Demand Planning and Inventory Optimization for Supply Chain Success

Demand Planning/Supply Chain 101
Microsoft Partner Summit, Phoenix, AZ
February 12, 2013

Agenda
• Introduction
• Definition of Demand Planning
• Why Demand Planning
• Demand Forecasting
• Inventory Planning

• Replenishment Planning
• Summary of Benefits from integrated Demand Planning
• Q&A

2013 JustEnough Software

2

Introduction



3

Introduction
• Jeff Livingston – Director, Partner Development
 15+ years Supply Chain Planning Industry Experience
 Supply Chain Planning experience across SMB and Tier 1
businesses with domain expertise in Demand Planning,
Inventory Optimization, Replenishment/Supply Planning
and S&OP.
 Experience with Logility, Oracle/Demantra, JustEnough
and other applications.
 B.S. Mathematics – Purdue University, MBA California
Polytechnic



4

What is “Demand Planning”
• Demand planning is a multi-step operational supply chain
planning process used to create reliable forecasts of customer
demand and align inventory levels and supply plans to achieve
the targeted level of customer service desired by a business.
• In Short:
“ Having the Right Product, at the Right Place, at the
Right Time and at the Right Price, to satisfy customer
demand.”



5

Realities of the New Business Environment
Companies encounter continuous change and require the ability
to rapidly adapt in a fast changing business environment

© 2013 JustEnough Software

6

We Live in Turbulent Times
Things beyond your control can have a major impact on your
supply chain.


7

Traditional Planning Systems Don’t Work Anymore
• Traditional tools (spreadsheets and disconnected, fragmented
systems) can not respond quickly enough to the rapid pace of
change:
 Global Sourcing
 Longer lead-times
 Increased Demand Volatility

 Faster Product Lifecycles
 More choices & new competitors
 Fewer Resources, People, Capacity…doing more with less.
Greater Demand + Longer
+
Variability
Lead-Times

More Full
Container
Shipments

=

Increased
Inventory Risk


8

Traditional Approach = Disconnected
Slow Processes, limited collaboration, no consensus forecasting
Typical Company
Finance
Budget

Manual
processes

Sales
Forecasts/Quotas

Marketing
New Products

E-mail
overload

Inventory
Planning

Manufacturing
Capacity/Scheduling

Multiple,
non-integrated
systems

Low forecast accuracy
Departmental/Silo orientation to Supply Chain Planning
Misalignment between metrics and objectives
Reactive vs. Proactive/Exception based
Multiple versions of “The Truth”


9

A Better Approach = Integrated Sales Forecasting, Inventory
Planning and Replenishment Planning
Integrated Supply Chain Planning – Driving the business via ONE
PLAN:
Demand Forecasting
•
•
•
•
•
•

Statistical
Forecasting
Market Intelligence
Seasonality
Lost Sales
Supersessions
Promotion Planning

Merchandise Financial Planning
Inventory Planning
Order Planning &
• Strategic Inventory
Replenishment
•
•
•

Management
ABC Stratification
Scenario Analysis
Time-phased
Optimization

•
•
•

•

Recommended PO’s,
WO’s and Transfer
Orders
Constrained Orders
Alerts/Exceptions
(Potential Stock Outs,
Excess, Defers, Cancels,
Expedites, etc.)
Opportunity Buying


10

Better Approach = Significant Benefits (Supply Chain Champs)
• Demand-Driven Supply Chain Planning Leaders Have:
 15% Less Inventory
 17% Better Order Performance

 35% Shorter Cash-to-Cash Cycle
times

• Which Translates To:
 60% Better Profit Margins
 65% Better EPS
 2-3 X ROA

Source:

 1/10 the Stock outs of their peers

11

Better Approach - People, Plans & Objectives Aligned


12

Demand Planning Core Components
Integrated Forecasting, Inventory Planning and Replenishment Planning

A holistic planning system that is also linked/integrated to the ERP
backbone execution system (i.e. MS Dynamics)
MS Dynamics
ERP

Events, Promotions &
Market Intelligence

Sales History

Demand
Forecasting
Forecasts, and
Statistical Standard
Deviation

On Hand balance
Open Orders
MOQ, Multiples, etc.

