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Demand Planning/Supply Chain 101
February 12, 2013
Microsoft Partner Summit, Phoenix, AZ
Agenda
• Introduction
• Definition of Demand Planning
• Why Demand Planning
• Demand Forecasting
• Inventory Planning
• Replenishment Planning
• Summary of Benefits from integrated Demand Planning
• Q & A
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Introduction
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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
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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 customerdemand.”
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Companies encounter continuous change and require the ability to rapidly adapt in a fast changing business environment
Realities of the New Business Environment
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We Live in Turbulent Times
Things beyond your control can have a major impact on your supply chain.
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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 + More Full = Increased Variability Lead-Times Container Inventory Risk
Shipments
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Traditional Approach = Disconnected
Slow Processes, limited collaboration, no consensus forecasting
Multiple,non-integrated
systems
Manual processes
Finance Budget
MarketingNew Products
InventoryPlanning
SalesForecasts/Quotas
ManufacturingCapacity/Scheduling
Typical Company
Low forecast accuracyDepartmental/Silo orientation to Supply Chain Planning
Misalignment between metrics and objectivesReactive vs. Proactive/Exception based
Multiple versions of “The Truth”
E-mail overload
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A Better Approach = Integrated Sales Forecasting, Inventory Planning and Replenishment Planning
Integrated Supply Chain Planning – Driving the business via ONE PLAN:
• Statistical Forecasting
• Market Intelligence• Seasonality• Lost Sales• Supersessions• Promotion Planning
Demand Forecasting
• Strategic Inventory 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
Merchandise Financial PlanningInventory PlanningOrder Planning & Replenishment
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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 1/10 the Stock outs of their peers Source:
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Better Approach - People, Plans & Objectives Aligned
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Demand Planning Core ComponentsIntegrated 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
Demand Forecasting Inventory
PlanningReplenishment
Planning
Sales HistoryOn Hand balance
Open OrdersMOQ, Multiples, etc.
Events, Promotions & Market Intelligence
Forecasts, and Statistical Standard
Deviation Time-phased Inventory Policies & Strategy, ABC
Stratification, Order Constraints, Lead times, etc.
Time-Phased Inventory Plan
Recommended Orders:
Purchase Orders, Work Orders & Transfer Orders
Exceptions: Potential Stock outs, Excess, Defers, Cancels, Expedites, etc.
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Do You Have an Accurate Picture of Customer Demand?
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Demand ForecastingDemand Forecasting “Best Practice” process approach
There are four generally applied and recognized forecasting methods that when combined help generate an accurate demand plan:
Total Demand Forecast
Future statistical projections/extrapolations based on historical data patterns and trends
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
Generating forecasts based on pre-defined profiles and continuously maintaining those profiles based on
performance
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Demand ForecastingTime 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)
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Demand ForecastingStatistical Forecasting and selecting best statistical method
Typical Types of Statistical Methods/Models applied:
Straight/flat line
Linear or non-linear growth projection
Repetition at fixed intervals
Random pattern
Trend
Seasonal
Sporadic
Constant
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Demand ForecastingStatistical Forecasting and selecting best statistical method
Typical Types of Statistical Methods/Models applied:
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Demand ForecastingAdd 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
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Demand ForecastingGroup/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”
- Strategy/Budget focused
- Greater visibility to Seasonality or Trends
Bottoms-up- Sum SKU history/forecasts to family/groups
- Operationally/customer focused
Middle –Out - Blended Method
• Demand Shaping for Profitability: Plan promotions, events & incentives Plan new product introductions Identify cross-selling opportunities
Product Family
Item
Item-Location
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Demand ForecastingForecasting New Products
Need to polish-up your Crystal Ball?
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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”
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Demand ForecastingMeasure/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?)
