability to an Existing Revenue Assurance System can Deliver Big Savings – a presentation by Efrat Nissimov, cVidya’s Director of Product Management at the “Big Data and Analytics for Telecom & Mobile Carriers” event in Atlanta.
A Mathematical Model for Evaluation of Data Analytics Implementation Alternat...Jānis Grabis
This document presents a mathematical model for selecting an optimal platform for implementing new data analytics reports within an enterprise architecture. The model aims to minimize costs while maximizing user preferences and architectural principles. It considers development, maintenance, integration and decentralization costs. The model is demonstrated on a sample enterprise architecture of a fleet management solution with 4 new report requests. Scenarios are analyzed with different emphases on user preferences, centralization and integration costs. The optimization results and cost breakdowns are presented. The model allows prescriptive analysis of trade-offs between architectural principles.
This document provides an overview of Six Sigma and its application to software development. It discusses key Six Sigma concepts like DMAIC (Define, Measure, Analyze, Improve, Control), tools used in each phase, and how they can help improve processes and reduce defects in software development. It also covers process maturity models, different types of waste specific to software development, and how Six Sigma principles of data-driven problem solving can help organizations deliver higher quality software and improve customer satisfaction.
Understanding Business APIs through statisticsWSO2
This document discusses using statistics and data analysis to understand API usage. It describes WSO2's tools for offline and real-time analysis of API data. For offline analysis, the API Manager integrates with WSO2 Business Activity Monitor (BAM) which aggregates event streams, stores the data in Cassandra, analyzes it using Hive, and stores summaries in a relational database. For real-time analysis, the API Manager integrates with WSO2 Complex Event Processing (CEP) which executes queries over event streams to identify patterns like excessive requests from a client. It also discusses integrating Google Analytics for additional monitoring and visualization of API usage statistics.
Hear how an OEM has digitalized its direct spend processes with SAP and SAP Ariba solutions. Learn how to gain agility and savings by integrating product lifecycle management, source-to-contract management, planning, and supplier collaboration. See the blueprint for the future with artificial intelligence and machine learning-enabled supply chains.
ARC's Greg Gorbach CPM & Operations Mgmt Presentation @ ARC Industry Forum 2010ARC Advisory Group
CPM and Operations Management “Manufacturers Plant Software – Need Now More Than Ever”
ARC's Greg Gorbach CPM & Operations Mgmt Presentation @ ARC Industry Forum 2010 in Orlando, FL.
Operations Management/MES Trends
1. Deals Driven Top-Down
• Governance
• Process Improvements
• Manufacturing Excellence
• Visibility, End-to-End Performance
2. Business Decision to Invest in Plant IT
• Ops Platform
• Legacy replacement
• Needed for responsiveness, flexibility, quality, etc.
3. Integration with PLM Needed
• Customer-driven, not industry hype
• No standards means implementation cost
4. Sustainable Manufacturing Dawning
• Energy Management
• Carbon tracking, accountability, compliance
• Integrated, Flexible Packaging Management
5. Cloud Computing/Services/SaaS on horizon
Business Intelligence in Upstream-DownstreamNirav Modh
People working in the oil and gas industry focus on collecting, storing, analyzing, and interpreting large amounts of data to gain operational efficiencies and extract more value from core assets. A recent study suggests oil companies have opportunities to significantly increase value by carefully reviewing their current data management practices. Implementing business intelligence and analytics solutions across the downstream value chain can help companies improve cost control, drive efficiencies, and increase profitability through better management and analysis of operational data in real time.
Data analytics and software sensors for single use bioprocessingExputec
This document discusses data analytics and software sensors for single-use bioprocessing. It provides an overview of Exputec, which offers solutions to improve biopharmaceutical development and production processes using data analytics. Software sensors are described as using models to monitor process variables like substrates and metabolites, complementing hardware sensors. Challenges of data analytics include data integration from collaborators and dealing with outliers. The document advocates for using robust multivariate statistics rather than removing outliers to avoid incorrect results from data analytics.