Inventory
Planning
Time-phased Inventory
Policies & Strategy, ABC
Stratification, Order
Constraints, Lead times, etc.

Time-Phased
Inventory Plan

Recommended
Orders:
Purchase Orders,
Work Orders &
Transfer Orders

Replenishment
Planning
Exceptions: Potential Stock outs, Excess,
Defers, Cancels, Expedites, etc.

13

Do You Have an Accurate Picture of
Customer Demand?


14

Demand Forecasting
Demand Forecasting “Best Practice” process approach

There are four generally applied and recognized forecasting
methods that when combined help generate an accurate demand
plan:
Future statistical
Time Series
Forecasting

Generating forecasts based
on pre-defined profiles and
continuously maintaining
those profiles based on
performance

Experimental
Forecasting

Total
Demand
Forecast

Judgment and
Collaborative
Forecasting

projections/extrapolations based on
historical data patterns and trends

Causal
Forecasting

Predict and incorporate the
effect of particular events,
promotions, and other factors

Seeking extrinsic knowledge from
internal or market sources and
coming to a forecast consensus

15

Demand Forecasting
Time Series/Statistical Forecasting

• The Process Starts with a Statistical Forecast as a baseline
forecast
 Premise – Historical customer demand patterns are the best indicator of
future demand patterns.
 Requires import/integration of Historical Sales Demand from
ERP/Transaction system as foundation data (Typically 2 to 3 years of
prior monthly/weekly demand)

• Statistical Forecast Engine analyzes demand history/patterns
 Evaluates multiple forecast methods/algorithms to determine “best-fit” or most
statistically accurate method/model.

 Determines appropriate level of expected sales, projected growth/decline trends
and seasonal patterns.
 Statistical Mean Square Error or Standard Deviation used to select best-fit model
(Standard Deviation can be used as input to “Statistical Safety Stock” calculation in
Inventory Planning)


16

Demand Forecasting
Statistical Forecasting and selecting best statistical method

Typical Types of Statistical Methods/Models applied:
Constant
Trend
Seasonal
Sporadic

Straight/flat line

Linear or non-linear growth
projection
Repetition at fixed intervals

Random pattern


17

Demand Forecasting
Statistical Forecasting and selecting best statistical method

Typical Types of Statistical Methods/Models applied:


18

Demand Forecasting
Add Market Intelligence and Demand Shaping activities

• The Process Continues – Improve the forecast with market
intelligence inputs & forecast overrides

• Demand Shaping for Profitability:
 Plan promotions, events & incentives
 Plan new product introductions
 Identify cross-selling opportunities


19

Demand Forecasting
Group/Hierarchical & Attribute Forecasting

• Forecasts are often required or better managed at multiple
levels of detail/aggregation:
 Top-Down
-

Forecast by Product Family/Groups
Fair-share disaggregation to SKU
Leverage “Law of Large Numbers”

Product
Family

Strategy/Budget focused

Greater visibility to Seasonality or Trends

Item

 Bottoms-up
-

Sum SKU history/forecasts to family/groups
Operationally/customer focused

Item-Location

 Middle –Out
-

Blended Method


20

Demand Forecasting
Forecasting New Products

Need to polish-up your Crystal Ball?


21

Demand Forecasting
New Product Forecasting

• Forecasting New Items – Approaches to forecasting items that
have no prior demand history?
 Manual Forecast (Judgment)
 Like-Item Forecasting

- Supersessions/Chaining
- Copy/Merge similar product
 Profile/Curve Shaping

- Life Cycle Profiling
- Attribute based Profiling
- Seasonality “Group Profiling”


22

Demand Forecasting
Measure/Monitor Forecast Accuracy

• Measure and monitor forecast accuracy for continuous
improvement:
 Typical forecast accuracy measurements include:
-

MAD (Mean Absolute Deviation) – Used for inventory calculations
MAPE (Mean Absolute Percent Error) – Common Forecast Accuracy metric
Relative Error – Relative measure of error for diagnostic purposes – exceptions

 Measure Accuracy of Multiple Inputs:
-

Accuracy of Statistical Forecast
Accuracy of Overrides & Market Intelligence inputs
(Did inputs help/hurt the forecast accuracy?)


