1 Dumex Malaysia Workshop 1 Finished Goods Inventory at Nilai FGS July 9, 2008

2 Objectives Update stakeholders Introduce inventory model and its application Review the initial outputs of the model Discuss scenarios

3 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) FGD1PRG1 000PUMY FGD3PRG1 000PUMY FGD3PHN10 00PUMY FGD3PCH10 00PUMY

6 Average Inventory Value Pareto (Apr-07 to Mar- 08) Top 20% of SKU contributed 86% of the total inventory value

7 Inventory Model Inputs Model period is Apr-07 to Mar-08 Data availability Avoid disturbance Raw Data Requirement & Source: Historical Sell In SAP, Sales Order Historical 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 history Standard material price From SAP

8 Domestic Delivery Order & Fill Rate Order

9 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 stock Safety stock due to demand volatility Additional 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 achieved Lost contribution

10 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 – 1 st half TIME Total Stock On Hand Safety Stock - Volatility Cycle Stock Fcst related elements, refer to next slide +

11 Inventory Model Formulation – 2 nd 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 forecast 1.Apply the same safety stock formula, but with different parameters 2.Compare the value of ‘ Safety Stock – Underfcst ’ against ‘ Safety Stock – Volatility ’ 3.Calculate ‘ Additional Safety Stock due to Underfcst ’

12 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

13 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

14 Distributor FG Inventory Scenarios : Results & Assumptions Distributor ScenarioInventory day required Avg. inventory value index (actual =100) Service Level % (Fill Rate) Assumptions / Description Sc 0 Correct Parameters Sc 1a Production frequency 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 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 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 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 Same production frequency and response lead time as Sc 1a, but forecast accuracy improves by 5%

15 Distributor FG Inventory Scenarios : Results & Assumptions Distributor ScenarioInventory day required Avg. inventory value index (actual =100) Service Level % (Fill Rate) Assumptions / Description Sc 1a Production frequency Super A produce weekly, CH & CR in PU produce once every 8 weeks Sc 4a Production frequency & Fill Rate Re- set Same production frequency and response lead time as Sc 1a, but fill rate varies by ABC class Sc 5a Production frequency & Demand pattern change 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 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 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