QuTrack: Model Life Cycle Management for AI and ML models using a Blockchain ...QuantUniversity
As the complexity in AI and Machine Learning processes increases, robust data pipelines need to be
developed for industrial scale model development and deployment. . In regulated industries such as
Finance, Healthcare etc. where automated decision making is increasingly becoming used, tracking
design of experiments and from inception to deployment is critical to ensure a robust process is
adopted. Model Life-cycle management solutions are proposed to track experiments, design robust
experiments for hyper parameter tuning, optimization and selection of models and for monitoring.
The number of choices and the parameters that need to be tracked makes is significantly
challenging to trace experiments and to address reproducibility concerns.
In this talk, we discuss QuTrack, a Blockchain-based approach to track experiment and model
changes primarily for AI and ML models. In addition, we discuss how change analytics can be used
for process improvement and to enhance the model development and deployment processes.
Xiaodan Chen is a data science professional with over 2 years of research experience in quantitative analysis and machine learning as well as 8 months of industry experience. Her skills include Python, SQL, AWS, and machine learning algorithms. She has worked on projects involving NLP, fraud detection, recommendation systems, A/B testing, and more.
Data Science as a Service: Intersection of Cloud Computing and Data SciencePouria Amirian
Dr. Pouria Amirian explains data science, steps in a data science workflow and show some experiments in AzureML. He also mentions about big data issues in a data science project and solutions to them.
This document is a resume for Aastha Grover, who is seeking a full-time data science position starting in June 2017. She has a Master's degree in Information Systems from Northeastern University and is proficient in programming languages like R, Java, Python and databases like Neo4j, SQL Server and MongoDB. She has work experience in data science roles at Fidelity Investments and as a programmer analyst at Cognizant. She has also completed academic projects involving predictive modeling, big data analysis and recommendation systems.
Kaushik Shakkari is a graduate student at USC seeking a Master's degree in Computer Science with a focus on Data Science. He has work experience as a grader, team leader, and database intern. His research as an undergraduate focused on analyzing user browsing behavior. His skills include Python, Java, SQL, Tableau, Hadoop, and machine learning libraries. Some of his projects include clustering machines to detect malware, analyzing tablet sales data with Tableau, predicting customer churn for a bank, and optimizing plant growth recommendations. He has received awards for outstanding student and project contributions.
MOCHA Challenge was hosted by European Semantic Web Conference ESWC, 3-7 June 2018, held in Heraklion, Crete, Greece (Aldemar Knossos Royal & Royal Villa).
This work was supported by grants from the EU H2020 Framework Programme provided for the project HOBBIT (GA no. 688227).
Kristof Coussement - The Debate: the Future of (Big) Data Analytics SoftwareBAQMaR
The document discusses trends in analytical software such as big data, data science, and the growing popularity and use of the R programming language. It provides an overview of Revolution Analytics as the leading commercial provider of software and services based on the open source R language. The presentation discusses challenges in big data analysis and how tools like R and Revolution R can help address these challenges by enabling faster analytics on larger and more varied data sources.
Data analytics is the process of examining data sets to draw conclusions from the information. There are three main types: descriptive analytics which creates summaries of historical data; predictive analytics which is used to make predictions about future events; and prescriptive analytics which is dedicated to finding the best course of action for a given situation. The processes involved in data analytics are data requirement, collection, processing, cleaning, transformation, modeling. Google Analytics allows for data collection, processing, and reporting on acquisition, behavior, and conversion metrics to analyze website traffic and marketing effectiveness.
The document describes Oracle's G-Log Transportation Management software. It provides high-level overviews of the software's key capabilities including transportation optimization, collaboration with partners, automation, visibility, and management. Customers cite benefits like cost savings, improved on-time delivery, and streamlined processes.
This document discusses secure benchmarking and its applications. Secure benchmarking allows companies to anonymously share and compare sensitive performance data through encryption techniques. This allows benchmarking without revealing raw confidential data. The document provides examples of secure benchmarking for transportation procurement bids and shipment costs. Regression analysis is performed on encrypted data to identify cost structures and compare performance between companies and markets over time. Insights from benchmarking can guide strategy and value creation for shippers and carriers.
Smart ERP is a web bases solution with mobility having 20 + modules , for trading services and manufacturing concerns. On-premises and SAAS based deployment.