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Demand ForecastingCollaboration and Consensus Forecasting
Consensus forecast process results in best single number to drive downstream planning process
Statistical Basel
ine Forecast
Input from Sales, Marke
ting and
Finance
Input Events
, Promo’s and Incenti
ves
Customer & Competitor Informatio
n
New
Products
Consensus
Forecast
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Demand ForecastingGain Competitive Advantage via Better Forecasting
• Improved forecast accuracy hits the bottom line:
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
5% Forecast Accuracy
Improvement
10% Perfect Order
Fulfillment
$0.50 Greater
EPS
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Inventory PlanningBalancing Inventory Investment with Desired Service Level
Inventory Planning – Optimizing inventory investment.
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Inventory PlanningWhy 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)
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Inventory PlanningThe Inventory/Service Level “Exponential Relationship”
Safety Stock Inventory Increases dramatically as Service Levels approach 100%
80 85 90 95 100Service Level %
Safe
ty S
tock
Qua
ntity Safety Stock
increases dramatically as Service Level
increases
Inventory PlanningForecast Accuracy & Safety Stocks
Forecast Error has a direct relationship with Safety Stocks:
Item A
Item B
Item A
Low Standard Deviation Item requires less Safety Stock
Carrying too much Safety Stock would be costly!!!
Item B
Higher Standard Deviation item requires more safety stock
Having too little Safety Stock increases the risk of stock outs!!
Small Standard Deviation
Big Standard Deviation
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Inventory PlanningNot 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
200 Total SKUs
40 SKUs account for 80% of the
revenue120 SKUs
account for 15% of the
revenue
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Inventory PlanningSafety 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.
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Inventory PlanningTime-Phased Supply Chain Network Planning
• Modeling the Distribution Network – Nodes, Sources and Lead Times:
Manufacturing/Vendors
East Region USA DC
Customers/Consumers
West Region USA DC
Canadian DC Midwest USA DC
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Inventory PlanningPlanning for the Entire Supply Chain Network
An optimal replenishment plan – demand-driven pull planning
Manufacturing/Vendors
East Region USA DC
Customers/Consumers
West Region USA DC
Canadian DC Midwest USA DC
Independent Demand
Dependent Demand
Dependent Demand
Dependent Demand
Dependent Demand
Optimal replenishment policy considers all network dependencies – dependent and independent demand
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Replenishment PlanningTime-Phased Supply Chain Network Planning
Traditional Re-order Point (ROP systems) trigger orders when stock falls below a defined level
Re-Order Point 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
DRP Planning Logic
A Better W
ay ?
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Replenishment PlanningTime-Phased Supply Chain Network Planning
An optimal replenishment plan proactively proposes orders over a future horizon based on planned inventory projections
Re-Order Point 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
DRP Planning Logic
Proactive process - orders are recommended based on projected inventory position
Orders are Time-Phased over a defined future horizon – based on future need date(s)
Dynamic Ordering Policies
Holistic plan for all supply chain network dependencies – dependent demand
Promotes PULL Planning
Replenishment PlanningHow 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
SS
OQ Rate of Inventory DepletionAs demand occurs, on-hand
decreases
Model Stock
SS + CS
Inventory Max
SS + OQ
Order Received In Potential risk for stock out
Replenishment PlanOrders suggested in lead-time advance
Lead Time
The Saw-Tooth Diagram
Cycle Stock
½ OQ
CS
Time
OQ = 100Order Date = Sept. 1
Due Date = Dec. 14
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Replenishment PlanningOutput = 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
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Replenishment Planning 101Closed-Loop Planning – Release Orders to ERP
Integration of the “Demand Planning” Recommendations with the ERP system for Execution (Closed Loop Planning)
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MS Dynamics ERP
Demand Planning
Real/Actual PO’s, WO’s and Transfer Orders
Recommended PO’s, WO’s and Transfer Orders
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Replenishment PlanningInventory 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.
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Replenishment PlanningInventory Optimization and Process Maturity
• Optimizing Inventory across the Supply Chain Network – what companies are really doing (level of process maturity):
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Demand Planning SummaryRecent 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.
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Demand Planning SummaryCompetitive 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
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Demand Planning SummaryMakes running a business a little bit easier…