iD FRESH FOOD: DEMAND FORECASTING FOR PERISHABLE SHORT
SHELF- LIFE HOMEMADE FOOD
Group- 4
Pankaj Singh (x01019)
Jaiveer Singh (x01013)
Ajay Singh Rathore ( x01001)
Sagar Kumar (x01023)
Introduction
Fast-moving consumer goods (FMCG) sector is the 4th largest sector in the Indian economy with
Household and Personal Care accounting for 50 per cent of FMCG sales in India. Growing
awareness, easier access and changing lifestyles have been the key growth drivers for the sector.
The urban segment (accounts for a revenue share of around 55 per cent) is the largest contributor
to the overall revenue generated by the FMCG sector in India However, in the last few years, the
FMCG market has grown at a faster pace in rural India compared with urban India. Semi-urban
and rural segments are growing at a rapid pace and FMCG products account for 50 per cent of
total rural spending. The Retail market in India is estimated to reach US$ 1.1 trillion by 2020
from US$ 840 billion in 2017, with modern trade expected to grow at 20 per cent - 25 per cent
per annum, which is likely to boost revenues of FMCG companies. Revenues of FMCG sector
reached Rs 3.4 lakh crore (US$ 52.75 billion) in FY18 and are estimated to reach US$ 103.7
billion in 2020. The sector witnessed growth of 16.5 per cent in value terms between JulySeptember 2018; supported by moderate inflation, increase in private consumption and rural
income.
This case study is about ID Fresh foods, which is based out at Bengaluru and deals in food
products. Company manufactures a range of fresh foods including Idly/Dosa Batter, Parotas,
chapatis, curd, and paneer. A private limited company, founded in 2005 by P.C Mustafa along
with cousins - Abdul Nazer, Shamsudeen TK, Jafar TK, and Noushad TA. The company was
established with the capital of Rs 50000 by Mustafa. Mustafa holds 50 percent share in the
company, and his four cousins hold the rest. In their first order, company supplied ten packets of
one-kilogram batter to 20 stores in Bengaluru under the brand name ‘ID’ and initially operated
from a 550 square feet space with two grinders, a mixer, and a sealing machine. Soon, Mustafa
invested more and as the demand for their product increased, in 2008 bought a new 2,500 square
feet shed in Hoskote Industrial Area of Bangalore. In next two years, the company started
making 2000 Kg of batter daily and partnered with more than 300 of stores. In 2014, ID Fresh
foods raised Rs 35 Cr. in the first round of funding from Helion Venture Partners. They utilized
the funds to add more products and scaled-up the business. In 2016, ID Fresh foods manufacture
around 50,000 kg of batter daily from their units across the country and one in Dubai, which
converts into a million idlis. The company has achieved a turnover of Rs 100 Cr. in 2015-16. ID
Fresh Foods expects to end this fiscal with Rs 286 Cr. turnover from 182 Cr. in 2017-18. The
company expects the turnover as Rs 350-400 Cr. in 2019-20.
Sales and Distribution Network
Now with its processing facilities in 6 cities like Dubai, Bengaluru, Chennai and Mumbai, it
prepares batter for different items. The first day of making batter is counted as day zero. Once
prepared batter is kept inside a cold store unit for about 12 to 16 hour, then packets are loaded
into the chiller vans for distribution. This is day 1, the loaded chiller vans as early as 5:00 a.m.
are ready for dispatch to different shops. In Bengaluru there are some 70 beats or routes and
about 200+ across its operations, each beat was serviced by one salesman and a driver in most
cases. They are supposed to visit every single store of their beat and are supposed to deliver fresh
batter and collect expired batters from the stores. The sales person replenishes SKUs in each
store according to previous sales and it generally ranges from three to twenty packets in the 90%
of stores.
iD being natural and needed to be kept fresh, handled 15 stock-keeping unit. The packets in the
SKUs had shelf life of 7 days. Each unsold SKU will be returned to the salesman and dumped at
the factory. With a short shelf life, it is a challenge to distribute the packets to 16000 stores now.
The distribution network is designed to deliver approx. 50000 kg of idli/dosa batters and the
mission is to deliver the packs fresh and tasty, to the kitchens of customers. An early delivery to
the retail stores help iD to cater fresh batters to their customers.
E-delivery startups like Amazon and Big Basket have created a new line of distribution network
for the company, catering some iD customers directly. Demand at the stores do not follow a
straight pattern, weather, festivals, weekdays and holidays all affect the demand and thus the
supply has to be maintained accordingly. The model of sales and distribution is like walking on a
tight rope. Every increased units at shelf or under stocked was losing of potential sale. In many
markets this was a new product and new category. Every return have to be replaced with fresh
unit, stock-out information was not yet captured and only inferred. The information on return,
expiry and damage was not captured. The company has realized that the current distribution
model is non-rational and largely depends on the gut of the salesman. So, a more predictable and
scientific way was the need of iD foods. Modernizing of IT applications is in process at iD, data
pertaining the same is getting captured (though slow), quality of the same is a concern.
Demand Forecasting
Main challenge that iD Fresh Foods is facing, is to determine the optimum quantity of each
SKUs to be loaded onto a vehicle for each store each day. Overstocked or understocked stores
are loss for iD foods which results in customer dissatisfaction. A predictable and scientific
method of demand forecasting for its SKUs at the granularity is desired to minimize returns and
keep up with the increasing frequency of supply.
The demand for Perishable short shelf life homemade food at iD Fresh Food can be forecasted
using time series methods. Different time-series methods like Simple Moving Average-3
window, Simple Moving Average-5 window, Exponential smoothing and Adjusted Exponential
Smoothing were used to develop forecasting model with error (i.e. minimum difference between
actual demand and forecasted demand). Two errors namely Mean Average Deviation (MAD)
and Mean Average Percent Deviation (MAPD) are considered for choosing the most accurate
forecasting technique. Among the techniques, MAD and MAPD are found to be least for
Adjusted Exponential Smoothing with α= 0.1 and β=0.1 values. So the demand for SKUs can be
forecasted with greater accuracy using the Adjusted Exponential Smoothing (α= 0.1, β=0.1).
Moreover, seasonal fluctuations can be accounted by multiplying the forecasted demands with
seasonality index. This would further minimise the error in forecasting demand.
Figure1. Moving Average for 3 and 5 window
Table1. Comparing Errors of Forecasting Techniques
MAD
MAPD
Simple Moving Average (3 Window)
35.12
20.94
Simple Moving Average (5 window)
33.0
19.25
Exponential Smoothing (α=0.1)
31.05
19.56
Adjusted Exponential Smoothing (α= 0.1, β=0.1)
31.13
18.98
Forecasting Techniques
Conclusion: