ERP technology Areas.pptx

CONFIDENTIAL & RESTRICTED
ERP
TECHNOLOG
Y AREAS
ASSISTANT
PROFESSOR:-
NIVEDITA PANDEY

ENTERPRISE APPLICATIONS PORTAL AND CONTENT
MANAGEMENT
• Enterprise Applications Portal and Content Management (EPCM) is a broad topic encompassing
strategies, methods, and software tools to capture, manage, store, preserve, and deliver information and
documents related to an organization's processes. It plays a crucial role in knowledge sharing,
information search, and ultimately, organizational growth.
Enterprise Applications Portal:
• Think of it as a single point of access (web-based) for employees, customers, suppliers, or any relevant
audience.
• It integrates information, people, and processes from various sources across the organization.
• It personalizes content and provides secure access to applications and tools.
• Examples include Microsoft SharePoint, Liferay Portlet, and Oracle WebLogic Portal.

Content Management System (CMS):
• Handles the creation, editing, publishing, and delivery of content
(text, images, videos, etc.) on websites, portals, and other platforms.
• Offers user-friendly tools for non-technical users to manage content without
relying on IT.
• Enables version control, workflow management, and collaboration features.
• Popular CMS options include WordPress, Drupal, Joomla, and Adobe Experience
Manager.

Benefits of EPCM:
• Improved Efficiency: Streamlines access to information and tools, leading to faster decision-making
and increased productivity.
• Enhanced Collaboration: Provides a platform for knowledge sharing and team
communication, fostering a more connected workforce.
• Cost Reduction: Eliminates paper-based processes and reduces IT reliance for content
management, saving time and resources.
• Greater Compliance: Ensures controlled access to sensitive information and simplifies adherence to
regulations.
• Improved Customer Experience: Offers a personalized portal for better communication and
engagement with external stakeholders.

• Key Considerations:
• Target Audience: Define who will use the EPCM system and tailor features and
content accordingly.
• Integration: Ensure seamless integration with existing enterprise applications and
data sources.
• Security: Implement robust security measures to protect sensitive information.
• Scalability: Choose a system that can adapt to your organization's future growth.

DATA WAREHOUSING

DATA WAREHOUSING
• Data Warehouse:
• A data warehouse is a large, centralized repository of integrated data from
various sources.
• It is designed for query and analysis rather than transaction processing.
• Data warehouses are typically used to support business intelligence (BI) activities
such as reporting, online analytical processing (OLAP), and data mining.

ETL Process:
• ETL stands for Extract, Transform, and Load, which are the three key steps in the
data warehousing process.
• Extract: Data is gathered from various sources, such as transactional databases,
flat files, or external systems.
• Transform: Data is cleaned, transformed, and integrated into a consistent format
to ensure accuracy and uniformity.
• Load: The transformed data is loaded into the data warehouse.

Data Mart:
• A data mart is a subset of a data warehouse that is focused on a specific
business function or topic.
• Data marts are often created to provide more targeted and efficient access to
data for specific departments or teams within an organization.

Star Schema and Snowflake Schema:
• In data warehousing, data is often organized using star and snowflake schema
models.
• Star Schema: A central fact table is connected to one or more dimension tables
in a star-like structure.
• Snowflake Schema: Similar to the star schema, but dimension tables are
normalized into multiple related tables.

Data Warehouse Architecture:
• Data warehouse architecture can be divided into three main tiers:
• Bottom Tier (Data Source): Where data is sourced from various systems and
databases.
• Middle Tier (ETL Process): Where data is transformed and loaded into the data
warehouse.
• Top Tier (Data Warehouse): Where data is stored and made available for querying
and analysis.

• OLAP and Data Mining:
• OLAP (Online Analytical Processing) tools are used for multidimensional analysis
of data stored in a data warehouse.
• Data mining involves discovering patterns and relationships in data to gain
insights and make predictions.
• Data warehousing plays a crucial role in helping organizations make informed
decisions by providing a consolidated and structured view of their data. It enables
users to analyze historical data, track trends, and gain a better understanding of
business performance.

