Build an AI Roadmap and Win the Consumer Goods Intelligence RaceGib Bassett
My presentation from Salesforce Connections 2018. In it, I describe why it's important to think about a use case driven strategy for advanced analytics and AI.
Tips for Improving Google Shopping Campaign results Elizabeth Clark
Presenter slides from the first Shopping Guru's group meeting at Deloitte in Manchester. Attended by retailers , digital agencies and ad tech companies. Presentations covered the Google Myths to be wary of, rapid growth retailer case study, the agency role in managing feeds, and Manchester's opportunity to play a key role in setting shopping best practice.
Hi tech whitepaper_augmenting_value_saas_changing_business_eco-system_09_2010thinkofdevil
This document discusses key challenges faced by both SaaS providers and consumers. For customers, major challenges include revenue recognition with subscription models, supporting growing business needs with complex infrastructure, and integrating SaaS applications with existing in-house systems. For providers, ensuring customer success is critical as the popularity of SaaS depends on how adaptable solutions are to business needs. The document advocates exploring integration solutions and moving legacy systems to SaaS to help overcome these challenges.
2016 Oracle OpenWorld Presentation - Digital Field Service for Consumer GoodsGib Bassett
This document discusses the Internet of Things (IoT) in the consumer goods industry and how IoT can enable digital field service capabilities. It provides examples of how some consumer goods companies are currently using IoT technologies. The document then discusses how IoT can provide opportunities to improve field service through remote monitoring, predictive maintenance, and resolving issues without dispatches. It demonstrates an Oracle solution for digital field service for consumer goods that uses IoT, analytics and other cloud services to predict and address machine issues to improve uptime, service levels and costs.
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.
Unleash Enterprise Innovation with Sogeti’s Industry SolutionsCapgemini
Sogeti’s industry solutions, built on Hewlett Packard Enterprise ConvergedSystem for Microsoft Analytic Platform System (APS) and Power BI, create a proven platform for visualizing, modeling and reporting data insights for industries including Healthcare and Retail.
Learn how to unite structured inpatient and outpatient data. Find out how to converge real-time inventory visualization and notifications from external sources and Social Media. Learn to capture and analyze data to improve your decision-making.
Presented at Discover London 2015.
Build an AI Roadmap and Win the Consumer Goods Intelligence RaceGib Bassett
My presentation from Salesforce Connections 2018. In it, I describe why it's important to think about a use case driven strategy for advanced analytics and AI.
Tips for Improving Google Shopping Campaign results Elizabeth Clark
Presenter slides from the first Shopping Guru's group meeting at Deloitte in Manchester. Attended by retailers , digital agencies and ad tech companies. Presentations covered the Google Myths to be wary of, rapid growth retailer case study, the agency role in managing feeds, and Manchester's opportunity to play a key role in setting shopping best practice.
Hi tech whitepaper_augmenting_value_saas_changing_business_eco-system_09_2010thinkofdevil
This document discusses key challenges faced by both SaaS providers and consumers. For customers, major challenges include revenue recognition with subscription models, supporting growing business needs with complex infrastructure, and integrating SaaS applications with existing in-house systems. For providers, ensuring customer success is critical as the popularity of SaaS depends on how adaptable solutions are to business needs. The document advocates exploring integration solutions and moving legacy systems to SaaS to help overcome these challenges.
2016 Oracle OpenWorld Presentation - Digital Field Service for Consumer GoodsGib Bassett
This document discusses the Internet of Things (IoT) in the consumer goods industry and how IoT can enable digital field service capabilities. It provides examples of how some consumer goods companies are currently using IoT technologies. The document then discusses how IoT can provide opportunities to improve field service through remote monitoring, predictive maintenance, and resolving issues without dispatches. It demonstrates an Oracle solution for digital field service for consumer goods that uses IoT, analytics and other cloud services to predict and address machine issues to improve uptime, service levels and costs.
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.
Unleash Enterprise Innovation with Sogeti’s Industry SolutionsCapgemini
Sogeti’s industry solutions, built on Hewlett Packard Enterprise ConvergedSystem for Microsoft Analytic Platform System (APS) and Power BI, create a proven platform for visualizing, modeling and reporting data insights for industries including Healthcare and Retail.
Learn how to unite structured inpatient and outpatient data. Find out how to converge real-time inventory visualization and notifications from external sources and Social Media. Learn to capture and analyze data to improve your decision-making.
Presented at Discover London 2015.
Learn more about a world beyond CRM suites and how your company can build the customer data technology stack that matches the reality of today’s multi-channel, digital era.
The document provides an overview of EY's advisory services. It discusses how EY helps clients address forces like digitization, increasing regulation, and globalization by developing strategies to drive transformational change, optimizing business processes, and implementing technologies. It highlights EY's focus on leadership, alignment, execution, and adoption. The document shares examples of how EY has helped clients in industries like utilities, healthcare, and banking grow revenues, improve operations, and protect their businesses and brands. It also notes EY's global scale and recognition as a market leader in consulting.
Join Mindmatrix's Kevin Hospodar and SiriusDecision's Kathy Freeman Contreras, as they discuss-
-The most effective means for delivering lead generating programs that achieve the highest adoption rates and ROI
-How you can drive better engagement and marketing performance with partners
-The 4 phases of the SiriusDecisions Fast-Tracking Channel Demand Model
-How you can prepare for a successful sales enablement project
Breaking Bad Data: The Journey to Data-fuelled Digital TransformationCapgemini
Jorgen Heizenberg explains how a business can harness data both from within and outside the organization to fuel its journey to digital transformation.
Presented at Informatica World 2016 by Jorgen Heizenberg, CTO Netherlands, Capgemini Insights & Data
Peppercan is a cloud-based business management software created by Safecoms that allows small and medium-sized businesses to increase performance and management visibility through integrated customer relationship management, marketing, project collaboration and finance modules. It provides a centralized place to store critical business data securely and helps users improve relationships and profitability through a subscription model. Safecoms is a startup company based in Thailand that has seen success with over 400 Peppercan users across 8 countries, with resellers establishing in multiple countries in Europe and Asia.
