LS NAV - Demand plan in replenishment
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LS Nav- Demand Plan in Replenishment -
Matthías E. MatthíassonProduct Manager LS Nav
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Demand Planning• Introduce a proactive approach to the replenishment process
– Valuable sales information
• Implement processes that will reduce inventory– Reduced costs related to inventory holding– Increased working capital
• Implement processes that will reduce stock-outs– Increasing sales– Increased customer service level
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Current methods• Min – Max method is theoretically good
– Correct parameters need to be set– Sales trends need to be accounted for– Parameters need to be reviewed on a regular basis
• In reality the parameters are seldom correct– Changed when stock-outs occur– The damage is already done
• Demand pattern not analyzed– Sales trends produce stock-outs and overstock situation
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LS Retail – Demand Planning• Fully integrated forecasting capabilities
– LS Extended Pack (Replenishment)– Installation wizard guided implementation
• Best fit forecasting engine• Dynamic safety stock calculation• Sales history adjustments• Graphical display of
– Sales history– Inventory levels– Forecasts– Safety stock
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Data Definition Hierarchy
Item StoreHold Data Data Profile
ItemHold Data Data Profile
Product GroupData Profile
Item CategoryData Profile
DivisionData Profile
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Warehouse Replenishment• Use Demand Plan for Warehouse
– Sales History• Sales + Transfer Out
– Available for Warehouse• Stores Need• Min / Max• Demand Plan
• Warehouse can Replenish another Warehouse– Only Min/Max before
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Proactive strategy• Demand forecasting
– Demand forecast creation– Forecast catches sales trends – Trends are accounted for in the replenishment
process
• Safety stock calculation– Service level adjustments enabled– Safety stock calculated based on service level
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Forecasting methods• Automatic selection of forecasting methods
– No statistical expertise required
• Forecasts based on sales history
• Forecasts catch all trends in sales history– Increasing and decreasing sales– Seasonality
• Major holidays and annual events• Two-year sales history needed for seasonality
– Mixed trends
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Forecasting methods• 17 algorithms and forecasting methods• Can be classified into:
– Simple methods• Moving average, automatic and random walk
– Exponential smoothing • For short and volitile data with no leading indicators• Multiple Holt-Winters models for seasonality
– Croston´s intermittent demand model• Low volume, sparse data
– Box-Jenkins • For stable data sets
– Dynamic regression
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Proactive strategy• Sales trend analysis
– Demand forecast creation– Forecast catches sales trends – Trends are accounted for in the replenishment process
• Safety stock calculation– Service level adjustments enabled– Safety stock calculated based on service level
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Safety Stock• Safety stock calculation
– To achieve defined service levels– Based predictability and variation in sales
• Service level defined in LS Nav– Standard service level settings:
• A items = 95%• B items = 85%• C items = 65%
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Safety stock calculation
1 3 5 7 9 11 13 15 17 19 21 23 250
20
40
60
80
Sales History - Item A
Weeks
Sal
es
1 3 5 7 9 11 13 15 17 19 21 23 250
20
40
60
80
Sales History - Item B
Weeks
Sal
esAverage Sales
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Safety stock calculation
1 3 5 7 9 11 13 15 17 19 21 23 250
20
40
60
80
Sales History - Item A
Weeks
Sal
es
1 3 5 7 9 11 13 15 17 19 21 23 250
20
40
60
80
Sales History - Item B
Weeks
Sal
es
Days cover method results in too high safety
stock
Days cover method leads to stock-outs
Safety Stock based on days cover
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1 3 5 7 9 11 13 15 17 19 21 23 250
20
40
60
80
Sales History - Item A
Weeks
Sal
es
1 3 5 7 9 11 13 15 17 19 21 23 250
20
40
60
80
Sales History - Item B
Weeks
Sal
es
Safety Stock based on
average method
Safety stock calculation
1 3 5 7 9 11 13 15 17 19 21 23 250
20
40
60
80
Sales History - Item A
Weeks
Sal
es
1 3 5 7 9 11 13 15 17 19 21 23 250
20
40
60
80
Sales History - Item B
Weeks
Sal
es
Optimal Safety Stock based on 95% service levels
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Replenishment with forecasting• Variant Items are supported• All data maintenance done
within NAV• Automatic Process
– Nightly Process• extract->• calculate-> • forecast returned
• Run Replenishment Journals– On Demand
• Is part of the Extended Pack
Forecasting Process
ItemSales History
Purchase Orders
ForecastResult
NAV
ReplenishmentJournalProcess
Forecasting Process
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Summary• Introduce a proactive approach to the
replenishment process– Valuable sales information
• Implement processes that will reduce inventory– Reduced costs related to inventory holding– Increased working capital
• Implement processes that will reduce stock-outs– Increasing sales– Increased customer service level
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