INVENTORY
METHODOLOGY
© Barloworld Supply Chain Software 2014
Inventory optimization
essentials
Exception metrics for highlighting potential
availability and coverage issues
Projections that drive to the
inventory cover targets
Dynamic calculation of the optimum
inventory unit levels
User interface for capturing inventory
cover and availability targets
© Barloworld Supply Chain Software 2014
2
Primary inventory
categories
MAKE / BUY TO ORDER:
• Customer demand is firm for the full sourcing lead-time cycle OR
• Supply point stocks and can supply to lead-times within customer promise date period
• Any investment in inventory is for a customer.
• Model inventory cover = customer orders in supply lead-time.
MAKE / BUY TO STOCK:
• Customer demand is not firm for the full sourcing lead-time cycle OR
• Supply point does not supply to lead-times within customer promise date period
• An investment in inventory is expected, covering the
• Order release cycle strategy on a supplier
• Unexpected variations in
• Customer demand and / or
• Supply point lead-times
• Model inventory cover = cycle cover strategy + safety stock
© Barloworld Supply Chain Software 2014
Inventory cover for 1 SKU,
or many SKU’s
The model cover for all SKU’s
should meet or exceed
financial coverage targets
Average / Model
Cover
Target
Target
RC
RC
Safety Stock
RC = Replenishment Cycle (order cycle strategy)
4
© Barloworld Supply Chain Software 2014
Balancing safety stock cover
and inventory cover targets
Risk coverage
High
Long lead time, unpredictable
demand patterns, unreliable
supply, infrequent review
We still get stock-outs
Medium lead time, stable
demand patterns, less unreliable
supply, more frequent review
Two Months
Safety Stock
Low
Individual SKU’s
© Barloworld Supply Chain Software 2014
Short (1 day) lead time, very stable
demand patterns, very reliable
supply, daily review
First step:
optimize the current risk investment
Released Investment
Risk coverage
High
Low
Individual SKU’s
© Barloworld Supply Chain Software 2014
Inventory optimization –
Six Dimensional Problem
A 6-dimensional solution
Lead Time
Reliability
Inventory Cover
Forecast
Accuracy
Replenishment Cycles
Cycle Stock
Target Service Levels
Time
Time
Lead Time
Review
Periods
Safety Stock
Review Periods
© Barloworld Supply Chain Software 2014
7 key processes
for inventory management
Behavior rules
for investment
Measure
primary risk
factors
Quantify cover
for availability
targets
Manage
outliers
Replenish to
cover targets
Data integrity
Demand
Planning
Strategy
Modeling





Shortfall
Excess
Expedite
De-Expedite
Redistribution
Product
Classification
Supplier
Management
Inventory
Management
Metrics
© Barloworld Supply Chain Software 2014
Metrics
 External Orders
 Internal Orders
 Constrained
Orders
Next step:
Reduce the risk & need for safety stock
80:20 rule and TARGETS
Risk coverage
High
Low
Individual SKU’s
© Barloworld Supply Chain Software 2014
Two phases to
inventory optimization
Inventory Team processes are improved,
releasing time for more advanced
planning processes
Establish integrity of
data for quantifying
existing baseline
Establish baseline
inventory processes for
forecasting and leadtime management
© Barloworld Supply Chain Software 2014
Expand process to
Sales and Operations
integration
Phase 1:
Start with a time-phased SKU forecast
Forecast is based on how past data points occurred
• Extrapolate that pattern into the future
• There will always be “error”
• For example
GNPt+1= ƒ(GNPt, GNPt-1, GNPt-2, GNPt-3, GNPt-4, GNPt-5, ...,error)
OBJECTIVES:
• Generate a statistically reliable forecast to support the ongoing inventory planning processes
• Determine the normalized (stable) forecast error input for safety stock cover on each SKU
• Establish a process for operational forecast exception management
END GOAL:
• Reduce inventory exception management triggered by
• forecast volatility
• forecast bias