The document discusses how SAP BPM can help streamline new product introduction processes by reducing cycle times, improving data quality, increasing visibility, and providing greater process agility. It provides an example of a company that was able to cut new product launch times from 3-6 months to 3-4 weeks by implementing SAP BPM to automate and standardize cross-functional and cross-country coordination activities.
Calculating the ROI on investing in data products?
Analytics return $13.01 for every dollar spent, according to Nucleus Research. That’s a 13:1 ROI for you, and for your customers when you offer embedded analytics in your SaaS solution. Check out this guide to learn more about the benefits of buying vs. building, and how GoodData customers like Influitive and Demandbase are achieving upwards of 650% ROI.
Give your customers what they want, with SaaS embedded analytics Powered by GoodData. Read this guide to learn why Zendesk says “Advanced analytics are the #1 reason why customers upgrade.” Get a better understanding of:
1. How embedded analytics can help you differentiate in a crowded SaaS market
2. Why Forrester identifies the cloud and analytics as two key drivers of future business applications innovation
3. How you can practice agile revenue development, monetizing the data you already have within your core application
4. The unique benefits of becoming a Powered by GoodData embedded analytics partner
5. How GoodData is driving revenue, retention and relationships for software vendors across operations, martech, health, travel and other sectors
Rahul Chande, Specialist Leader at Deloitte Consulting LLP, shares keys to master data management and effectiveness in the federal government sector at the 2015 Informatica Government Summit.
PTC Joins New Salesforce Analytics Cloud Ecosystem to Extend Internet of Thin...PTC
Continuing with its Internet of Things strategy to enable customers to bring smart, connected products to market faster, PTC (Nasdaq: PTC) today announced it has joined the Salesforce Analytics Cloud ecosystem.
This document provides an overview and summary of new features in IBM SPSS Predictive Analytics and IBM Decision Optimization. It discusses how predictive analytics can help organizations in various industries and functional areas. Key new features highlighted include empowering every user, unlocking more data faster, ground to cloud deployment options, optional coding and open source integration, and making predictive analytics accessible everywhere. The document demonstrates how these solutions have provided quantified benefits to customers.
Driving change: Unlocking data to transform the front officeAccenture Operations
Front office teams like marketing, sales, and customer service often operate in silos with separate data. This document discusses how unlocking data can transform the front office by:
1) Breaking down data silos between teams so they can speak a common language of data;
2) Using data to gain insights about customers and ask the right questions to optimize the customer lifecycle;
3) Transforming processes, technology, and talent to make data the lever for future-ready growth across more than 11% over three years.
Business Partner Product Enablement Roadmap, IBM Predictive AnalyticsArrow ECS UK
This document provides an overview of IBM's predictive analytics products and capabilities. It discusses IBM SPSS products like Statistics, Modeler, Data Collection, Text Analytics for Surveys, and Analytic Server. It explains what each product does, such as build predictive models, analyze structured and unstructured data, deploy analytics, and more. The document also highlights the strengths of the IBM predictive analytics portfolio in areas like customer analytics, operational analytics, threat and fraud analytics, and decision management.
The document discusses how SAP NetWeaver enables smart enterprises by addressing five key business challenges through business intelligence. It provides the complete, end-to-end and open BI platform needed for effective decision making, compliance, aligning strategy with execution, understanding competitors and markets, and justifying IT budgets. Case studies of Aventis and Dow Corning are presented showing how SAP NetWeaver addressed their challenges.
Operationalizing Customer Analytics with Azure and Power BICCG
Many organizations fail to realize the value of data science teams because they are not effectively translating the analytic findings produced by these teams into quantifiable business results. This webinar demonstrates how to visualize analytic models like churn and turn their output into action. Senior Business Solution Architect, Mike Druta, presents methods for operationalizing analytic models produced by data science teams into a repeatable process that can be automated and applied continuously using Azure.
CWIN17 san francisco-shawn kelly-iot business valueCapgemini
This document discusses how manufacturers are driving business value by adopting Internet of Things (IoT) technologies. It provides examples of how IoT is being used across different parts of organizations, such as using sensor data from industrial pumps to improve product design (product development) and increasing manufacturing plant efficiency through real-time visibility (operations/manufacturing). The document advocates that both physical products and digital systems must work together to unlock value from IoT, and shares how PTC's technologies connect the physical and digital worlds for customers.
Why is Sales and Operations Planning So Hard?Lora Cecere
Sales and Operations Planning processes are not a panacea. Just because an organization has a process, does not automatically mean that the company will drive value.
In the past decade, company progress moved backwards with fewer and fewer companies believing that they are successful. The reasons? Lack of definition of supply chain excellence, the need for design, clear delineation of governance, clarity of the role of the financial budget and the organizational tension in reconciliation with the market, and the lack of organizational alignment.
Best practice for data interoperabilityCRMT Digital
This document discusses best practices for data interoperability. It notes that as customer interactions occur across multiple channels, organizations need to integrate their data to gain insights. However, data is accumulating in new silos. Achieving a holistic, secure view of data requires establishing central control and providing technical marketing training. The key is enabling complete transaction visibility and interoperability across all departments to improve marketing effectiveness.
The document provides an overview and comparison of legacy CPQ (configure-price-quote) solutions versus next-generation CPQ solutions. It discusses key criteria for evaluating CPQ solutions such as flexibility and ease of use, cross-platform mobility, customer experience, go-to-market cycles, pricing and implementation time. Next-generation CPQ solutions are more affordable, easy to use, mobile-enabled and have faster implementation compared to legacy CPQ solutions which require more resources and customization. The document aims to help readers understand the CPQ market and choose the right solution for their needs.
Machine Learning Application to Manufacturing using Tableau and Google by Pluto7Manju Devadas
This document summarizes the backgrounds and proposed presentation by Manju Devadas and Salil Amonkar of Pluto7. Manju has over 25 years of experience in business process transformation, IT strategy, and management consulting. Salil has over 20 years of supply chain and technology experience. Their proposed presentation will provide an introduction to machine learning and supply chain use cases, including forecasting, predictive analytics, and price optimization. It will also demonstrate visualizing insights from machine learning models in Tableau.
The document describes building a supply chain academy using a competency-based learning approach. It involves 3 main steps: 1) Developing a competence framework and mapping competencies to job roles, defining competency levels. 2) Conducting a gap analysis to determine competency gaps. 3) Building individualized, blended learning programs based on job roles to address gaps through activities like e-learning, simulations, and trainings. The goal is to develop world-class supply chain skills and build team capabilities, not just improve individual knowledge.
Learn more about a world beyond CRM suites and how your company can build the customer data technology stack that matches the reality of today’s multi-channel, digital era.
The document provides an overview of EY's advisory services. It discusses how EY helps clients address forces like digitization, increasing regulation, and globalization by developing strategies to drive transformational change, optimizing business processes, and implementing technologies. It highlights EY's focus on leadership, alignment, execution, and adoption. The document shares examples of how EY has helped clients in industries like utilities, healthcare, and banking grow revenues, improve operations, and protect their businesses and brands. It also notes EY's global scale and recognition as a market leader in consulting.
Join Mindmatrix's Kevin Hospodar and SiriusDecision's Kathy Freeman Contreras, as they discuss-
-The most effective means for delivering lead generating programs that achieve the highest adoption rates and ROI
-How you can drive better engagement and marketing performance with partners
-The 4 phases of the SiriusDecisions Fast-Tracking Channel Demand Model
-How you can prepare for a successful sales enablement project
Breaking Bad Data: The Journey to Data-fuelled Digital TransformationCapgemini
Jorgen Heizenberg explains how a business can harness data both from within and outside the organization to fuel its journey to digital transformation.
Presented at Informatica World 2016 by Jorgen Heizenberg, CTO Netherlands, Capgemini Insights & Data
Peppercan is a cloud-based business management software created by Safecoms that allows small and medium-sized businesses to increase performance and management visibility through integrated customer relationship management, marketing, project collaboration and finance modules. It provides a centralized place to store critical business data securely and helps users improve relationships and profitability through a subscription model. Safecoms is a startup company based in Thailand that has seen success with over 400 Peppercan users across 8 countries, with resellers establishing in multiple countries in Europe and Asia.
The document discusses how SAP BPM can help streamline new product introduction processes by reducing cycle times, improving data quality, increasing visibility, and providing greater process agility. It provides an example of a company that was able to cut new product launch times from 3-6 months to 3-4 weeks by implementing SAP BPM to automate and standardize cross-functional and cross-country coordination activities.
Calculating the ROI on investing in data products?
Analytics return $13.01 for every dollar spent, according to Nucleus Research. That’s a 13:1 ROI for you, and for your customers when you offer embedded analytics in your SaaS solution. Check out this guide to learn more about the benefits of buying vs. building, and how GoodData customers like Influitive and Demandbase are achieving upwards of 650% ROI.
Give your customers what they want, with SaaS embedded analytics Powered by GoodData. Read this guide to learn why Zendesk says “Advanced analytics are the #1 reason why customers upgrade.” Get a better understanding of:
1. How embedded analytics can help you differentiate in a crowded SaaS market
2. Why Forrester identifies the cloud and analytics as two key drivers of future business applications innovation
3. How you can practice agile revenue development, monetizing the data you already have within your core application
4. The unique benefits of becoming a Powered by GoodData embedded analytics partner
5. How GoodData is driving revenue, retention and relationships for software vendors across operations, martech, health, travel and other sectors
Rahul Chande, Specialist Leader at Deloitte Consulting LLP, shares keys to master data management and effectiveness in the federal government sector at the 2015 Informatica Government Summit.
PTC Joins New Salesforce Analytics Cloud Ecosystem to Extend Internet of Thin...PTC
Continuing with its Internet of Things strategy to enable customers to bring smart, connected products to market faster, PTC (Nasdaq: PTC) today announced it has joined the Salesforce Analytics Cloud ecosystem.
This document provides an overview and summary of new features in IBM SPSS Predictive Analytics and IBM Decision Optimization. It discusses how predictive analytics can help organizations in various industries and functional areas. Key new features highlighted include empowering every user, unlocking more data faster, ground to cloud deployment options, optional coding and open source integration, and making predictive analytics accessible everywhere. The document demonstrates how these solutions have provided quantified benefits to customers.
Driving change: Unlocking data to transform the front officeAccenture Operations
Front office teams like marketing, sales, and customer service often operate in silos with separate data. This document discusses how unlocking data can transform the front office by:
1) Breaking down data silos between teams so they can speak a common language of data;
2) Using data to gain insights about customers and ask the right questions to optimize the customer lifecycle;
3) Transforming processes, technology, and talent to make data the lever for future-ready growth across more than 11% over three years.
Business Partner Product Enablement Roadmap, IBM Predictive AnalyticsArrow ECS UK
This document provides an overview of IBM's predictive analytics products and capabilities. It discusses IBM SPSS products like Statistics, Modeler, Data Collection, Text Analytics for Surveys, and Analytic Server. It explains what each product does, such as build predictive models, analyze structured and unstructured data, deploy analytics, and more. The document also highlights the strengths of the IBM predictive analytics portfolio in areas like customer analytics, operational analytics, threat and fraud analytics, and decision management.
The document discusses how SAP NetWeaver enables smart enterprises by addressing five key business challenges through business intelligence. It provides the complete, end-to-end and open BI platform needed for effective decision making, compliance, aligning strategy with execution, understanding competitors and markets, and justifying IT budgets. Case studies of Aventis and Dow Corning are presented showing how SAP NetWeaver addressed their challenges.
Operationalizing Customer Analytics with Azure and Power BICCG
Many organizations fail to realize the value of data science teams because they are not effectively translating the analytic findings produced by these teams into quantifiable business results. This webinar demonstrates how to visualize analytic models like churn and turn their output into action. Senior Business Solution Architect, Mike Druta, presents methods for operationalizing analytic models produced by data science teams into a repeatable process that can be automated and applied continuously using Azure.