DATA MINING
Data mining, also known as knowledge discovery in databases (KDD), is the process of
uncovering hidden patterns, trends, and insights from large datasets. It's like sifting through a vast
digital treasure trove to find precious nuggets of information that can help organizations make
better decisions, solve problems, and gain a competitive edge.
Here's a closer look at what data mining entails:
What data gets mined?
•Huge datasets: Think mountains of information from customer transactions, website clicks, social
media posts, sensor readings, financial records, and more.
What patterns are uncovered?
•Trends: What products are customers buying together? When are website visits highest?
•Correlations: Is there a link between weather patterns and electricity consumption? Do certain
medical conditions share similar genes?
•Clusters: Can customers be grouped by similar buying habits? Are there hidden segments within
your audience?
•Outliers: Can fraudulent transactions be identified based on unusual spending patterns?

How does it work?
•Data preparation: Cleaning, formatting, and organizing the massive dataset.
•Model selection: Choosing the right data mining technique (e.g., classification,
clustering, regression) based on the desired insights.
•Model training: Feeding the data through the chosen algorithm to learn patterns
and relationships.
•Evaluation and testing: Analyzing the model's results and refining it for accuracy
and interpretability.
•Deployment and insights: Using the model to generate actionable insights for
decision-making, forecasting, and optimization.

Benefits of data mining:
•Improved decision-making: Data-driven insights can inform marketing campaigns,
product development, resource allocation, and more.
•Fraud detection and risk management: Identifying suspicious patterns can prevent
financial losses and protect sensitive information.
•Customer segmentation and targeting: Personalizing your approach to different
customer groups for better engagement and marketing success.
•Scientific discovery and innovation: Uncovering hidden relationships in scientific
data can lead to breakthroughs in medicine, materials science, and other fields.

Challenges of data mining:
•Data quality: Dirty or incomplete data can lead to misleading insights.
•Model selection and interpretation: Choosing the right algorithm and understanding
its results can be complex.
•Privacy concerns: Balancing data-driven insights with the ethical use of personal
information is crucial.
Ready to explore further?
I can provide you with detailed information on specific data mining techniques,
applications in different industries, popular tools and software, or ethical
considerations. Just let me know what interests you the most, and I'll be your guide
on this fascinating data mining journey!

DATA MINING
ARCHITECTUR
E

BUSINESS INTELLIGENCE AND ANALYTICS
Business Intelligence (BI) and Analytics (BA): A World of Data-Driven Insights
• Imagine navigating a vast ocean of business data, not with oars and sails, but
with powerful searchlights and precision instruments. That's the essence of
Business Intelligence (BI) and Analytics (BA). They equip businesses with the
tools and capabilities to transform raw data into actionable insights that guide
strategic decisions, optimize operations, and drive growth.

BI and BA are closely intertwined, yet distinct facets of this data-driven world:
• BI focuses on the present and the past: It provides clear visuals and
reports, dashboards and scorecards, showcasing how your business is currently
performing and has performed historically. Think of it as your rearview mirror and
odometer, keeping you informed about your journey so far.
• BA delves into the future: It employs sophisticated statistical models and
predictive algorithms to forecast trends, identify potential risks and
opportunities, and recommend data-driven actions. Think of it as your
GPS, charting your optimal course based on past performances and future
possibilities.

• Make informed decisions: Backed by data-driven insights, you can confidently
allocate resources, launch effective marketing campaigns, and optimize product
offerings.
• Identify inefficiencies: BI can pinpoint areas of performance weakness, while BA
can predict potential bottlenecks and suggest proactive solutions.
• Gain a competitive edge: By understanding your customers better and reacting
swiftly to market changes, you can outmaneuver your competitors.
• Boost overall performance: Data-driven decision-making leads to improved
operational efficiency, cost reduction, and ultimately, increased profitability.

The BI and BA landscape is constantly evolving, with exciting trends like:
• Democratization of data: Tools are becoming more user-friendly, giving wider
access to valuable insights across organizational levels.
• Integration of AI and machine learning: Advanced algorithms are automating data
analysis and generating even deeper insights.
• Real-time analytics: Businesses are gaining the ability to analyze data as it
happens, enabling swift and informed reactions to market fluctuations.

