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Analytics to Unlock Operational
Value
Service Offerings
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Opportunities at the intersections of functions
Amidst pressures of daily operations, it is a challenge to identify and act on
value enhancement opportunities at the intersection of functions
Illustrative
Buy
Convert
Product – plant market – logistics
decisions
Sell
CUSTOMERS
SUPPLIERS
Choice of raw
material
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Typical opportunity at interface between Sales - Operations
Typical business challenge: What basket of products to make to maximize
contribution in a fluctuating market
Copper Example
Recovery
Ratios
Demand
Constraints
Output
Capability
Feedstock
Composition
Capacity
Constraints
• Generic Linear Programming Model
Market Price
Output Saleability
• Maximize ∑ Contribution across
Contribution
all product-market-plant
combinations
Conversion
Costs
• Subject to
• Yields or recoveries achievable,
based on production technology
in use at the plant
• Capacity constraints for each
relevant production facility and
transportation mode
• Demand constraints for each
market
• Generic model would need to be
customized and detailed for each
business
Typical opportunity at interface between Sales - Operations –
Outbound Logistics Planning
Typical business challenge: What products to make at which plant and to ship to
which market by what mode of transportation to maximize contribution
• Generic Linear Programming Model
• Maximize ∑ Contribution across all product-marketplant combinations (taking into account all direct costs,
Sales
Production
Planning
including transportation costs)
• Subject to
• Demand constraints for each market
• Order shipment sizes for each customer (if relevant)
and / or transportation mode
Outbound
Logistics
• Capacity constraints for each relevant production
facility and transportation mode
• Minimum and maximum run lengths, if relevant
• Minimum and maximum load sizes, if relevant
• Yields or recoveries achievable, based on production
technology in use at the plant
What products to make at which plant and
to ship to which market by what mode of
transport to maximize contribution
• Could have added complexity of servicing customers from
warehouses / transshipment points
• Generic model would need to be customized and detailed
for each business
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Typical opportunity at interface between Sales - Operations – Inter
Plant Logistics Planning
Typical business challenge: What products to process to what stage of
processing at each plant to maximize contribution across the business
Aluminium Products Example
Market
Demand &
Conversion
Premium
Plant
Plant Capacities
Capabilities
and Costs
• Generic linear programming model
• Maximize Σ(Earnings – ΣCost across
various stages of processing) for all
Possible Paths
(for an order to
be processed)
Optimal
Loading &
Contribution
Other
Considerations *
products across all plants
• Subject to
• Demand being met
• Capacity being available
• Other considerations (scrap
consumption, material balancing,
customer line certification /
preferences etc.)
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Typical opportunity at interface between Sales - Production Planning –
Operations – Procurement
Typical business challenge: What feedstock maximizes total contribution across
all products and by products
Max Contribution from a Feedstock :
Linear Programming Model
Feedstock
Procurement
Production
Planning
Operations
Sales
• Composition
• Cost
• Demand
• Capacity – yield
• Contribution
• Technology
• Conversion
costs
• Demand
• Price
Maximize ∑ Contribution across all
product-market-plant combinations
Plot for
multiple
feedstocks
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Typical opportunity at interface between Sales and Production
Typical business challenge: Optimal production run length in the face of demand
fluctuations and capacity constraints
Phosphates Production Planning Example
• Generic linear programming model
• Minimize plant idle capacity
• Subject to
• Demand being met
• Capacity constraints
• Inventory build up being within
norms
• Based on
• Capacity requirement of different
shared resources (throughputs) of
different products
Typical opportunity at interface between Outbound Logistics
Planning and Sales
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Typical business challenge: Optimal FG inventory to be held at each node of the
distribution network
What-if Simulation Model(s) combining human
judgment with mathematical modeling capabilities
Total FG inventory (Working
Capital Locked Up) in the
System
Lost Sales / Delays in Order
Servicing due to Stock Outs
Optimal Balance
Between
Minimizing