23

Demand Forecasting
Collaboration and Consensus Forecasting

Consensus forecast process results in best single number to drive
downstream planning process

Statistical
Baseline
Forecast

Input from
Sales,
Marketing
and Finance

Input Events,
Promo’s and
Incentives

Customer &
Competitor
Information

New
Products

Consensus
Forecast


24

Demand Forecasting
Gain Competitive Advantage via Better Forecasting

• Improved forecast accuracy hits the bottom line:
5% Forecast
Accuracy
Improvement

10% Perfect
Order
Fulfillment

$0.50 Greater
EPS

 Costs go down & earnings go up:

-

17% stronger perfect order fulfillment
15% less inventory
35% shorter cash-to-cash cycle times
1/10 of the stock outs of their peers


25

Inventory Planning
Balancing Inventory Investment with Desired Service Level

Inventory Planning – Optimizing inventory investment.


26

Inventory Planning
Why Do Businesses Carry Inventory/Safety Stock?

• Economic Reasons
- Lot Sizes/Ordering Costs
- Minimum Order Quantities
• Transportation Inventory
- Pipeline fill
• Special Events
- Promotions/Incentives, etc.
• Fluctuation Inventory – Due to Uncertainty (Safety Stock)
- Uncertainty of Demand (Forecast Error)
- Uncertainty of Supply (Supplier Delivery variability)

27

Inventory Planning
The Inventory/Service Level “Exponential Relationship”

Safety Stock Quantity

Safety Stock Inventory Increases dramatically as Service Levels
approach 100%

80

85

90

95

100

Service Level %


28

Inventory Planning
Forecast Accuracy & Safety Stocks

Forecast Error has a direct relationship with Safety Stocks:
Small Standard
Deviation

Item A
Low Standard Deviation Item
requires less Safety Stock
Carrying too much Safety Stock
would be costly!!!

Item A
Big Standard
Deviation

Item B
Higher Standard Deviation item
requires more safety stock

Item B

Having too little Safety Stock
increases the risk of stock outs!!


2013 JustEnough Software Confidential

29

Inventory Planning
Not All Items are the Same

• Inventory Stratification – Pareto Principle – ABC Classifications
 ABC stratification can be used to set strategic inventory policies
 Focus investment attention on “A” items
 Apply Uniform Rules to Like Items
 Reduce Decision Time


30

Inventory Planning
Safety Stock Policy Setting

• Common techniques/policies for setting safety stocks and
inventory objectives:
 Fixed Safety Stock Levels
 Periods of Supply ( 2 weeks of Supply)
 Statistical Safety Stock Policy

- Target Service Level (i.e. 98% Service Level)
- Accounts for Variability of Supply/Demand
- Accounts for Lead-time and Order Quantities
 Policies can be dynamic for optimization over time/season.


31

Inventory Planning
Time-Phased Supply Chain Network Planning

• Modeling the Distribution Network – Nodes, Sources and Lead
Times:
Manufacturing/Vendors

East Region USA DC

West Region USA DC

Midwest USA DC

Canadian DC

Customers/Consumers


32

Inventory Planning
Planning for the Entire Supply Chain Network

An optimal replenishment plan – demand-driven pull planning
Manufacturing/Vendors
Dependent
Demand

Dependent
Demand

East Region USA DC
Dependent
Demand

Midwest USA DC

West Region USA DC

Optimal replenishment
policy considers all network
dependencies – dependent
and independent demand

Dependent
Demand

Canadian DC

Independent
Demand

Customers/Consumers


33

Replenishment Planning

Traditional Re-order Point (ROP systems) trigger orders when
stock falls below a defined level
Re-Order Point Logic