Smart Margin Analytics: Why Bolting on a Margin Assurance Capability to an Existing Revenue Assurance System can Deliver Big Savings - Efrat Nissimov, Director of Revenue Assurance Product Management, cVidya, in the Telecom Analytics 2013 Conference in Atlanta, January 30-31, 2013
Smart Margin Analytics: Why Bolting on a Margin Assurance Capability to an Ex...cVidya Networks
Smart Margin Analytics: Why Bolting on a Margin Assurance Capability to an Existing Revenue Assurance System can Deliver Big Savings – a presentation by Efrat Nissimov, cVidya’s Director of Product Management at the “Big Data and Analytics for Telecom & Mobile Carriers” event in Atlanta.
A Mathematical Model for Evaluation of Data Analytics Implementation Alternat...Jānis Grabis
This document presents a mathematical model for selecting an optimal platform for implementing new data analytics reports within an enterprise architecture. The model aims to minimize costs while maximizing user preferences and architectural principles. It considers development, maintenance, integration and decentralization costs. The model is demonstrated on a sample enterprise architecture of a fleet management solution with 4 new report requests. Scenarios are analyzed with different emphases on user preferences, centralization and integration costs. The optimization results and cost breakdowns are presented. The model allows prescriptive analysis of trade-offs between architectural principles.
This document provides an overview of Six Sigma and its application to software development. It discusses key Six Sigma concepts like DMAIC (Define, Measure, Analyze, Improve, Control), tools used in each phase, and how they can help improve processes and reduce defects in software development. It also covers process maturity models, different types of waste specific to software development, and how Six Sigma principles of data-driven problem solving can help organizations deliver higher quality software and improve customer satisfaction.
Understanding Business APIs through statisticsWSO2
This document discusses using statistics and data analysis to understand API usage. It describes WSO2's tools for offline and real-time analysis of API data. For offline analysis, the API Manager integrates with WSO2 Business Activity Monitor (BAM) which aggregates event streams, stores the data in Cassandra, analyzes it using Hive, and stores summaries in a relational database. For real-time analysis, the API Manager integrates with WSO2 Complex Event Processing (CEP) which executes queries over event streams to identify patterns like excessive requests from a client. It also discusses integrating Google Analytics for additional monitoring and visualization of API usage statistics.
Hear how an OEM has digitalized its direct spend processes with SAP and SAP Ariba solutions. Learn how to gain agility and savings by integrating product lifecycle management, source-to-contract management, planning, and supplier collaboration. See the blueprint for the future with artificial intelligence and machine learning-enabled supply chains.
ARC's Greg Gorbach CPM & Operations Mgmt Presentation @ ARC Industry Forum 2010ARC Advisory Group
CPM and Operations Management “Manufacturers Plant Software – Need Now More Than Ever”
ARC's Greg Gorbach CPM & Operations Mgmt Presentation @ ARC Industry Forum 2010 in Orlando, FL.
Operations Management/MES Trends
1. Deals Driven Top-Down
• Governance
• Process Improvements
• Manufacturing Excellence
• Visibility, End-to-End Performance
2. Business Decision to Invest in Plant IT
• Ops Platform
• Legacy replacement
• Needed for responsiveness, flexibility, quality, etc.
3. Integration with PLM Needed
• Customer-driven, not industry hype
• No standards means implementation cost
4. Sustainable Manufacturing Dawning
• Energy Management
• Carbon tracking, accountability, compliance
• Integrated, Flexible Packaging Management
5. Cloud Computing/Services/SaaS on horizon
Business Intelligence in Upstream-DownstreamNirav Modh
People working in the oil and gas industry focus on collecting, storing, analyzing, and interpreting large amounts of data to gain operational efficiencies and extract more value from core assets. A recent study suggests oil companies have opportunities to significantly increase value by carefully reviewing their current data management practices. Implementing business intelligence and analytics solutions across the downstream value chain can help companies improve cost control, drive efficiencies, and increase profitability through better management and analysis of operational data in real time.
Shailesh Mishra is a senior supply chain and procurement professional with nearly 6 years of experience in strategic procurement, supply chain management, and import activities. He has a proven track record of excellence in managing the entire supply chain function and implementing cost saving measures. He is targeting assignments in strategic procurement and supply chain management, preferably in North India with MNCs.
