2. TABLE OF CONTENTS
1. INTRODUCTION
2. PROBLEM STATEMENT
3. PROBLEM WITH THE EXISTING PROCESS
4. OBJECTIVES
5. EXISTING AUTOMATED SYSTEMS
3. INTRODUCTION
In the pursuit of operational excellence, this
warehouse automation analytics project
employs advanced data analytics to
optimize warehouse processes. Through
detailed analysis of historical and real-time
data, the project enhances resource
allocation, mitigates bottlenecks, and
improves inventory management.
Leveraging predictive maintenance models
and real-time dashboards, the project
achieves significant reductions in
downtime, higher order fulfilment rates, and
improved overall efficiency.
4. PROBLEM
STATEMENT
The warehouse operations face challenges related
to inefficiencies, suboptimal resource utilization,
inventory management, and maintenance. These
challenges lead to bottlenecks, increased
downtime, unpredictable inventory levels, and
compromised operational efficiency. To address
these issues, the project aims to leverage
advanced data analytics to identify bottlenecks,
optimize resource allocation, enhance inventory
management, implement predictive maintenance,
and provide stakeholders with real-time operational
insights. The overarching goal is to streamline
processes, reduce downtime, minimize stockouts,
and improve overall warehouse performance for
sustained competitiveness.
5. PROBLEM WITH THE EXISTING PROCESS
INEFFICIENT
RESOURCE
ALLOCATION
INVENTORY
IMBALANCES
Without data-driven insights,
warehouses might struggle to
allocate human and automated
resources optimally, leading to
overutilization or underutilization of
assets.
Lack of analytics can result in
inaccurate inventory management,
leading to stockouts, excess
inventory, and increased carrying
costs.
Without analytics, it's difficult to
identify bottlenecks or
inefficiencies in processes, which
can hinder workflow and lead to
delays in order fulfilment.
UNIDENTIFIED
BOTTLENECKS
6. PREDICTIVE
MAINTENANCE
CHALLENGES
LACK OF REAL-TIME
INSIGHTS
Equipment downtime can be
frequent and unpredictable without
predictive maintenance models,
leading to increased maintenance
costs and reduced operational
efficiency.
The absence of real-time reporting
and dashboards can limit
managers' ability to make
informed decisions during critical
operational moments.
Without data analysis, warehouses
may struggle to accurately predict
future demand, resulting in
inefficient procurement and
production planning.
INACCURATE DEMAND
FORECASTING
7. REDUCED CUSTOMER
SATISFACTION
INEFFECTIVE QUALITY
CONTROL
Inaccurate inventory levels and
delayed order processing can lead
to stockouts and late deliveries,
negatively affecting customer
satisfaction.
Without analytics, identifying
quality issues in real-time
becomes challenging, potentially
leading to increased returns and
customer complaints.
Warehouses may struggle to
adapt to changing market trends
and customer preferences without
the insights gained from analysing
data.
LIMITED
ADAPTABILITY
8. INVENTORY MANAGEMENT
OBJECTIVES
Implement data-driven strategies for
optimal inventory management, reducing
stockouts, and minimizing excess stock.
This approach uses data analysis to
identify demand patterns and historical
sales, guiding decisions on reorder
points, safety stock levels, and
replenishment schedules. The goal is to
prevent missed sales due to stockouts
and reduce the financial burden of
excess inventory.
Analyzing warehouse data helps
identify bottlenecks and inefficiencies.
By scrutinizing data from various
sources, we pinpoint workflow
slowdowns caused by inadequate
resources or suboptimal processes.
We also uncover redundant steps and
resource wastage. The goal is to find
opportunities for improvement,
streamlining operations and enhancing
overall efficiency.
EFFICIENCY ENHANCEMENT
9. PREDICTIVE
MAINTENANCE
Develop predictive maintenance models
using sensor and monitoring data to
anticipate maintenance needs for
automated systems. By analyzing
historical performance and wear
patterns, these models predict issues
before they cause equipment failures.
This proactive approach reduces
unplanned downtime, allowing for
scheduled maintenance during planned
downtime, ultimately enhancing
equipment efficiency and warehouse
productivity.
Efficiently allocate human and robotic
resources using data analytics for
tasks and shifts in the warehouse.
Analyze workload, peak activity times,
and resource capabilities to create
optimized schedules and assignments.
This approach maximizes productivity,
minimizes resource overuse, and
ensures timely task completion,
enhancing overall warehouse
efficiency.
EFFICIENCY ENHANCEMENT
10. Efficiently allocate human and robotic
resources using data analytics for
tasks and shifts in the warehouse.
Analyze workload, peak activity times,
and resource capabilities to create
optimized schedules and assignments.
This approach maximizes productivity,
minimizes resource overuse, and
ensures timely task completion,
enhancing overall warehouse
efficiency.
OPERATIONAL INSIGHTS
11. EXISTING AUTOMATED SYSTEMS
Tableau for Warehouse Analytics
SAP Extended Warehouse Management (EWM) Dashboard
Oracle Warehouse Management Analytics
Microsoft Power BI for Supply Chain
Qlik Sense for Logistics and Warehousing
WiseTech Global CargoWise One
IBM Cognos Analytics for Supply Chain
12. CONLCLUSION
To sum up, effective warehouse management is crucial for overall business success. The
warehouse automation analytics project, driven by advanced data analytics, has shown
great potential in revolutionizing operations. It addresses bottlenecks, optimizes resource
allocation, improves inventory management, and implements predictive maintenance,
boosting efficiency and reducing downtime.
Real-time dashboards and historical reports offer valuable insights into KPIs and trends,
empowering agile decision-making.
This project underscores the significance of data-driven decisions, adapting to technology,
and optimizing processes to stay competitive in today's evolving business landscape. Its
success points to a future where analytics-driven strategies reshape warehousing for
greater efficiency, customer satisfaction, and operational excellence.