• Establish baseline operations forecast, and forecast process, for input to a sales and operations
planning process
© Barloworld Supply Chain Software 2014
Phase 1:
Operations forecast management
Manual forecasts
• Separate protected
profiles / profiles with
disparate /
unavailable history
• Manage sourcing of
forecast data (sales /
market / other)
• Assess / measure the
success of the manual
forecasting activity
• Forecast accuracy
• Safety stock
investment
Statistical forecasts
• Measure forecast
error impact on
inventory investment
• Review variances
based on cost to
inventory
• Quarantine and
manage timing of
forecast changes
Group forecasts
• Use demand planning
levels to assess
customer / product
forecast changes
• Track / identify events
in history
• Use sales / market
data for adjusting
/freezing forecasts for
groups of profiles
• Monitor the impact
of group forecast
adjustments on the
accuracy of the
overall forecasts
© Barloworld Supply Chain Software 2014
Phase 1:
Supply performance management
Lead time
accuracy
• Review protected
profiles / profiles with
manual forecasts
input
• Review internal policy
driver relationship to
the length of leadtimes
• Lead-time
comparison to supply
agreements
Delivery volatility
• Measure lead-time
error impact on
inventory investment
• Review variances
based on cost to
inventory
• Quarantine and
manage timing of
lead-time variance
updates
© Barloworld Supply Chain Software 2014
Projections
• Provide projections to
suppliers
• Track supply delivery
to projections
• Track project accuracy
Phase 2:
Sales and Operations Planning
Forecast generation with cross-company data
• Build demand plan using multi-level techniques
• Apply events/profiles (New Part Introduction, promotions, causal's) by product/family/region/other
• Use Data Streaming to create a single version of the final forecast
© Barloworld Supply Chain Software 2014
The S&OP process
Definition:
• Fully integrated decision-making and planning process
• Connects the business drivers across the supply chain
• Allows all functions to contribute to tactical and strategic inventory drivers
Objective
• Balance demand and supply
• Align volume and mix
• Integrate financial and operating forecasts
Vital to success of S&OP process:
A Centralized Material Planning Function
that has S&OP responsibility & authority
© Barloworld Supply Chain Software 2014
Hierarchy forecasting
functionality
Illustrative structure
Territory
Market
Aggregate
Channel
Statistical
Product
Group
Edited
Primary
Forecast
level
Prorated
Customer
Final
Supplier
Hierarchy transfers
Prorate by
Forecast
History
Copy
Average
None
Product Group
Product
Product
location
Location
SKU planning forecast
Protected Nodes (below primary forecast)
© Barloworld Supply Chain Software 2014
SKU
profiles
Primary
forecast
level
Demand Planning Workflow
New Product Introductions:
Launch Profiles
Replacement profiles
Forecast Conditioning:
Causal & Event Management
Seasonality & Algorithms
Lifecycle & Proration
Sales Conditioning:
Generation:
Cleanse History
Outlier adjustment
Stock-out compensation
Forecasting
Generate history
Archive history
Forecast Accuracy:
Sign-offs:
Measure, Track, and control
Forecast Quality
Approval staging
Financial Review
Gross requirements planning
Commitment to material planning
© Barloworld Supply Chain Software 2014
Process sample
Before meeting: forecast
preparation
Operations
data
Apply conditions
Review protected forecasts
S&OP meeting
Market
indicators
Operations
data
Ship to promise
date %
SKU forecast updates
Forecast
variances
Find outliers (exceptions)
Prorate / aggregate
After meeting: Planning
forecast updates
Accuracy counts
Risk calculations for safety
stock coverage
Supply forecast performance
metrics
Generate summaries
• Forecast accuracy
• Forecast summaries
• Fill rates
• Budget / margin variances
Supply on time
in full %
© Barloworld Supply Chain Software 2014
Period to date exception
management