Transportation Costs
In case of Fashion Retail,
Markdowns Due to Missed
Seasons or Trends
Typical opportunity at interface between Operations and
Technical Support Services
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Typical business challenge: What mix of coal to use in which boilers to run which
turbines for how long to generate required quantum of electricity at lowest cost
• Given captive power plant
capacity > demand, how to
balance load to minimize cost
of power consumed
• Minimize cost of power
consumed across captive
generation and grid
• Subject to
• Steam (at different pressures)
+ Power demand being met
• Capacity constraints
• Based on
• Calorific value of different
types of coal
• Boiler efficiencies
• Turbine productivity
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Typical opportunity between Procurement and
Commercial
Typical business challenge: Whether to stock up or liquidate stocks of a highly
price volatile input material
Phosphoric Acid Price
Prediction Example
• Predicting the price of
Phosphoric Acid (commodity
common raw material) based
on 2 different PA production
technologies
• Regression analysis to
develop correlation
• Prediction model to
forecast prices
• Used monthly to decide
whether to stock up or
liquidate stocks of PA, based
on whether prices are forecast
to move up or down
Typical opportunity at interface between Spares Procurement and
Maintenance / RM Procurement and Production Planning
Typical business challenge: Optimum inventory holding, without exposing plant
to stock outs
What items to procure centrally vs distributed procurement
What items to hold centrally vs distributed (more relevant for spare parts)
Reorder levels, reorder quantities etc
ABC Inventory Classification
XYZ Inventory Classification
VEA Inventory Classification
A : High volume consumption
X : High value stock holding
V : Vital to plant operations
B : Medium volume
consumption
Y : Medium value stock
holding
E : Essential for plant
operations
C : Low volume consumption
Z : Low value stock holding
A : Auxiliary to plant
operations
• Determine inventory holding and procurement policies for each combination
• Determine reorder levels and reorder quantities by analyzing consumption
patterns, economic order quantities, lead time for delivery by suppliers, buffer
stock requirements to offset supply uncertainties
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Typical opportunity across Procurement-Production-Sales-Distribution
• Typical business challenge: How to minimize the cash to cash cycle
• Study of supply chain process for issue identification and redesign
opportunities (e.g. functional overlays highlight conflicting functional objectives,
rework loops highlight process deficiencies etc.) to reduce total cycle time
Illustrative Supply Chain Process Map
S
U
P
P
LI
E
R
Purchase RM,
PM
Receive
RM, PM
Produce
Store RM,
PM
Issue RM,
PM
PPC
Manage
WIP
Ship
Deliver to
W/H
W/H
Prep del.
doc.
Test quality
Stores Function
Procurement
Function
Production Function
Stores and Logistics
Function
Note: Flow depiction, resources, controls removed for simplification
Invoic
e
Collect
orders
Pay for RM,
PM
Collect
outstandings
Accounts
Sales
C
U
S
T
O
M
E
R
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Typical opportunity across Procurement-Production-Sales-Distribution
• Typical business challenge: How to minimize the cash to cash cycle
Spinning & Weaving Unit Example
• Identification of critical supply
chain processes and their
business imperatives
• Analysis of current state and
redesign to address business
imperatives
• Identification of projects,
responsibility allocation and
program implementation and
governance
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Summary of Potential Service Offerings
• Operational decision support system
• Product mix decisions
• Production planning for single plant, multi-plant
• Transportation logistics planning (mode and route)
• Procurement timing (stock up or liquidate based on price forecasts)
• Feedstock blend composition / procurement
• Captive power generation modality
• Stores and spares inventory holding policies
• Cash to cash process design
• Strategic decision support system
• Capacity planning given a product mix and demand estimate
• Product mix planning given a capacity and pricing-costing estimate
• Distribution network planning
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Illustrative Tools Used in Such Model Building
• Operations Research Models
• Linear Programming Models
• Goal Programming Models
• Analytical Tools
• Regression Analysis
• Trend Analysis / Curve Fitment
• Process mapping and analysis
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Thank you
Contact details : Ajita Kini : Cellphone +91 98195 99412; [email protected]