1

Dumex Malaysia Workshop 1

Finished Goods Inventory at Nilai FGS

July 9, 2008

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Objectives

Update stakeholders

Introduce inventory model and its application

Review the initial outputs of the model

Discuss scenarios

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Total FG Sold (Domestic & Export) FG Inventory in Nilai FGS

4

50% of inventory is less than 34 days cover

30% of inventory is beyond 52 days cover

5

Inventory cover day versus rate of sales (sell in)

FGD1PRG1000PUMY

FGD3PRG1000PUMY

FGD3PHN1000PUMY

FGD3PCH1000PUMY

6

Average Inventory Value Pareto (Apr-07 to Mar-08)

Top 20% of SKU contributed 86% of the total inventory value

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Inventory Model Inputs

Model period is Apr-07 to Mar-08Data availabilityAvoid disturbance

Raw Data Requirement & Source:Historical Sell In

SAP, Sales OrderHistorical Demand Forecast

APO, Forecast releases in Month M, Forecasting the demand of Month M+1

Replenishment Frequency (e.g. once per week) FG material: SAP historical production order TG material: SAP historical purchase order

Fill rate between Dumex DC and her customers From report, prepared by shipping

Response Lead Time From interview

Phrase In Phrase Out historyStandard material price

From SAP

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Domestic Delivery Order & Fill Rate

Order

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Inventory Model Outputs

At SKU or modified SKU (SKU drop version code, last two digits) level:

Total average stock on hand is broken down into:Average cycle stockSafety stock due to demand volatilityAdditional safety stock due to over-sold (under-forecast)Additional average stock due to under-sold (over-forecast)

They are presented in Base Unit, in kg & in Ringgit (apply the standard material price).

Others outputs:Overall level, fill rate achievedLost contribution

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Total Stock On Hand

Average Cycle Stock

+

Safety Stock - Volatility

+

Avg. Sell In

per Day

X

No of days between

consecutive replenishments

X

2

/

SD of Daily

Sell In

X

K Factor (driven by

Filling Rate)

X

Sq. Root of Lead Time

X

Inventory Model Formulation – 1st half

TIME

Total Stock On Hand

Safety Stock - Volatility

Cycle Stock

Fcst related elements,

refer to next slide

+

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Inventory Model Formulation – 2nd half, Forecast Related Stock

Total Stock On Hand

Average Cycle Stock

+

Safety Stock - Volatility

+Additional

Safety Stock – Under forecast

+

Avg. Stock – Over forecast

+

SD of Under

forecast Qty

X

K Factor (driven

by Filling Rate)

X

Sq. Root of

Response Lead Time

X

Avg of Over

forecast Qty (4 wkly)

X

2

/Safety Stock

– Under forecast1. Apply the same safety stock formula,

but with different parameters2. Compare the value of ‘Safety Stock –

Underfcst’ against ‘Safety Stock – Volatility’

3. Calculate ‘Additional Safety Stock due to Underfcst’

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Sell Out quantity are assumed to follow normal distribution.K Factor is derived by probability curve, one tail method.Given the confidence level (i.e. the filling rate, X%), what is inventory needed (i.e. the probability of sell out quantity is less than the inventory quantity on hand = X%).K Factor is defined as a scaling factor, which calculate the ‘safety inventory’ given the standard deviation of sell out.Simulations are performed to ensure that filling rates are met.

Inventory Model Assumptions: the K Factor

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Promotion volumes will distort the inventory required Promotion item, such as FOC Item, which is under promotion

Phrase In & Phrase Out timing New product, which has short history Retiring product, which has stopped further production

Day with zero sales versus Day Off versus Day Out of Stock Scanned Sales data versus Sell Out

Other Data Considerations

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Distributor FG Inventory Scenarios :Results & Assumptions

Distributor Scenario

Inventory day

required

Avg. inventory

value index (actual =100)

Service Level %

(Fill Rate)

Assumptions / Description

Sc 0 Correct Parameters

20.1 94 95

Sc 1a Production frequency

18.8 97 95 Super A produce weekly, CH & CR in PU produce once every 8 weeks. Urgent order is made as soon as possible. RM is available

Sc 1b Production frequency & response LT

18.8 97 95 Super A produce weekly, CH & CR in PU produce once every 8 weeks. Urgent order is made in the next pack cycle. RM is available

Sc 2a Production frequency & further increase in Response LT

20.3 89 95 Super A produce weekly. Urgent order is made as soon as possible. Response lead time increase further due to RM unavailable

Sc 2b Production frequency & longest response LT

20.3 89 95 Super A produce weekly, Urgent order is made in the next pack cycle. Response lead time increase further due to RM unavailable.

Sc 3a Production frequency & FA improvement

18.6 97 95 Same production frequency and response lead time as Sc 1a, but forecast accuracy improves by 5%

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Distributor FG Inventory Scenarios :Results & Assumptions

Distributor Scenario

Inventory day

required

Avg. inventory

value index (actual =100)

Service Level %

(Fill Rate)

Assumptions / Description

Sc 1a Production frequency

18.8 87 95 Super A produce weekly, CH & CR in PU produce once every 8 weeks

Sc 4a Production frequency & Fill Rate Re-set

18.1 77 95 Same production frequency and response lead time as Sc 1a, but fill rate varies by ABC class

Sc 5a Production frequency & Demand pattern change

17.8 102 95 Same production frequency and response lead time as Sc 1a, but demand volatility reduces by 10%

Sc 6a Production frequency & Giant serve via different channel

19.4 101 95 Same production frequency and response lead time as Sc 1a, but Giant related SKU demand volatility increases by 6%

Sc 7a Production frequency & manage by Sell Out

17.4 95 Same production frequency and response lead time as Sc 1a, but uses Sell Out history

Monthly stock reservation for Tesco & Carrefour

Export product made mid of month – impact on month end inventory level