The document discusses Oracle's E-Business Suite Release 12 (R12) supply chain management (SCM) capabilities. R12 aims to help customers drive operational excellence, deliver enterprise innovation, and manage supply chain risk through richer information and faster decision making. New R12 SCM capabilities are highlighted across areas like demand planning, supplier management, transportation management, asset lifecycle management, order management, and business intelligence/dashboards. Analyst feedback praises R12 for its significant functionality improvements and tightly integrated suite approach.
This document provides an overview of SAP automotive procurement processes implemented in a major car manufacturer. It discusses background on automotive procurement principles learned from previous experiences. Key principles from Chrysler and Honda around supplier relationships and total cost modeling are examined. The document then outlines standard SAP procurement processes and building blocks relevant for automotive procurement. Specific processes around purchasing with just-in-time delivery schedules and quality management/vendor evaluation are described. Finally, it summarizes the complete procurement process and discusses next steps around potential Ariba integration.
As a product manager specialize in monetization for software, I like to share my concepts and techniques with my colleagues to help them understand my approach.
The document provides a summary of Luis Andrés Rodríguez Guzmán's professional experience as an SAP Consultant over 15+ years. It details his extensive experience implementing and customizing SAP modules for inventory management, warehouse management, purchasing, quality management, and other business processes across various industries. Recent projects include an SAP HANA implementation and quality module rollout for a manufacturing client.
This document discusses analytic engines and solutions for banking ecosystems. It outlines the key areas of compliance, risk, performance and customer analytics that banks need to address. It then describes the current analytic needs in areas like risk, finance, customers and specialized domains. Finally, it provides examples of how analytic engines can work for customer analytics, finance analytics and risk analytics.
Supply Chain Framework, Logistics Simulation Model for Food Delivery business.
This SCM Logistics Model has the simulator, modeller, scenario builder, database and customized reports.
The document discusses HCL's expertise and solutions for the automotive industry. It summarizes that HCL has over 10 years of experience serving 7 of the top 10 global automotive companies. It offers end-to-end IT services and solutions as well as engineering, electronics, and embedded services. HCL is continuously investing in solutions for advanced passenger experiences through smart electronics and mechanical enhancements.
The document provides an overview of Oracle E-Business Suite Release 12. Key points include:
- Release 12 provides a flexible architecture to meet the needs of dynamic global businesses.
- It enables users to think globally, work globally, and manage systems globally.
- New features improve global decision making, financial consolidation, profitability analysis, and other capabilities.
Similar to Industry 4.0: use cases for integrated supply chain (20)
Introduction to Jio Cinema**:
- Brief overview of Jio Cinema as a streaming platform.
- Its significance in the Indian market.
- Introduction to retention and engagement strategies in the streaming industry.
2. **Understanding Retention and Engagement**:
- Define retention and engagement in the context of streaming platforms.
- Importance of retaining users in a competitive market.
- Key metrics used to measure retention and engagement.
3. **Jio Cinema's Content Strategy**:
- Analysis of the content library offered by Jio Cinema.
- Focus on exclusive content, originals, and partnerships.
- Catering to diverse audience preferences (regional, genre-specific, etc.).
- User-generated content and interactive features.
4. **Personalization and Recommendation Algorithms**:
- How Jio Cinema leverages user data for personalized recommendations.
- Algorithmic strategies for suggesting content based on user preferences, viewing history, and behavior.
- Dynamic content curation to keep users engaged.
5. **User Experience and Interface Design**:
- Evaluation of Jio Cinema's user interface (UI) and user experience (UX).
- Accessibility features and device compatibility.
- Seamless navigation and search functionality.
- Integration with other Jio services.
6. **Community Building and Social Features**:
- Strategies for fostering a sense of community among users.
- User reviews, ratings, and comments.
- Social sharing and engagement features.
- Interactive events and campaigns.
7. **Retention through Loyalty Programs and Incentives**:
- Overview of loyalty programs and rewards offered by Jio Cinema.
- Subscription plans and benefits.