CWIN17 san francisco-shawn kelly-iot business valueCapgemini
This document discusses how manufacturers are driving business value by adopting Internet of Things (IoT) technologies. It provides examples of how IoT is being used across different parts of organizations, such as using sensor data from industrial pumps to improve product design (product development) and increasing manufacturing plant efficiency through real-time visibility (operations/manufacturing). The document advocates that both physical products and digital systems must work together to unlock value from IoT, and shares how PTC's technologies connect the physical and digital worlds for customers.
Why is Sales and Operations Planning So Hard?Lora Cecere
Sales and Operations Planning processes are not a panacea. Just because an organization has a process, does not automatically mean that the company will drive value.
In the past decade, company progress moved backwards with fewer and fewer companies believing that they are successful. The reasons? Lack of definition of supply chain excellence, the need for design, clear delineation of governance, clarity of the role of the financial budget and the organizational tension in reconciliation with the market, and the lack of organizational alignment.
Best practice for data interoperabilityCRMT Digital
This document discusses best practices for data interoperability. It notes that as customer interactions occur across multiple channels, organizations need to integrate their data to gain insights. However, data is accumulating in new silos. Achieving a holistic, secure view of data requires establishing central control and providing technical marketing training. The key is enabling complete transaction visibility and interoperability across all departments to improve marketing effectiveness.
The document provides an overview and comparison of legacy CPQ (configure-price-quote) solutions versus next-generation CPQ solutions. It discusses key criteria for evaluating CPQ solutions such as flexibility and ease of use, cross-platform mobility, customer experience, go-to-market cycles, pricing and implementation time. Next-generation CPQ solutions are more affordable, easy to use, mobile-enabled and have faster implementation compared to legacy CPQ solutions which require more resources and customization. The document aims to help readers understand the CPQ market and choose the right solution for their needs.
Machine Learning Application to Manufacturing using Tableau and Google by Pluto7Manju Devadas
This document summarizes the backgrounds and proposed presentation by Manju Devadas and Salil Amonkar of Pluto7. Manju has over 25 years of experience in business process transformation, IT strategy, and management consulting. Salil has over 20 years of supply chain and technology experience. Their proposed presentation will provide an introduction to machine learning and supply chain use cases, including forecasting, predictive analytics, and price optimization. It will also demonstrate visualizing insights from machine learning models in Tableau.
The document describes building a supply chain academy using a competency-based learning approach. It involves 3 main steps: 1) Developing a competence framework and mapping competencies to job roles, defining competency levels. 2) Conducting a gap analysis to determine competency gaps. 3) Building individualized, blended learning programs based on job roles to address gaps through activities like e-learning, simulations, and trainings. The goal is to develop world-class supply chain skills and build team capabilities, not just improve individual knowledge.
Presentation at the October Scope Event on Internet of ThingsLora Cecere
How do we do we make decisions at the speed of business? Traditional supply chain processes are batch, and out of cadence with business. How do we rethink these processes to have the right data available when we need it. In this presentation, we discuss the inclusion of streaming data in supply chain visibility. It is not sufficient to ask the question of "Where is my stuff?" without the opportunity to use the data in better decision making.
The Fresh Connection - Simulation based Supply Chain Learning PlatformFrinson Francis
The Fresh Connection is a Web based Business Simulation in the area of Supply Chain Management and Organisation Wide Collaboration used for Experiential Learning. Learn Supply Chain Management, Supply Chain Performance and Analysis, Sales and Operations Planning, Inventory Management, Supply Chain Strategy, Demand Planning, Collaboration, Risk Management in Supply Chains with in-house workshops at your company
From Big Data to Big Value presented by Nicolas Kruchten, Head of Product Engineering at Datacratic. Presented at the Montreal kickoff of Big Data Week 2014 #bdw14.
The Machine Learning Value Chain is a simple framework that shows how to build products that make real-time automated decisions to take you from Big Data to Big Value.
This document discusses marketing channels and supply chain management. It covers why companies use distribution channels, how channel members interact and are organized, major channel alternatives, selecting, motivating and evaluating channel members, and the importance of marketing logistics and integrated supply chain management. Specific topics include channel design decisions, managing channel relationships, and the goals and functions of effective logistics systems.
“How P2P Fits Within an Enterprise Supply Chain” is the second topic of a supply chain learning series presented by ScottMadden and Shared Services & Outsourcing Network (SSON).
Big Data & Analytics to Improve Supply Chain and Business PerformanceBristlecone SCC
Prof. David Simchi Levi, Engineering Systems Professor at MIT and Chairman of OPS Rules spoke at Bristlecone Pulse 2017 about delivering customer value through digitization, analytics and automation.
Artificial intelligence transforming the phase of supply chain managementRahul R
Artificial intelligence is transforming supply chain management by optimizing business processes and establishing agile supply chains. AI can help with inventory control and planning by accessing real-time information on customer demands and inventory levels. It can also help with transportation network design challenges like routing and scheduling through techniques like genetic algorithms and ant colony optimization. Expert systems allow purchasing managers to evaluate suppliers and make more informed make-or-buy decisions. Overall, integrating AI offers competitive advantages through predictive analytics and more efficient supply chain management.
A basic presentation of how we can use Machine learning to sort out different problems faced by supply chain management and How we can also use it to model Inventory management.
Over 40,000 clients around the globe use the Accenture Supply Chain Academy because they want to raise the performance of their supply chain through knowledge & skill development of their employees... Our online learning solution has allowed them to improve efficiency and reduce cost because we cover all the functional areas of the supply chain. I would love to discuss this with you further patricia.b.terra@accenture.com
Real-Time Supply Chain Analytics with Machine Learning, Kafka, and SparkSingleStore
This document discusses real-time supply chain analytics using machine learning, Kafka, and Spark. It outlines four key requirements for real-time supply chain databases: supporting massive data ingestion, serving as a system of record while providing real-time analytics, integrating with familiar ecosystems, and allowing for online scaling. The document then introduces MemSQL as a database platform that can meet these requirements using an in-memory approach. It provides an example called MemEx that combines MemSQL, Kafka, and Spark with machine learning for global supply chain management and real-time predictive analytics.
Starbucks has achieved success through several factors:
1) Their unconventional marketing strategy focuses on high quality products and customer experience rather than traditional advertising.
