This was part of the solution that we suggested towards SCM & Operations case study competition Marico - Over the wall. However, the idea was not accepted we believe that this will make an impact.

1. Presented By
Team: Co-Existence
Shailesh J. Mehta School of Management, IIT Bombay
Marico Over the wall - 2015
Phase 1
Member 1: Rohit Kumar (rohit.kumar@sjmsom.in, 8451999445)
Member 2: Sumeet Kumar Seth (sumeet.seth@sjmsom.in )

2. Case Highlights Approach & Primary research Model Designing Solution & Sustainability
0
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60
80
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Sales-forecast for Parachute 100ml
Sales Demand Forecast
Seasonality index
Component
Trend
Component
Level
component
Sales-Forecast Gap Analysis
1. The existing model fails to incorporate the seasonality index, trend,
and level components, due to which there is drastic error in the
demand forecast
2. Hence, we suggest this model which will accommodate those
variation automatically
Key problems:
Highlights Rationale
 Lack of consent among sales team and
distributor; hence leading to chaos
 Suggest change management for Better
operating model
 Increase in lead time & Push for product
from sales team .
 Prioritizing Dealer’s requirement and
fulfilling it ASAP
 Loss of revenue due to stock-out  Device a better mathematical model
 Measure safety stock with customer service
level of 95 %
 Demand variation from the retailer side
 Locking of working capital in non-moving
SKU
 Use of technology (Mobile App) to track
sales on daily basis & Improve forecasting
 Link channels for mobilizing & unknotting
such blockages
Mathematical
model redesign
Linking
Channels
Mobility
&
Improved
IT
Improve
operating
model
Push to Pull
Approach
Sustainability

3. Unorganized
(Mom & Pop
shops)
Specific
Distributor
(Hans in Powai)
Specific
Distributor
(LINK Co-
operation)
Organized Retail
(6% approx)
(D-mart, Spencer,
Nature Basket)
Retail
Depot
Information was extracted from discussion. They were reluctant to share information
Distributor: Separate distributors for organized &
Unorganized retail.
Organized
(HANS, Powai)
Unorganized
(LINK Co-operation,
Ghatkoper )
• Low chances of stock-
out. In case if there was
one, the responsibility
was with the company.
• Organized retailers
follow weekly PDP
schedule
• Major problem was
order collection & sales
push
• No proper sales
forecast/ demand
• May fall short of
inventory at times.
Retailor:
Exist as an organized/unorganized sector)
Organized (D-mart, Big
bazaar, Spencer's)
Unorganized
(Pop & Mom)
• Mainly pull based
system
• Follow PDP schedule
• Safety stock: 2 days
approx.
• Distributors serving
organized retailers have
a margin of around
1.5%
• Mainly push based
system
• May/may not follow
PDP schedule
• Safety stock: 2 days
approx.
Organized retail
such as Big-Baazar
(Purchase directly
from Depot)
Zone 1 Zone 2 Zone 3
We tried to
divided the
system into
4 zones and
analyzed the
problem
phase wise
Zone 4
Case Highlights Approach & Primary research Model Designing Solution & Sustainability

