ls nav - demand plan in replenishment
TRANSCRIPT
LS Nav- Demand Plan in Replenishment -
Matthías E. MatthíassonProduct Manager LS Nav
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
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
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
Data Definition Hierarchy
Item StoreHold Data Data Profile
ItemHold Data Data Profile
Product GroupData Profile
Item CategoryData Profile
DivisionData Profile
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
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
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
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
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
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%
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
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
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
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
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
