ls nav - demand plan in replenishment

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