mrp case study asian paint

8/18/2019 MRP Case Study Asian Paint

1/5

Simulation Asian Paints

A question of a long tail!

BACKGROUND

Our product portfolio extends over 1000 SKU’s serviced over 100 depots. Like in most businesses,

the major volumes are contributed by some fast moving products and the rest - a long tail of

SKU’s which contribute only to the bottom 5 to 10% sale. However, beyond the volume sales that

this large tail brings in, the presence of entire range of SKU’s for consumption is an important

driver in the way consumers choose our product; thus dealers stock them and hence these

products have an extremely crucial service need.

But these slow moving, large range of products face the typical problem of poor predictability,

maldistribution, high inventory stockings despite aggregation of demand at higher levels and

servicing requirements through replenishment. Hence, these result in poor service levels, lost

sale and decline in consumer confidence.

This simulation case study is to find an apt and implementable solution to this practical problem

of replenishing stocks for this range of SKUs with real order data and consumption patterns.

Product profile and Distribution Network

Let us begin by understanding our distribution strategy. These tail end SKUs under consideration,

are considered as slow moving(SM) SKUs and are distributed through Regional Distribution

Centers (RDC). The Fast moving (FM) SKUs are moved directly from plant to depots. The network

structure is shown in figure1.

Plant

RDC Depot Dealer

Depot Dealer

Fi ure 1

SM

FM


2/5


Our Central material distribution function plans the movement FM material to depots and SM

material to RDC based on month estimate and safety stock requirement. The SM material is

replenished to depots based on classic replenishment MRP system where if stock level at depot

falls below a particular level, replenishment stocks based on certain logic are provided to depots

by RDC.

In short, FM skus work on push principle (predict estimates upfront, they have reasonable

accuracy and hence supply) while SM SKUs work on pull principle for replenishment (predict

estimates, consolidate estimates at higher level (RDC), service to this consolidated location basis

estimate and then allow replenishment to the final sale point, depot)

E.g. Say reorder level for a depot is 100 Ltrs. Every time stock goes below ROL, stock needs to be

replenished to maximum( MAX) of 150 ltrs. If current stock at depot is 130 ltrs and depot receives

an order of 70 ltrs then resultant stock is 130 ltr – 70 ltr = 60 ltr. Since 60 ltr < 100 ltr, depot needs

replenishment. Replenishment quantity = 150 ltr – 60 ltr (i.e. max stk requirement – current

stock)

Existing Inventory management process

The RDC needs to maintain sufficient stock of slow moving material to replenish depot

requirement. The Depots need to serve the dealer orders on same day while replenishment lead

time from RDC to depot is of the order of 3 days. The Depot also needs to maintain stock to serve

the orders considering that RDC to depot replenishment takes more time. Depot stocks are

defined considering demand variability and lead time. Depot stocks are maintained in terms of

days of estimate. Total SM inventory in the system is calculated at RDC level in terms of days of

estimate.

E.g. If the cumulative estimate of all depots for a SKU is 1000 L and current total stock at RDC

and depots put together is 1500 L, then inventory in the system is said to be 45 days.

[(1500/1000)*30]


3/5


The Stock at depot is a resultant stock on account of replenishment from RDC and orders on

depot. In case RDC replenishes more stock to depot than estimate or orders, there is risk of the

stock laying idle at depot and eventually become obsolete. This is called Maldistribution of the

stock. So, a tight control is required to avoid maldistribution.

Ordering system and fulfillment

The Dealers place order at central customer service desk or online through dealer portal. The

orders are then transferred to concerned depot in the form of SKU and quantity along with dealer

code. All the orders from dealers need to be serviced on the same day. An order is considered to

be fulfilled if depot has opening stock more than the order received on that day. Orders not

fulfilled are deleted on the same day and are considered as sale loss. There is no carry forwarding

of orders on next day.

The SM SKU demand variability is very high and also daily sale pattern is less predictable. This

poses challenge of deriving stock levels at RDC and depot to ensure high order fill rate (OFR).

OFR = (1- unfulfilled orders/ total orders received).

E.g. 1: if all dealer orders clubbed together are equal to 100L of paint and if opening stock at

depot is only 80L then OFR = (1-20/100)= 80%.

E.g.2: If total order at the depot is 120L and SOH is 123L, then OFR is 100%.

OFR is calculated at Depot SKU for each day. OFR for the month is accumulation of all loss of sale

(20 ltrs in example 1, each day for the month, divided by total orders received that month)

Monthly OFR = (sum (daily loss of sale))/sum(total orders received)) @ depot level


4/5


THE CHALLENGE

The challenge for you is to design a replenishment strategy from RDC to depot to achieve a high

OFR. OFR target is set at minimum 90%.

Each team needs to simulate the replenishment system for the given data set to optimize OFR

with controlled inventory. High OFR with least average inventory in the system is the objective

to be achieved.

The Teams need to provide average inventory in the system and resultant OFR as output of the

simulation along with rationale/strategy/assumptions/enablers considered for simulation.

The Teams also need to also provide inputs on replenishment strategy that best suits overall for

such low volume high variability products.

Remember that the data set provided here is a sample one and any solution need to be scalable.

The output format required is provided in attachment provided with case. The logic and final

working sheet needs to be provided as a part of output.

DATA INPUT AND OTHER ASSUMPTION

1. Information containing network structure of RDC with code name: 1000 is provided

2. This RDC caters to 22 depots (Depot code 1001-1022) and around 350 skus.

3. The Estimate for each depot SKU is provided at month level for each of 3 months

4. Day level order Data is provided for each depot – SKU over a period of 3 month. Order

given on day represents cumulative order placed by all the dealers at central customer

service desk on that day for that depot SKU

5. Current average SM inventory at RDC including depot is maintained at 30 days of estimate

(across all).

6. For simulation purpose one can assume the inventory levels designed at RDC can be met

by the upstream supply process when stocks deplete after replenishment to depots.

7. Lead time from RDC is depot is provided

8.


5/5


Rules and Regulations

Maximum Number of Members in a Team – 3

Register with your Placement Co-ordinator before 21 August 2015

All the entries need to be submitted before 5 September 2015.

Solution to be provided in a Zip folder as a Word document

along with the excel sheet

which consists of the simulation run input & output.

All assumptions (if any) to be clearly documented.

The word document also should have your contact details (name, email and phone)

Any doubts to be mailed to the respective placement co-ordinators.

Decision of the panel will be final and binding.

mrp case study asian paint

Documents