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1. Data Analytics in
Inventory Management
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customizing your presentaon.
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AVINASH (B200200EE)

2. • Introduction
• Inventory Management Overview
• Challenges in Inventory Management
• Data Analytics: A Solution
• Benefits of Data Analytics
• Data Analytics Types
• Data Sources
• Data Collection & Preprocessing
• Data Analysis Techniques
• Case Study
Overview
• Data Visualization
• Inventory Optimization
• Decision Support Systems
• Challenges & Future Trends
• Conclusion & Q&A

3. Data analytics helps businesses improve forecasting, reduce costs, and
operate more efficiently.
It involves analyzing large sets of data to make informed decisions.
Data analytics plays a crucial role in optimizing inventory management.
Introduction
Use this slide to set the stage for the
presentation by highlighting the
importance of data analytics in inventory
management.
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4. Effective inventory
management helps
businesses optimize costs,
minimize stockouts, and
maximize customer
satisfaction.
It plays a crucial role in
ensuring that the right
products are available at
the right time to meet
customer demand.
Inventory management is
the process of overseeing
and controlling the flow of
goods in and out of a
company.
Inventory Management Overview
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specific examples or statistics related to
inventory management in your industry.
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01 02 03

5. • Overstocking
• Understocking
• Stockouts
• Carrying costs
Challenges
Challenges in Inventory Management
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specific examples or case studies related
to each challenge.
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6. Data analytics optimizes
operations by identifying
opportunities for process
improvement and
streamlining inventory
management.
By analyzing historical sales
data, data analytics helps
identify slow-moving or
obsolete inventory,
reducing carrying costs.
Data analytics provides
insights into demand
patterns and customer
behavior, enabling accurate
forecasting and preventing
overstocking or
understocking.
Data Analytics: A Solution
When implementing data analytics in
inventory management, ensure you have
access to reliable and comprehensive data
sources to maximize the effectiveness of
your analysis.
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01 02 03

7. Efficient operations: Data analytics enables businesses to streamline their
inventory management processes and improve overall operational efficiency.
Cost reduction: By analyzing data, businesses can identify cost-saving
opportunities and minimize inventory holding costs.
Improved forecasting: Data analytics helps businesses accurately predict
demand and optimize inventory levels.
Benefits of Data Analytics
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specific examples and case studies related
to inventory management in your industry.
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8. Predictive Analytics
Descriptive analytics focuses
on understanding historical
data and providing insights into
what has happened in the past.
It involves summarizing and
visualizing data to gain a better
understanding of trends and
patterns.
Descriptive Analytics Diagnostic Analytics
Data Analytics Types
Diagnostic analytics aims to
determine the causes of past
events or outcomes. It involves
analyzing data to identify the
factors that contributed to a
particular result or event.
Diagnostic analytics helps
answer the question 'Why did it
happen?'
Predictive analytics uses
historical data and statistical
models to forecast future
outcomes or events. It involves
analyzing patterns and trends
to make predictions about what
is likely to happen in the future.
Predictive analytics helps
answer the question 'What is
likely to happen?'
You can customize this slide by adding
examples or case studies related to each
type of data analytics.
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9. 1 2 3
Internal tracking systems: Data
generated from internal
processes and systems, such
as order fulfillment and
production.
Supplier data: Information
provided by suppliers, such as
lead times and delivery
schedules.
Data Sources
1 2 3
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including specific examples of data
sources relevant to your industry or
organization.
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Point-of-sale data: Sales data
collected at the point of
purchase.

10. Data Collection & Preprocessing
• Accurate and comprehensive data collection is
crucial for effective inventory management.
• Data can be collected from various sources such
as point-of-sale systems, supplier data, and
internal tracking systems.
• Collecting data in real-time allows for timely
decision-making and proactive inventory
management.
Data Collection
• Data preprocessing involves cleaning and
transforming raw data into a usable format.
• Cleaning data involves removing errors,
duplicates, and inconsistencies.
• Data transformation includes converting data into
a standardized format and handling missing
values.
• Preprocessing ensures data quality and reliability
for accurate data analytics results.
Data Preprocessing
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Collection' column with specific examples
of data sources relevant to your industry or
business. In the 'Data Preprocessing'
column, explain any specific techniques or
tools used for cleaning and transforming
data in your organization.
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11. Data Analysis Techniques
Statistical analysis involves the use of mathematical models and
techniques to analyze inventory data and identify patterns, trends, and
relationships. It helps in making data-driven decisions and forecasting
future inventory needs.
Regression analysis is used to establish a relationship between dependent
and independent variables in inventory management. It helps in predicting
inventory levels based on factors such as sales data, customer demand,
and seasonality.
Statistical Analysis
Regression
Clustering
Clustering analysis is a technique that groups similar inventory items
together based on their attributes and characteristics. It helps in
identifying product categories, optimizing storage, and managing
inventory based on demand patterns. Customize the examples of data analysis
techniques based on specific industry or
business requirements.
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12. 1 2 3
Inventory optimization
leads to cost reduction
and improved customer
satisfaction.
It enables businesses to
optimize inventory levels
based on demand
forecasting.
Data analytics helps in
reducing inventory
carrying costs and
improving efficiency.
Inventory Optimization
1 2 3
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visually represent the impact of inventory
optimization on cost reduction and
efficiency.
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13. 1
2
3
By using decision support systems, businesses can make more
informed and data-driven decisions to improve their inventory
management processes.
They analyze large amounts of data to generate forecasts,
optimize inventory levels, and support decision-making.
Decision support systems leverage data analytics to provide
insights and recommendations for inventory management
decisions.
Decision Support Systems
1
2
3
When discussing decision support
systems, provide examples of specific
tools or software that can be used in
inventory management.
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14. • Lack of skilled personnel: Implementing data
analytics in inventory management requires
trained professionals who understand both data
analysis and inventory management principles.
• Data quality and availability: Poor data quality
and limited availability of relevant data can
hinder the effectiveness of data analytics in
inventory management.
• Integration with existing systems: Integrating
data analytics tools and processes with existing
inventory management systems can be complex
and time-consuming.
• Cost and infrastructure: Implementing data
analytics in inventory management may require
Challenges in Adopting Data Analytics
• Lack of skilled personnel: Implementing data
analytics in inventory management requires
trained professionals who understand both data
analysis and inventory management principles.
• Data quality and availability: Poor data quality
and limited availability of relevant data can
hinder the effectiveness of data analytics in
inventory management.
• Integration with existing systems: Integrating
data analytics tools and processes with existing
inventory management systems can be complex
and time-consuming.
• Cost and infrastructure: Implementing data
analytics in inventory management may require
Future Trends in Inventory Management
Challenges & Future Trends
Customize the challenges in adopting data
analytics based on the specific challenges
faced by your organization. Additionally,
stay updated on emerging technologies
and trends in inventory management to
leverage them for better inventory control.
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15. The future of inventory management lies in embracing data analytics and
leveraging emerging technologies.
It helps address common inventory management challenges and improves
forecasting.
Data analytics plays a crucial role in optimizing inventory management.
Conclusion
Summarize the key takeaways from the
presentation and encourage the audience
to ask questions to further clarify any
doubts they may have.
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