4. *Refer to the note section & attached Excel sheet for references & Comments
Process Algorithm
 Implied assumption: It will automatically
compensate the forward forecast with
seasonality factor
 We will find the Level, Trend and
seasonality factor by assuming alpha, beta
and gamma close to industry level value (By
secondary research)
 Finding the Mean square forecasting error
(MSE)
 Minimize the value of MSE by changing the
optimum level of Alpha, beta and gamma
 Initial Level & Trend could be found by
regression analysis between time period &
Sales demand
 Initial seasonal factor is assumed to be ‘1’
 Now, we will find new error and on
backtracking it, we will find out the optimum
forecast data
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Parachute 100 ml - Ram Agency
Sales Demand Old Forecast New Forecast
Stock optimization – Secondary sales
Mean Absolute Deviation 20.183 1.314
Mean Squared Error 658.425 7.579
Mean Absolute Percentage Error 0.30 0.09
Old New
Stock optimization – Primary sales
70.0%
75.0%
80.0%
85.0%
90.0%
95.0%
100.0%
H&C PC 100 PC 500 SG 5L SG 1L SM SP
Correlation w.r.t Secondary Sales
Forecasted Data Primary Sales
Push situation
Pull situation
Approach:
 Replacing the Primary sales data with the new forecast data to
calculated the error of meeting the secondary sales demand
 Safety stock is adjusted in the first month for each SKU.
 Find correlation for total primary sales & total forecasted data wrt
total secondary sales.
Correlation of Secondary Sales w.r.t
SKU Forecasted Data Primary Sales
Hair&Care Silk 200ML 98.1% 73.6%
Parachute 100ML 98.8% 76.4%
Parachute 500ML 97.7% 96.3%
Saffola Gold 5L 97.5% 86.9%
Saffola Gold 1L Pouch 96.8% 98.2%
Saffola Masala Oats 96.9% 98.3%
Saffola Plain Oats 97.0% 85.2%
Rationale: Push-Pull Model Determination
1. Pull based model is applicable when, wrt
Secondary Sales, forecasted data correlation
is much above the Primary Sales and Vice
Versa
2.The portion where there is high degree of
correlation of Forecasted data as compared
to primary data it will accommodate
maximum level of variation in demand
Safety Stock Determination
Safety Stock Calculation of SKU's
(For forecast)
Optimized Safety
Stock
Hair & Care Silk 200ML 3.5
Parachute 100ML 58.8
Parachute 500ML 121.3
Saffola Gold 5L 118.7
Saffola Gold 1L Pouch 24.0
Saffola Masala Oats 2.2
Saffola Plain Oats 2.0
Case Highlights Approach & Primary research Model Designing Solution & Sustainability

5. 2. Pull-Push Hybrid Mobility Model : By Android Mobile App
Select Current stock
level
Display available self
stock
Dropdown showing all the
SKU of any product
Select & Update stock
level
 We cannot do away with push based system due to highly unorganized market & heavy competition. So, to leverage the
growing smartphone usage habit, we developed a Pull-Push Hybrid Mobility Model. The mobile based app will be
available for all the distributors, retailors & will have separate access level based on login.
 This will introduce the Pull based system. Currently, it is mainly present in the organized sector
 The App will mainly cater to unorganized retail; will record the current shelf stock
 The retailor will be able to update stock on daily basis & place new order directly. Visible up to the company
 App will track the secondary sales, & analytics could be incorporated for future forecasting
 App will give retailors a direct access to the company & we could convey latest offers directly through app
 App will connect various stakeholders and mobilize the flow of information and inventory
 If customer do not update stock, the distributor can track & inquire for sale stagnancy.
Sales Team Forecast &
Distributor Requirements
Demand
=
Forecast
Demand
>
Forecast
1. Distributor Quoted Demand
Executed
2. Reason for Disagreement
Distribu
tor
Agrees
Distributor Stockout
1. Inter Distributor Linkage
2. Credit Facility given to
distributor
Margin shared by the burrower and
lender.
Model
Successful
Sales Team
Quoted Order
Executes
Order
Executed
Yes
No
No
Yes
Yes
1. Suggested key Change management
 They will be able to borrow stock from their
counterpart in case of any stock out
 The borrower will have to share margin with
the distributor
 We propose to have a provision to link the
distributors
3.Linkingthechannels
*Refer to the note section & attached Excel sheet for references & Comments
Case Highlights Approach & Primary research Model Designing Solution & Sustainability