2) Strategic expansion establishes hubs in major cities before expanding to surrounding areas, allowing them to quickly achieve market dominance.
3) While threats from competitors exist, Starbucks differentiates itself through its brand image and emphasis on consistency in customer experience across all stores.
The document discusses four components of an effective supply chain modeling platform: 1) A unified optimization and simulation engine, 2) Automated model building for simplified data analytics and documentation, 3) Cloud-based model solving and collaboration, and 4) A shared service center/center of excellence for supply chain design. The platform allows businesses to build a "living, digital model" of their end-to-end supply chain to enable continuous improvement, answer "what-if" questions, and rapidly adapt to changing market conditions.
Cloud technology is no longer a new player in the market,
but it’s a mature and integral part of the IT landscape and a
key parameter in driving business growth. It is an
indispensable topic among CXOs. A research by Fraedon has
found that almost half of the banks find their legacy
systems to be the biggest hindrance in their growth.
The document discusses how communications service providers are moving their legacy business support systems to the cloud in order to reduce costs, increase speed and agility, and improve customer experiences. It introduces Accenture's SaaS Reference Architecture for Communications Service Providers, which provides a framework and reference designs to simplify and accelerate the migration of business support systems to cloud platforms like Salesforce. The reference architecture helps communications providers develop cloud-based solutions more quickly and reduce project risks.
Supply chain design was traditionally done through occasional projects but is now becoming a core business process. This is driven by advancements in technology allowing more detailed analysis, the fast pace of business changes requiring continuous optimization, and increased volatility. Establishing supply chain design as a centralized shared service can help organizations benefit from consistency, prioritization of initiatives, and identification of quick wins and game-changing opportunities.
Supply chain design is becoming a core business process rather than an occasional project due to changes in technology, business practices, and market volatility. Establishing supply chain design as a core process through a shared service center can help optimize the entire supply chain rather than individual business units. This centers of excellence approach avoids local biases and focuses on data-driven solutions.
Supply chain design is becoming a core business process rather than an occasional project due to changes in technology, business practices, and market volatility. Establishing supply chain design as a core process through a shared service center can help optimize the entire supply chain through continuous modeling, scenario testing, and identification of better future state networks. This provides consistency, standardized processes, and access to data that isolated optimization projects lack.
1) The document discusses new delivery models for IT sourcing, including transformational outsourcing, innovative outsourcing, menu-based solutions, new pricing models, and delivery from low-cost locations.
2) Transformational outsourcing aims to help clients achieve competitive advantage through business process transformation and technology optimization. It leverages a partner's expertise to implement strategic changes.
3) Innovative outsourcing involves partners taking on an innovation role to help clients transform their business functions, models, products, and operations through approaches like social media integration and green IT.
Beyond Cost Savings: Driving Business Value from the Cloud Through XaaSCognizant
By using the cloud for analytics services and other run-the-business capabilities, organizations can move quickly into new markets and free capital for innovation initiatives.
Cloudway is a consulting firm that provides strategic sourcing, CRM consulting, HRM solutions, ERP implementation, and cloud integration services. It has a large team in India to help enterprises source more efficiently and improve operations. Cloudway's mission is to act as a trusted partner for clients' IT transformations and technology projects. It has expertise in providing cloud platforms and solutions through innovative partnerships with leading technology companies.
This document proposes a data mining project for XXXX Kanpur to help streamline their operations. It discusses common challenges faced by manufacturing/retail industries such as tight margins, high competition, and large amounts of data that is difficult to analyze. The proposal recommends implementing a data warehouse and using data mining techniques to gain insights from the data. This would help overcome challenges by facilitating faster decision making. Examples of how data mining can be used for applications like customer segmentation, sales forecasting, and process optimization are provided. The company, Accommodator Consultancy Services, is well-qualified for the project due to their experience implementing similar systems and analyzing big data.
Effective performance engineering is a critical factor in delivering meaningful results. The implementation must be built into every aspect of the business, from IT and business management to internal and external customers and all other stakeholders. Convetit brought together ten experts in the field of performance engineering to delve into the trends and drivers that are defining the space. This Foresights discussion will directly influence Business and Technology Leaders that are looking to stay ahead of the challenges they face with delivering high performing systems to their end users, today and in the next 2-5 years.
An SCCT provides more than just visibility - it orchestrates intelligent response and execution throughout the supply chain. GE Appliances implemented a control tower that reduced order backlogs through real-time tracking and machine learning. True SCCTs anticipate market changes, deeply understand customers, and engage them with personalized experiences. They are built on flexible cloud architectures and implement capabilities through a hybrid approach of business use cases over time to generate quick value while strengthening organization-wide capabilities.
The majority of survey respondents (71%) are unfamiliar with or in the education phase of cloud computing, while only 11% have plans to implement cloud initiatives in 2010. Many software companies are rapidly expanding their portfolio of cloud offerings across infrastructure, platform, and software services. Transitioning to a software-as-a-service (SaaS) model provides benefits like reduced costs, faster implementation, scalability, and security, but established software companies face challenges adapting their business models, partnerships, and operations to the SaaS approach. Recurring revenue from SaaS contracts is a key driver of higher business valuations for software companies.
Accenture and Salesforce have a strategic partnership to deliver enterprise cloud solutions that help clients accelerate business value and enable high performance. Accenture is recognized as Salesforce's most experienced integration partner, having completed hundreds of successful implementations across industries. Together, they provide a broad set of cloud and industry solutions to help companies connect with customers, partners, and employees in new ways to drive revenues and performance.
Accenture and Salesforce have a strategic partnership to deliver enterprise cloud solutions using Salesforce platforms. They have extensive experience implementing Salesforce solutions globally across many industries. Together, they help companies connect with customers, partners, and employees through dynamic IT that improves business performance and agility.
#IBMInsight session presentation "Orchestrating a Customer-Activated Supply Chain"
Assembling the pieces of a customer-activated supply chain involves activities on three dimensions: Sharpen visibility and insight, Partner for innovation, Become customer-activated
IBM supply chain analytics solutions to leverage Big Data
More at ibm.biz/BdEPRX
The document discusses CA's new Cloud-Connected Management Suite, which aims to help IT organizations manage their internal and external cloud resources. The suite will include products for gaining insight into existing IT services, composing applications to work across environments, optimizing sourcing decisions, and orchestrating deployments. It will leverage acquisitions and build on CA's existing portfolio. The suite aims to help IT organizations treat their resources as a dynamic supply chain and make optimal cloud sourcing choices.