BUSINESS INTELLIGENCE (BI):
1. Data Integration:BI systems often involve the integration of data from various sources, such as databases, spreadsheets, and external
systems, to create a unified and consistent view.
2. Data Warehousing:As mentioned earlier, data warehousing is a core component of BI, providing a centralized repository of integrated
data that can be analyzed and reported.
3. Reporting:Reporting tools in BI allow users to create and generate reports based on the data stored in the data warehouse. These
reports can take the form of tables, charts, graphs, and dashboards.
4. Dashboards:Dashboards provide a visual representation of key performance indicators (KPIs) and other relevant metrics, allowing
users to quickly grasp the overall health and performance of the business.
5. Ad Hoc Querying:BI systems often enable users to perform ad hoc queries, allowing them to explore and analyze data on the fly
without relying on pre-defined reports.
6. OLAP (Online Analytical Processing):OLAP tools enable multidimensional analysis of data, providing users with a more interactive
and dynamic way to explore relationships and trends.
7. Data Visualization:Data visualization tools help transform complex data sets into easily understandable visuals, such as charts, graphs,
and maps, making it easier for users to interpret and analyze information.
8. Predictive Analytics:BI can leverage predictive analytics to forecast future trends and outcomes based on historical data, helping
organizations make more informed decisions.

ANALYTICS
• Analytics, in a broader sense, involves the discovery, interpretation, and communication of meaningful patterns in data. It encompasses
various approaches and techniques to analyze and interpret data, gaining insights and supporting decision-making. Here are some key
aspects of analytics:
1. Descriptive Analytics:Descriptive analytics focuses on summarizing historical data to understand what has happened in the past. It
involves the analysis of patterns, trends, and key performance indicators. Diagnostic Analytics:Diagnostic analytics goes beyond
describing what happened and aims to identify why certain events occurred. It involves analyzing the root causes of issues or
successes.
2. Predictive Analytics:Predictive analytics involves using statistical algorithms and machine learning techniques to forecast future
outcomes. It helps organizations anticipate trends and make proactive decisions.
3. Prescriptive Analytics:Prescriptive analytics takes the analysis a step further by recommending actions to optimize outcomes. It
provides guidance on the best course of action based on the data analysis.
4. Big Data Analytics:Big Data analytics deals with the analysis of large and complex datasets that traditional data processing tools may
struggle to handle. It often involves technologies like Hadoop and Spark.
5. Text Analytics and Sentiment Analysis:Text analytics involves extracting insights from unstructured text data, such as social media
comments or customer reviews. Sentiment analysis determines the sentiment expressed in the text, such as positive, negative, or
neutral.
6. Business Analytics vs. Advanced Analytics:
1. Business analytics is a broader term that includes various analytical approaches applied to business data. Advanced analytics

EMERGING TRENDS IN ERP APPLICATIONS
• Cloud-Based ERP:
• Cloud-based ERP solutions are becoming increasingly popular, offering flexibility,
scalability, and accessibility. Cloud ERP allows businesses to access their
systems from anywhere, reduces the need for extensive on-premises
infrastructure, and often provides automatic updates.

• AI and Machine Learning Integration:
• The integration of artificial intelligence (AI) and machine learning (ML) into ERP
applications is on the rise. These technologies enhance automation, data
analysis, and decision-making processes. AI can optimize workflows, predict
demand, and provide intelligent insights to users.

• IoT (Internet of Things) Integration:
• ERP systems are incorporating IoT to gather real-time data from connected
devices. This integration enables businesses to monitor and manage physical
assets, track inventory, and improve overall operational efficiency.

1.Blockchain for Enhanced Security:
1. Blockchain technology is being explored to enhance security and transparency in
ERP applications. It can be used for secure and transparent record-keeping,
especially in areas like supply chain management and financial transactions.
2.Mobile ERP:
1. Mobile ERP applications are gaining traction, allowing users to access critical
business information and perform tasks on the go. Mobile ERP enhances flexibility
and ensures that users can stay connected to business processes from their
smartphones or tablets.