- Promotional offers, discounts, and partnerships.
- Gamification elements to encourage continued usage.
8. **Customer Support and Feedback Mechanisms**:
- Analysis of Jio Cinema's customer support infrastructure.
- Channels for user feedback and suggestions.
- Handling of user complaints and queries.
- Continuous improvement based on user feedback.
9. **Multichannel Engagement Strategies**:
- Utilization of multiple channels for user engagement (email, push notifications, SMS, etc.).
- Targeted marketing campaigns and promotions.
- Cross-promotion with other Jio services and partnerships.
- Integration with social media platforms.
10. **Data Analytics and Iterative Improvement**:
- Role of data analytics in understanding user behavior and preferences.
- A/B testing and experimentation to optimize engagement strategies.
- Iterative improvement based on data-driven insights.
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
We are pleased to share with you the latest VCOSA statistical report on the cotton and yarn industry for the month of March 2024.
Starting from January 2024, the full weekly and monthly reports will only be available for free to VCOSA members. To access the complete weekly report with figures, charts, and detailed analysis of the cotton fiber market in the past week, interested parties are kindly requested to contact VCOSA to subscribe to the newsletter.
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...Social Samosa
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
4. Order management & Fulfillment
Metrics
- # of Orders
- Ordered, Invoiced, Cancelled
- Discount
- COGS
- Margins
- Returns
- On time Pick Rate
- On time Shipping Performance
- Order to Invoice Lag
Inventory Metrics
- Available Consignments
- Blocked Consignments
- Days Inventory Outstanding
- In Transit Quantity
- Inspection Reports
- Inventory Turn
- Opening Quantity
- Replenishment Quantity
Data:
a. Structured
b. Semi-Structure
c. Unstructured
1. Core Transactional Data
2. Internal System Data
3. Other …?
Defining business KPIs
4
5. Target for predictive Analytics
in Supply chain
Network Planning and
Optimization – Do I have the
right network of manufacturing
and warehousing facilities?
● Optimized flow paths to
fulfill segments of demand
at lowest total cost.
Impacts: Fixed and variable
costs of operations.
Transportation Analytics –
Optimizing transportation routes
and loads including contract
compliance
● Optimizing shipment
schedules, routes including
backhaul.
Impacts: freight costs,
equipment utilization, contract
compliance.
Procurement Analytics – How to
achieve lowest landed cost and
secure long-term high quality
supplier partners?
● Implementing scoring
models for vendor quality,
cost, and stability.
Impacts: total landed cost,
security and quality of the
supplier chain.
5
6. Use Case: Upstream multi-tier visibility and analytics
Tier N Tier 2 Tier 1 Tier 0
➔ Suppliers ➔ Component
Manufacturers
➔ Suppliers
➔ Manufacturer
➔ Component
Manufacturers
➔ Manufacturer
● Components ● Sub assemblies
● Components
● Final Product
● Sub assemblies
● Final Product
Actors
Value
Add
Critical Parts Management
Parts Flow
Information and Planning Flow
6
7. Use Case: Pricing shipments & Capacity sourcing for 3PL
Input Output
Models
Datasets
● Shipment attributes
● Carrier attributes
● Lane attributes
● Weather
● Locations
● Events
● Contracts
● Historical Performance
Shipment X
from i to j
1. Shipper 1 $Bid A
2. Carrier 2 $Bid B
3. Carrier 3 $Bid C
● Acceptance model
● Profit maximization model
● Ranked carrier list
● Optimal shipper price
7
8. Demo: Price Variance Dashboard
Price variance dashboard References:
● Open dashboard: Click
here
● Online pricing and
capacity sourcing for
third-party logistics
providers: Click here
8
9. Use Case: Geospatial Analytics in Network Planning
Features:
● Network Overview
● Asset
management
9
10. Demo: Sandy Snow Removal Operations Web App
References:
● Open web app: Click here
● Network and locational
analysis: Click here
10
12. Use Case: pactris for Smart Cars
References:
● Open video: Click here
● Optimization of
heterogeneous Bin packing
using adaptive genetic
algorithm: Click here
● Warehouse Optimization:
Packing-algorithms for
goods with complex
geometries: Click here
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