Future of Work Enabler: Flexible Commercial ModelsCognizant
As companies adopt more flexible approaches to business process service delivery, they are also moving to new outcome-based payment models that support how businesses need to operate today. (An installment in our multipart series on the shifts necessary for future-proofing your company.)
Similar to Cloud, saas and analytics driven value chain business transformation version 1.1 (20)
GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
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Cloud, saas and analytics driven value chain business transformation version 1.1
1. Salil Amonkar
Value Chain Business Transformation
Industry Expert and Thought Leader
The convergence of Cloud, SaaS and
Machine Learning represents a new
opportunity to drive significant Business
Transformation in the Value Chain
Cloud, SaaS and Machine
Learning
for
Value Chain
Transformation
2. Contents
2 Title of the book
3
4
5
8
10
11
12
Note from the author
Fundamentals of Value Chain Best Practices
Impact of Cloud SaaS and Analytics on Value Chain
Characteristics of solutions for Value Chain Business
Transformation
Review of Market Place Solutions
Summary
References
3. Note from the author
3 Cloud, SaaS and Advanced Analytics for Value Chain Business
Transformation
Dear Readers,
It is my privilege to share with you insights from the business transformational experience
I have gained working over nearly three decades with various High-Tech companies in the
Silicon Valley, Discrete Manufacturing and Retail companies in Europe and Asia.
The pace of change brought forth by the disruptive effect of cloud and SaaS is occurring at
a much faster pace than that can be matched by enterprises. The high-tech industry has gone
through multiple transformations, from mainframe to PC to client server architecture during the
pre Y2K days, to the post-Y2K era of the Internet and now, with Cloud and SaaS. With this
evolution now data can be securely accessible anytime, anywhere by anyone. Expectations
around when, how and how much the customer should expect to pay for all of this has also
seen tremendous change.
Example of this change can be noted in how high tech companies are forced to react in
terms of their offerings and the monetization models that they need to focus on. An example of
this change in the value chain context is retail companies trying to rapidly build their
Omnichannel capabilities as they realize that driving a customer to a decision in their favor
rests on a brand’s ability to engage the customer on multiple fronts all with a consistent
customer experience. These and other dramatic shifts on the business fronts will drive most
enterprises to look at opportunities to ensure that their Value Chain processes are able to keep
up with this change.
In my role as Management Consultant focusing on leveraging Technology to drive Business
Transformation and having worked with several companies in implementing successful
business transformational initiatives I have gained a few insights that I would like to share with
all. I have used these insights in driving our own internal Cloud SaaS product design which has
been primarily based on the needs expressed by our customers that we see as remaining
unfulfilled.
My organization Pluto7 focuses on business transformation for Value Chain through Cloud
SaaS and Analytics and we have developed our own cloud-based supply chain management
subscription offering - Planning in a Box. Our collective experience has also contributed to
thoughts expressed in this paper.
- Salil Amonkar
4. Fundamentals of Value Chain Best Practices
The fundamentals of Value Chain Planning and Supply Chain Planning can be applied conceptually to
all industries. These are the four key areas namely Plan, Source, Make and Deliver, except that for
service supply chains the make and deliver is replaced by serve and deliver.
Many publications have been written by many thought leaders and experts on Top 10 Best Practices
for Value Chain and Supply Chain Management. After having read many of these and, coupled with
my experience of having successfully deployed these as solutions across multiple customers, the
following represents my personal view on how these apply to the above five areas:
1. An effective Value Chain is one where the corporation passes value on to the customer, with the
least possible cost to the business.
2. Plan is the starting point and driver of the Value Chain/Supply Chain Planning processes and
how this is managed has cascading impact on rest of the Value Chain. Best practices around
Plan process area have to do with Forecasting, Collaboration and Metrics.
3. Best practice(s) around Sourcing Operations are key to having the necessary flexibility to follow
demand changes while meeting lead times and cost constraints.
4. Make implementations across the industry have focused on best practices like Lean and Agile
Manufacturing and usage of Technology to drive automation in factory management and related
logistics.
5. Finally but not the least the effective external collaboration between partners, delivery
scheduling and streamlined logistics (forward and reverse logistics) are key to a good delivery
process. Cloud SaaS and Analytical solutions using Predictive Analytics and
Machine Learning provide a disruptive and significant potential
when used innovatively to drive Forecasting with high forecast
accuracy, easy collaboration and provide business real time data for
KPI management.
Similarly using Data Sciences and Machine Learning to determine
supply patterns can be used to achieve flexible sourcing operations
at lower cost.
Finally Machine Learning and Artificial Intelligence provide
capabilities to streamline Distribution and Logistics processes in a
way that has not been possible before.
Fundamentals of Value
Chain Best Practices
conceptually do not
change, what changes is
how technology advances
can be leveraged to
achieve the same
4 Cloud, SaaS and Advanced Analytics for Value Chain Business Transformation
5. Figuratively speaking Value Chain (Supply Chain) is the
infrastructure like combination of roads, train tracks, air
routes, sea routes , stations, airports etc. and Forecasting sets
the plan on how organizations will move traffic through these.
However as in case of real highway experiences that we go
through in everyday life; similarly weaknesses in forecasting
cause slow downs and impeded movements in the Supply
Chain.
The key to Forecasting is learning from past trends. At the
heart of machine learning is an algorithm that "learns" from
data which can be used to drive ever more accurate
predictors of demand which in turn will drive value chain
planning. This game-changing capability is possibly the
opportunity to reduce costs and reduce confusion. Using
Supply Chain jargon it can significantly help reduce the
impact of the “Bull Whip” effect which causes small demand
changes to have significant impact on back end supply chain.
Using artificial intelligence derived from machine
learning based models to establish a variation over the
normal baseline that is typically generated by today’s
forecasting models enable exception based demand
management leading to higher forecast accuracy. This is done
by ensuring that appropriate visualizations are provided that
clearly highlight the exceptions.
