This document discusses how demand driven quick response mechanisms can address demand variability in the service industry. It describes using demand driven material requirement planning to manage inventory of a critical ingredient for a coffee shop, which previously ran out of items due to highly variable demand. The planning simulates strategic inventory positioning, buffer profiles and levels, demand driven inventory planning, and automatic alerts. This approach aims to eradicate stockouts while keeping inventory dynamic and at an optimal level through real-time monitoring. Lessons are around addressing zero customer tolerance times with buffers and applying demand driven inventory management models.

1. HOW DEMAND DRIVEN QUICK RESPONSE
MECHANISM IN SERVICE INDUSTRY CAN ADDRESS
THE ISSUE OF DEMAND VARIABILITY
Ajay Kumar Routh
Vinod Gupta School of Management, IIT Kharagpur

2. NESCAFE COFFEE CENTERS
 Revenue Model: A chain of fast food
serving outlets inside IIT Kgp Campus
that serve customers on the go.
Differentiating itself as a coffee & fast
food joint, where customers would go for
short meetings and chats.
 Business Strategy: A go to place to
hangout over a coffee and a place to
mitigate mid night hunger for students
 Problems Noticed and Focused: Running
out of menu items
 Success Formula:
• Promoting as a place of
conversation
• Promotion as a mid night food joint

3. CURRENT STATE
• The selected product (noodles) has highly volatile demand as observed
• Many times it is running out of the menu items and this leads to opportunity loss as well as
customer dissatisfaction
• For discounted bulk purchase, order has to be placed 4days before to dealer
• For Nescafe Centres, customer tolerance time is zero so it has to maintain inventory of
noodles in such a way that it will be responsive to highly variable demand and at the same
time the inventory will be dynamically maintained
• Our focus is to build an demand driven inventory management model that will cater to high
demand variability along with following features:
• Automatic alert system
• Strategically designed buffer
• Real-time monitoring

4. DATA COLLECTION
 What we observed:
• Collected sales data of 12 weeks for the
selected product
• Gathered daily consumption data of the
noodles for 3 weeks and multiplied it with
factor 1.2 considering actual demand is
20% more than actual sales
• Prepared the buffer profile
Average Daily Usase 10
Type Purchased
Lead Time Category Short Lead Time (0.7)
Demand Variability Category High Variability (0.7)
MOQ 20
Lead Time (days) 4
0
5
10
15
20
25
30
0 5 10 15 20 25
Consumption pattern of 21 days
0
5
10
15
20
0 10 20 30 40 50 60 70 80 90
12 Weeks of actual sales data
Highly Variable Demand

5. TOOLS & TECHNIQUES
 Forecasting:
1. Moving Average Forecasting
 Demand Driven Material Requirement Planning: A Demand Driven Operating Model is a
supply order generation and inventory management tool utilizing actual demand in combination with
strategic decoupling, control points and stock, time and capacity buffers in order to create a
predictable and agile system that promotes and protects the flow of relevant information and
materials within the tactical relevant operational range (hourly, daily and weekly).
1. Strategic Inventory Positioning: Use the Decoupled lead time and a matrix bill of material to
determine correct inventory positioning
2. Buffer Profile & Levels: Understanding of the logic of the zones within the buffer for successful
demand shock absorption
3. Demand Driven Inventory Planning: Supply order generation depending upon on-hand inventory,
on-order supply and qualified demand
4. Buffer Status Alert: Measurement of relevant information to raise automatic alert depending upon
on hand inventory

6. BUFFER PROFILE & LEVELS

7. DEMAND DRIVEN PLANNING & EXECUTION

8. SUMMARY
• We simulated Demand Driven Material
Requirement planning to manage the
inventory of critical ingredient
• This quick response mechanism will
eradicate the issue of running out of
menu items
• Inventory management will be dynamic
and real-time monitoring will be
possible along with automatic alert
• Inventory will not exceed the buffer
level
LESSONS LEARNT
• How zero customer tolerance time can
be addressed by strategically designing
buffer
• Demand Driven Inventory Management
• Application of the model in real-time
• The same model can be implemented in
other service industries
• Continuous flow of relevant information
and material is most essential for any
Supply Chain