1.Focus on User Experience (UX):
1. ERP vendors are placing a greater emphasis on improving the user experience.
Modern ERP systems aim to be more intuitive, with user-friendly interfaces,
streamlined workflows, and personalized dashboards to enhance user adoption.
2.Edge Computing in ERP:
1. Edge computing involves processing data closer to the source of generation, reducing
latency and enhancing real-time processing capabilities. In ERP applications, edge
computing can be beneficial for scenarios where immediate data processing is
critical, such as in manufacturing environments.

1.Robotic Process Automation (RPA):
1. Robotic Process Automation is being integrated into ERP systems to automate
routine, rule-based tasks. This can improve efficiency, reduce errors, and free up
human resources for more strategic and complex activities.
2.Hybrid ERP Deployments:
1. Some organizations are opting for hybrid ERP solutions, combining on-premises and
cloud-based deployments. This approach allows businesses to leverage the benefits
of both environments while addressing specific needs and concerns, such as data
security or compliance.

1.Industry-Specific ERP Solutions:
1. ERP vendors are increasingly developing industry-specific solutions tailored to the
unique needs of different sectors. This trend allows businesses to adopt ERP systems
that align more closely with their specific processes and requirements.
2.Focus on Sustainability:
1. ERP systems are incorporating features to help businesses monitor and manage their
environmental impact. This includes functionalities related to sustainability reporting,
resource optimization, and supply chain transparency.

• Collaboration and Integration with Other Software:
• ERP applications are evolving to foster better collaboration by integrating
seamlessly with other business software, such as CRM (Customer Relationship
Management) systems, HR software, and business intelligence tools.

SAP ERP IMPLEMENTATION AT NESTLÉ:
ORACLE ERP IMPLEMENTATION AT LENOVO:
• Industry: Food and Beverage
• Background: Nestlé, a global food and
beverage company, implemented SAP ERP
to streamline its business processes,
enhance efficiency, and gain better visibility
into its supply chain.
• Results: The SAP implementation enabled
Nestlé to standardize and integrate its
operations worldwide, leading to improved
collaboration, reduced inventory costs, and
enhanced decision-making capabilities
• Industry: Technology (Computer Hardware)
• Background: Lenovo, a multinational
technology company, adopted Oracle ERP
to consolidate its financial systems,
optimize procurement processes, and
support global expansion.
• Results: The Oracle ERP implementation
helped Lenovo achieve greater financial
transparency, improve order accuracy, and
enhance overall efficiency in managing its
supply chain and financial operations.

MICROSOFT DYNAMICS ERP AT TAYLORMADE
GOLF:
INFOR ERP IMPLEMENTATION AT KOCH
INDUSTRIES:
• Industry: Manufacturing (Golf Equipment)
• Background: TaylorMade, a leading golf
equipment manufacturer, implemented
Microsoft Dynamics ERP to streamline its
manufacturing processes, improve
inventory management, and enhance
customer service.
• Results: The ERP solution provided
TaylorMade with real-time visibility into
production processes, enabling better
demand forecasting, reduced lead times,
and increased overall operational efficiency.
• Industry: Diversified (Oil & Gas,
Chemicals, Manufacturing)
• Background: Koch Industries, a diversified
conglomerate, adopted Infor ERP to
integrate its business processes across
various subsidiaries, improve financial
reporting, and enhance decision support.
• Results: The Infor ERP implementation
helped Koch Industries achieve
standardized processes, improved data
accuracy, and better financial visibility
across its diverse business units.

EPICOR ERP AT AEROBIE:
• Industry: Manufacturing (Outdoor Sporting
Goods)
• Background: Aerobie, a manufacturer of
outdoor sporting goods, implemented Epicor
ERP to streamline its manufacturing
operations, enhance inventory control, and
improve overall business agility.
• Results: The Epicor ERP implementation
enabled Aerobie to achieve better demand
planning, reduce lead times, and enhance
collaboration between different departments,
leading to increased efficiency and customer
satisfaction.
• These case studies showcase how ERP
implementations have helped organizations
across various industries achieve operational
excellence, improve decision-making, and drive
business growth. It's important to note that
successful ERP implementations often involve
careful planning, stakeholder engagement, and
a focus on aligning the ERP solution with the
specific needs and goals of the organization.

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