Manufacturing & Supply Chain Analytics: Advanced & Predictive. Production Analytics for Operational Efficiency. Supply Chain and Risk Management. Demand Planning & Forecasting. Cost & Overhead Tracking.

1. Manufacturing &
Supply Chain
Analytics Use Cases
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2. A World Economic Forum (WEF) and A.T. Kearney’s study of the future of
production find that manufacturers are evaluating how combining emerging
technologies including IoT, AI, and machine learning can improve asset
tracking accuracy, supply chain visibility, and inventory optimization.

3. The manufacturing
industry is undergoing a
lot of automation, cost
pressure is always high,
and margins are
depleting. Bringing in
efficiency and productivity
gain is important to
ensure competitiveness as
well as profitability.
Smart
Devices and
data StorageDigitising
Information
Do more
with your
Data Analytics
Increase Analytics
Adoption
Steps to Achieve that:

4. Machinery and systems are constantly operating
for long stretches under heavy load and any fault
can significantly impact your production.
Production Analytics for
Operational Efficiency:
A reactive approach is not sustainable, using
Analytics, factory supervisors can predict failures
in advance and avoid the downtime.

5. Advanced analytics in manufacturing
maximizes operational efficiency
through three key applications:
01
02
03Predictive Maintenance
via anticipating failures
Yield/throughput
Maximize yield/throughput of
individual assets, by optimizing
working parameters based on big
data analysis
Optimization with Advanced
Modeling
Dynamically define optimal setup
point(e.g., sales mix, value allocation,
procurement mix ) to maximize
profit/hour optimization

6. IoT data from sensors
can be pulled and analysed to understand the
pain areas and help in improving machine efficiency
Analysis of returned items provide insights related to
which stage of the production process is generating the
maximum volume of faulty items
Machine utilisation and effectiveness
data can highlight root-cause for any deviations like
human-error, raw-material scarcity, technical issues, etc.
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7. Greater visibility into supplier quality
levels and their other performance metrics,
manufacturers can identify the best vendor.
Supply Chain Analytics
& Risk Management:
Supplier dependencies are quantifiable and with timely
analysis of this information, the manufacturer can make
fact-based decisions for strategic risk management.
Collaboration among manufacturers and
supplier is key in today’s scenario, it leads to:

8. By combining existing data with predictive analytics to build
a more precise projection of what purchasing trends will be,
manufacturers can gain significant competitive advantage.
Capacity Planning: Using analytics solutions, decision makers can define
an optimal no. of units they should manufacture over a specified period,
taking into consideration capacity, sales forecasts, and parallel schedules.
Demand Planning & Forecasting
Demand forecasting guides a production chain and can be the difference
between strong sales or a warehouse full of unpurchased inventory.

9. Cost & Overhead tracking with Analytics:
A BI system can use the available data on primary
costs and the production unit's information to provide
insights to reduce the risk and suggest timely corrections
To have real control and visibility over these costs, connected
data sources and advanced analytics capabilities are
needed. Labour cost forms a major chunk of overhead costs.
Employee badges can be tracked with sensors placed on
the shop floor. This data can be analysed to assign the exact
cost of each task in a process, broken down to an
individual’s level.
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Keeping a track on the cost per unit of the item is important
for a production manager as it impacts the pricing decisions
and promotions as well.
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10. THANK YOU!
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Email: rishabh.rai@polestarllp.com