Using Cloud SaaS based approach to facilitate feeding of
multiple forecast input data feeds in a collaborative manner
helps enhance the forecast accuracy since more data and
more variations enable the machine learning models to be
even more effective.
5
Plan – Forecasting, Collaboration
& KPIs
Machine learning based Artificial Intelligence
models applied to forecast data and coupled
with appropriate visualizations that highlight
only those demand items that need to be
managed help drive forecast accuracy. It
provides the ability to react to quick changes
in demand that otherwise cannot be detected
by today’s conventional methods. Using KPI’s
such as Forecast Accuracy, Forecast Bias is
recommended.
Cloud, SaaS and Advanced Analytics for Value Chain Business
Transformation
Impact of Cloud SaaS and Analytics on Value Chain
Forecasting is
learning from
past trends.
Machine learning
with algorithms
that learn from
data can be used
to drive accurate
prediction of
Forecast which in
turn can drive
Value Chain
6. Impact of Cloud SaaS and Analytics on Value Chain
6 Cloud, SaaS and Advanced Analytics for Value Chain Business
Transformation
The Technology advances in Cloud SaaS and Advanced
Analytics based on Artificial Intelligence models based on
machine learning have significant impact on transforming the
Source, Make and Deliver Functions in the Value Chain.
While having a good Sourcing Strategy is an important
piece in the Supply Chain its operational implementation is
dependent upon significantly on usage of technology.
Effective collaboration is key for sourcing and effectiveness of
current on premise sourcing solutions is largely dependent
upon the integration of such solutions across enterprises.
Typically these have come at a cost resulting in not all
partners being able to leverage their functionality and thus
causing gaps in collaboration. Typical example of these are
gaps in consigned inventory, visibility of customer owned
inventory and so on.
Cloud SaaS solutions enable cost effective approach to
bridge these gaps. In addition the ability to use the large data
collected in the sourcing value chain and process it with
machine learning models enables the detection of trends that
can then be compared with demand management leading to
improved ability to react to changes and avoid costly
situations. This is done by not only leveraging the Cloud SaaS
Advanced Analytics solution to determine the trends but also
enable efficient and timely collaboration within all partners in
the Sourcing Value Chain through actionable information
available anywhere (i.e. desktop, mobile).
Cloud SaaS Advanced
Analytical solutions
not only improve
ability to react to
changes in demand
but also enable
efficient
collaboration within
Sourcing Partners
with actionable
information available
on desktop and
mobile
7. Impact of Cloud SaaS and Analytics on Value Chain
7 Cloud, SaaS and Advanced Analytics for Value Chain Business
Transformation
The Technology advances in Cloud SaaS and Advanced Analytics based on Artificial
Intelligence models combined with the developments in the internet of things and internet of
everything space is making the fully automated factory of the future a possible reality today.
IOE-IOT sensors sensing inputs from inventory in multiple locations such as in-transit,
consigned sites, supplier docks, receiving stores, supply carousels, distribution centers feeding
into the manufacturing execution systems, operations planning systems running machine
learning driven artificial intelligence algorithms can help manage optimized inventory levels.
Similarly taking sensor data from assembly lines and using machine learning to not only
achieve finely tuned statistical process control (quality control) but also predict potential
failures before they even occur thus eliminating potential for costly shut downs, rework and
obtain significant productivity on the shop floor is now possible.
Similar to sourcing which focuses more on inbound logistics Cloud SaaS solutions enable
cost effective approach to bridge the collaboration barrier between Manufacturing and
Distribution. By collaboratively collecting data from value chain and processing it with machine
learning models enables the detection of trends that links demand management, order
management, order fulfillment to logistics carriers and distribution centers leading to improved
ability to react to changes in value chain and avoid costly situations. One example of this is the
ability of such solutions to greatly simplify Omni Channel distribution.
Cloud SaaS Advanced
Analytical solutions are
the basis of creating the
Factories of the Future
today and Omni Channel
Retail Supply Chains
8. Characteristicsofsolutionsfor
ValueChainBusinessTransformation
8 Cloud, SaaS and Advanced Analytics for Value Chain Business
Transformation
Here are some key characteristics of transformational value
chain solutions.
Almost everyone of the solutions that I have been involved in
that have delivered innovation and transformational business
value has taken the crawl, walk and run approach.
Innovation requires out of the box thinking and also
challenging the norm and status quo. It is important to build
credibility in the key stakeholders to remove skepticism that
generally exists. It is important to also prove that the solution
meets its stated goals. The best way to do this is to pick a
important but tangible out-come to focus on as the initial scope of
the innovative solution and focus on accomplishing this. This is the
crawl part. Although we are crawling it is also very important to
keep this phase relatively short term in nature. Example of this is
when proving out the use of machine learning models for demand
forecasting, it may make sense to pick one product family where
issues have been observed with forecast accuracy or rapid demand
changes occurring. Taking the results of this proof of concept
phase lays the confidence building foundation to plan the
transition to most of the products thus leading to walk and run
phases.
Best practices on machine learning models recommends
usage of proving the model locally first by using training data and
then only moving it for the actual production usage. Taking the
same earlier example use actual data from selected product
families to train the machine learning algorithm, check the results
of the demand forecast for target products in the product family
with additional sample data, review the recommendations and
then once proven leverage the model for rest of the entire data set
Innovative
solutions require
crawl, walk and
run approach to
build credibility by
taking the
important crawl
step but in a very
rapid timeframe.
Best Practices
around machine
learning adaptive
models involve
training the
models locally and
iteratively
9. Characteristicsofsolutionsfor
ValueChainBusinessTransformation
9 Cloud, SaaS and Advanced Analytics for Value Chain Business
Transformation
Solutions should make it easy and economical to collaborate
within various partners involved in the business process.
Most of the current solutions do not make it easy to collaborate
within the various partners involved unless the partners are also
using same or almost similar solutions or integrations with their
associated costs are built between the solutions.