DRP Planning Logic

Reactive process – orders are
initiated based on inventory
level only
Assumes inventories in supply
chain network are independent
Orders are needed NOW – not
forward looking based on the
true need date
Static ordering settings (i.e.
min & Max levels)
Promotes PUSH planning

34


An optimal replenishment plan proactively proposes orders over a
future horizon based on planned inventory projections
Re-Order Point Logic

DRP Planning Logic

Reactive process – orders are
initiated based on inventory
level only

Proactive process - orders are
recommended based on
projected inventory position

Assumes inventories in supply
chain network are independent

Orders are Time-Phased over a
defined future horizon – based
on future need date(s)

Orders are needed NOW – not
forward looking based on the
true need date

Dynamic Ordering Policies

Static ordering settings (i.e.
min & Max levels)

Holistic plan for all supply
chain network dependencies –
dependent demand

Promotes PUSH planning

Promotes PULL Planning

35

How Much to Re-Order and When?

The Saw-Tooth Diagram: Orders Recommended (at lead-time) to
be received into inventory on the day that you “plan” to dip into
Safety Stock.
Units
Replenishment Plan
Orders suggested in
lead-time advance

OQ

Rate of Inventory Depletion
As demand occurs, on-hand
decreases

OQ = 100
Order Date = Sept. 1

CS

SS

Due Date = Dec. 14

The Saw-Tooth Diagram

Order Received In
Potential risk for stock out

Time


36

Output = Recommended Orders + Bonus Prize

• The Replenishment Planning “Optimal Order Generation” Engine
generates a stream of Recommended Orders into the future:
 Purchase Orders
 Manufacturing Orders
 Transfer Orders
 Re-distribution Orders

• Plus a Bonus Prize = Actionable Exception/Alert conditions:
 Stock Outs/Excess Inventory
 Potential Stock Outs/Shortfalls
 Surplus/Defer/Cancel Orders
 Expedite Orders

37

Replenishment Planning 101
Closed-Loop Planning – Release Orders to ERP

Integration of the “Demand Planning” Recommendations with the
ERP system for Execution (Closed Loop Planning)
MS Dynamics
ERP
Recommended
PO’s, WO’s and
Transfer Orders

Real/Actual
PO’s, WO’s and
Transfer Orders

Demand
Planning


38

Inventory Optimization and Process Maturity

• Optimizing Inventory across the Supply Chain Network:
 Strategically defining where inventory should be kept in the
network

 Setting specific stocking/ordering policies by item, location, etc.
 Advanced Inventory Optimization practices

- Postponement – A strategy used to eliminate excess Finished Goods
Inventory , by delaying the final activities (assembly, production,
packaging, tagging, etc.) until the latest possible time.

- Risk Pooling - Risk pooling suggests that demand variability is
reduced by aggregating demand across multiple locations, This
reduction in variability allows a decrease in safety stock and
therefore reduces average inventory.


39

Inventory Optimization and Process Maturity

• Optimizing Inventory across the Supply Chain Network – what
companies are really doing (level of process maturity):


40

Demand Planning Summary
Recent Trends

• DDSN – Demand Driven Supply Networks
 Demand-driven supply networks are driven from the front by customer
demand vs. push planning (build it and they will come).
 Partners in a supply chain will work more closely to shape market
demand by sharing and collaborating information – goal is to bring the
supply chain eco-system into balance.

• Demand Sensing Closer to the Point of Consumption
 Leverage POS Data…better picture of true market demand
 Technological Advances (Big Data) enabling use of retail/store data

• S&OP, Demand Shaping, and Business Intelligence/Analytics
 Make better and more profitable business decisions, more quickly.


41

Competitive Advantage in a Fast Changing World

• Improve forecast accuracy
• Sense demand changes and respond more quickly

• Optimize inventory across the full supply chain
• Improve customer service while reducing costs
• Increase visibility for better/faster decision making

• Increase efficiency/productivity via exception/alert-based
workflow
• Bottom-line impact/results


42

Makes running a business a little bit easier…


43

Demand Planning and Inventory Optimization for Supply Chain Success

Demand Planning and Inventory Optimization for Supply Chain Success

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