6. Secondary Sales Primary Sales
SKU Ram Agency
Lakshman
Agency
Depot
Hair & Care Silk 200ml 99.40% 98.95% 96.78%
Parachute 100ML 98.85% 99.01% 95.17%
Parachute 500ML 94.80% 98.08% 40.22%
Saffola Gold 5L 96.24% 99.97% 60.71%
Saffola Gold 1L Pouch 96.56% 99.99% -31.72%
Saffola Masala Oats 93.68% 93.03% -220.54%
Saffola Plain Oats 91.06% 99.50% 86.09%
Average 95.80% 98.36% 57.88%
Forecast Error (Mean Square Error) Improvement
 We have improved MSE by more than 95% in secondary sales and 58%
in secondary sales. -220 is assumed to be an outlier, hence ignored.
SKU Jan-14 Feb-14 Mar-14 Apr-14 May-14 Jun-14 Jul-14 Aug-14 Sep-14 Oct-14 Nov-14 Dec-14 Jan 15 Feb 15
1.Hair & Care Silk
200 ml
Ram Agency 1.7 2.3 1.6 3.4 1.9 3.3 2.4 1.8 2.1 2.8 2.5 2.6 0.4 3.2
Laxman Agency 7.4 7.0 6.5 8.0 7.1 6.3 7.1 7.5 6.9 6.9 7.4 10.6 7.5 8.8
2. Parachute 100 Ml
Ram Agency 91.5 82.4 68.8 114.6 72.5 87.4 46.9 78.5 71.4 94.4 61.5 65.6 66.2 104.3
Laxman Agency 121.5 140.2 97.6 167.8 99.0 151.7 75.4 121.1 143.2 129.8 108.7 177.2 88.2 188.8
3. Parachute 500 Ml
Ram Agency 188.5 142.2 118.1 380.1 114.7 103.5 97.6 182.4 138.6 109.0 132.7 103.5 106.7 212.3
Laxman Agency 73.0 96.4 85.0 316.2 73.6 125.3 91.5 146.1 200.5 147.7 122.8 120.5 12.1 166.0
4. Safola Gold 5l
Ram Agency 376.2 387.4 92.8 831.6 136.1 424.0 97.7 375.8 527.5 258.0 726.2 313.9 253.6 620.7
Laxman Agency 71.2 64.7 56.1 158.1 26.4 72.9 68.1 86.0 99.8 78.6 115.7 68.1 64.1 81.7
5. Safola Gold 1 L
pouch
Ram Agency 392.4 362.2 146.6 261.0 228.7 388.6 226.6 546.7 210.6 220.0 101.9 238.3 236.3 333.3
Laxman Agency 21.1 11.8 14.8 18.6 9.5 30.0 10.5 28.4 6.8 15.8 10.8 9.0 0.0 0.0
6. Saffola Masala
Oats
Ram Agency 3.3 4.2 3.1 4.1 3.2 5.8 5.6 5.0 3.0 8.7 8.1 9.6 10.3 5.9
Laxman Agency 0.0 0.2 0.0 0.1 0.0 0.5 1.1 1.7 2.1 1.4 1.5 4.0 1.1 0.6
7. Saffola Plain Oats
Ram Agency 4.9 2.3 1.9 2.2 6.0 1.8 5.7 2.2 3.0 1.8 2.7 1.7 3.4 1.7
Laxman Agency 0.8 0.7 0.4 1.5 1.6 1.4 2.2 0.9 1.3 1.9 1.3 3.1 0.5 1.0
Correct stock level to be maintained at Distributor point in 2014 (Solution 2) & Forecast for Jan/Feb 2015 (Solution 3)
Upgrading Operating model
 Availing the credit facility to the distributors
by assigning unique virtual account number
 Virtual accounts will release credit lines for
distributors immediately on receipt of
payment
64% 65%
155%
21%
86%
45%
17%
28%
9% 6%
12%
3%
16%
55%
0%
50%
100%
150%
200%
H&C 200 PC 100 PC 500 SG 5L SG 1L SM Oats SP Oats
FORECASTING ERROR(REKHA)
Jan, 2015 Feb, 2015
Case Highlights Approach & Primary research Model Designing Solution & Sustainability
 Automating the App order request & Depot
system

7. Thank you !! 