Cloud and SaaS solutions provide a easy way to get around this
problem by providing a low cost approach to get access to selective
information that has to be shared between partners. Simple
functions like capturing or viewing data can be easily provided at
very economical cost while maintaining security. Providing the ability
to access, input and view information on any media (laptop, smart
phones, iPads and similar mobile devices) and anywhere securely
changes the way collaboration takes place. Solutions are evolving
where a Supply Chain Operations controller is reviewing shortages of
key products on exception bases and is able to within the same
application either have an email, phone or chat conversation with all
the players in the Supply Chain to manage this on business real time
basis without having to leave their current user interface.
Exception based information delivery for Demand Supply
balance is a fundamental best practice to avoid delays in responding
to Supply Chain events that typically occur in today’s Supply Chains
due to data analysis paralysis, multitude of analytical tools that do
not match, visualizations that do not give business real-time data or
solutions whose business rules are not able to keep up with the rapid
changes in business scenarios in the Value Chain. The only way to
handle these situations is by leveraging the analytical capabilities
provided by Artificial Intelligence and Machine Learning based
solutions.
Cloud SaaS
solutions provide
the ability to
access, input and
view information
on any media
securely thus
significantly
changing the way
Value Chain
partners
collaborate with
each other.
This is further
accentuated by
exception based
analytical
solutions
powered by
machine learning
Example of Company who has achieved benefits Before
Predictive Commerce they had significant operational issues due to low
forecast accuracy as well as unexpected product requests from customers.
• They went through crawl walk run approach of using machine learning
for improving E-commerce product recommendations
• Followed this up by using machine learning to improve forecast accuracy
and supply chain operations
• Results
ü 8% ROI improvement over 1 year
ü Net Revenue uplift improved by 35% in 1 year
ü Lost sales reduced by 30% in 1 year
ü Forecast accuracy improved to 92% in 1 year
10. Current Solutions miss the mark on
Cloud SaaS and Advanced Analytics
Leading Edge Innovative
Solutions are now in play
The key statement that I encountered most
of the times that I have talked with Supply Chain
leaders, Business Operations
Managers/Directors/Planners is that while most
systems promise efficient planning in reality
exception based planning management becomes
a distant dream as they and their teams spend
time mostly on managing multiple
reports/dashboards, reconciliation of information
instead of focusing on business actions that are
needed. Even if this information comes in then it
is usually not business real-time and late in the
game.
Very few solutions are truly out there that
can effectively provide this capability although it
is my expectation such solutions will come up.
Some of these provide good solutions for
point solutions like Anaplan for Demand
Management and Financial Planning and Analysis,
Apttus for Quoting.
We took a note of this and came up with our
own Cloud and SaaS solution planninginabox.com
that is designed from ground up to solve the
above as well as facilitate the following:
Ability to easily integrate within existing
architectures while managing security with
multiple means of data input.
Ensure effective collaboration by enabling
the user to collaborate via email, chat, call on
either laptop, mobile devices while being in the
same user interface.
Have pre-built adaptors to process IOE-IOT
data , and have it drive the Advanced Analytics
with leading edge visualizations.
Finally but not the least leverage artificial
intelligence and machine learning for significant
business transformation.
8 Cloud, SaaS and Advanced Analytics for Value Chain Business Transformation
ReviewofMarketPlaceSolutions
Traditional solutions provided by Oracle,
SAP, IBM and highlighted by Analytical firms
like Gartner, Forrester although are well
established are constrained by the fact that
they are predominantly architected on the
on premise model and even though there is a
push by these companies to provide the
Cloud and SaaS based versions these and are
characterized by following:
They need significant integrations to
truly leverage information across multiple
organizations.
• Except for IBM which has its Watson
product none of them have significantly
demonstrated Artificial Learning and Machine
Learning capabilities that can be leveraged to
enhance Value Chain transformation.
• Although many of them provide robust
end to end capabilities within the enterprise
looking at the current state of such solutions
within the enterprise I have observed that
most of the times it results in users struggling
to get actionable information as a result of
static business rules that are typically
embedded in such solutions.
Some of the promising enterprise
solutions such as Anaplan while eliminating
most of the above issues still have gaps in
functionalities like ability to easily provide
processed information for use by other
solutions that can implement machine
learning capability on top of its data or have
weaknesses in the solutions that help
manage the back end of Supply Chain
Management while they have mastered the
solution for front end with their Demand
Planning and Forecasting, Financial Planning
and Analysis solution as an example.
11. Value chain Best Practices have developed over a long time and for
most purposes can be characterized into the following key areas:
Plan:
- Forecasting, Collaboration and Metrics
Source:
- Sourcing Operations
Make:
- Lean and Agile Manufacturing, Technology
Deliver:
- External Collaboration, Delivery Scheduling, Forward and Reverse Logistics
Fundamentals of Value Chain Best Practices conceptually do
not change, what changes is how technology advances can be
leveraged to achieve the same.
Cloud SaaS solutions provide the ability to access, input and
view information on any media securely thus significantly changing
the way Value Chain partners collaborate with each other. This is
further accentuated by exception based analytical solutions powered
by machine learning. This provides an opportunity to drive significant
business transformation in the Value Chain.
The need for this transition is crucial but it not easy. Many
complex offerings have not yet made that transition (Oracle, SAP) and
while many are emerging (Anaplan, Apttus) these do not cover all the
gaps which has led Pluto7 to offer our own Cloud based subscription
model of Supply Chain Analytics solution www.planninginabox.com
which addresses some of the business pains highlighted in this
paper.
Subscribe to our Blog : http://blog.pluto7.com
Summary
12. Following are the articles that have been referenced by the author
when creating this content
• Top 10 Supply Chain Best Practices – Multiple articles, Author’s experience, Pluto7 team
experience
• Forecasting best practices by BetterVu
• Machine Learning - A Giant Leap for Supply Chain Forecasting –Patrick Smith in 2015
• Democratizing Data Science through Automation by Data Informed
• Application of machine learning techniques for supply chain demand forecasting – Article
by Real Carbonneau, Kevin Laframboise, Rustam Vahidov, Concordia University
• Ultimate guide to machine learning by Apttus
• The Anaplan Platform explained by Anaplan
References