Indian Institute of Management Visakhapatnam
Executive Master of Business Administration (EMBA)
Winter Batch – 2024-26
(Term 2)
OPERATIONS MANAGEMENT
Course Instructor(s)
Prof. Akshay Gajanan Khanzode
Submitted by
Group Number: WG-11
Project Title: Decentralized Cloud Kitchens: Location &
Capacity Planning for Efficient Multi-Brand Operations
Group Members:
2461096
2461016
2461021
2461073
2461079
2461057
TV Radhakrishna Mullapudi Tv
Aryan Garg
Debasish Barman
Rashmi K.N
Rompalli Ganesh
Prajwal Singh
2461027 Isarapu Manikanta Rajeev Kumar
2461042 Manjari Sen
tvradhakrishna.mullapudi24-06W@iimv.ac.in
aryan.garg24-06W@iimv.ac.in
debasish.barman24-06W@iimv.ac.in
rashmi.KN24-06W@iimv.ac.in
rompalli.ganesh24-06W@iimv.ac.in
prajwal.singh24-06W@iimv.ac.in
isarapumanikanta.rajeevkumar2406W@iimv.ac.in
manjari.sen24-06W@iimv.ac.in
1. Introduction and Identification of Issues
1.1 Overview of Operations Management Operations Management (OM) is the administration
of business practices aimed at ensuring maximum efficiency within an organization. It involves
designing, overseeing, and refining processes that convert inputs into valuable outputs. With the
shift towards service-oriented industries, OM has expanded to include sectors like hospitality,
logistics, and food services.
1.2 Purpose and Scope The purpose of this study is to explore how OM concepts can be
effectively applied to decentralized cloud kitchens in India. The scope includes location selection,
capacity planning, layout optimization, inventory control, and demand forecasting.
1.3 Current Trends in Operations Management - Rise in service-based operations and digital
platforms - Data-driven decision making - Emphasis on lean operations and flexibility Decentralized operations for market proximity - Integration of AI and predictive analytics for
forecasting and resource allocation - Circular operations and sustainability through shared
infrastructure
Figure 1: Current trends driving change in operations management practices
1.4 Product & Services Continuum Cloud kitchens sit at the intersection of product and service.
While food is the tangible product, the end-to-end delivery experience, platform integration, and
timely service define the value proposition. This project addresses the continuum by optimizing
both physical and experiential elements.
1.5 Relevance of the Topic in Today’s World The food delivery industry is undergoing a
transformation post-COVID-19, where consumers expect faster, safer, and more diverse options.
Cloud kitchens emerged as a resilient model, yet their success hinges on operational excellence.
In Tier 2 and 3 cities, this evolution is nascent, and operators often face high operating costs,
inconsistent demand, and logistical challenges. This project aims to bridge that gap with scalable,
data-backed solutions.
•
Rapid growth in the online food delivery sector (CAGR of over 25% in India)
•
Increased preference for multi-cuisine aggregators
•
Supply chain decentralization trends post-pandemic
•
Pressure to reduce delivery time and increase order accuracy
•
Rising interest from food tech investors in cloud kitchen models
1.6 Additional Issues Identified - Limited interoperability between different brand workflows
within the same space - High fixed costs due to redundant infrastructure - Lack of visibility into
real-time performance metrics - Inefficiencies in kitchen expansion decisions due to absence of
predictive modeling
1.7 Project Context We analyze decentralized cloud kitchens—delivery-only kitchen models
without dine-in infrastructure. Our goal is to enable efficient multi-brand operations in Tier 2 and
Tier 3 cities by using data-backed OM strategies. This involves proposing a scalable model that
improves asset utilization, delivery turnaround time, customer satisfaction, and profitability.
1.8 Identified Operational Inefficiencies - Kitchen utilization below 40% during non-peak hours
- Suboptimal location decisions with delivery radii exceeding 6 km - Delays due to poor kitchen
layouts - Reactive rather than proactive forecasting - Fragmented scheduling leading to labor and
energy wastage
2. Methodology
2.1 Operations Strategy Alignment The project aligns with a cost-effective and responsive
operations strategy, ideal for price-sensitive markets with dynamic demand patterns.
2.2 Competitiveness and Productivity - Operations Strategy: Agile with modularity and
collaboration - Productivity Measures: - Labor productivity - Asset utilization - Delivery
efficiency (orders/hour) - Total productivity = Output / (Labor + Ingredients + Fixed Costs)
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Figure 2: Productivity comparison between single-brand and multi-brand cloud kitchen
operations
2.3 Process Selection - Batch process with elements of job shop for customization - Hybrid layout
combining functional areas (e.g., cooking zones, packaging stations) and brand-specific zones
2.4 Data Collection and Assumptions - Simulated demand data from Tier 2 cities (e.g., Indore,
Raipur) - Cost estimates from third-party cloud kitchen operators - Order distribution across
cuisines and time blocks
2.5 Techniques Used - Center of Gravity Method for location - Moving Average and Regression
for demand forecasting - EOQ with safety stock for inventory - Queuing Models (M/M/1 and
M/M/S) - AON/CPM for kitchen setup project - Network Diagrams and PERT (for delivery time
estimation)
Method
MAPE (%) Accuracy (%)
Moving Average 16.2
83.8
Regression Model 11.4
88.6
Table 1: Comparison of demand forecasting techniques
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3. Analysis
3.1 Location Planning - Used weighted average coordinates of high-order pin codes - Delivery
radius optimized to below 4.5 km - Evaluated via Center of Gravity and Factor Rating methods
Figure 3: Weighted location plot based on delivery pin codes and projected demand
3.2 Facility Layout for Product & Service - Combination layout integrating group technology
(cellular) - Shared stations (e.g., ovens, fryers) reduce idle time - Justified redesign using loaddistance analysis
Figure 4: Example layout of a hybrid kitchen showing shared and brand-specific zones
3.3 Capacity Planning - Modeled peak and non-peak order flows - Short-term: Shift scheduling,
part-time staffing - Long-term: Space expansion vs outsourcing - Average kitchen throughput:
180–240 orders/day
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Figure 5: Kitchen Utilization Rate Before and After Optimization
3.4 Inventory Management - EOQ used for predictable ingredients - Safety stock added for
perishables - Inventory Turnover improved from 3.5 to 5.2
Figure 6: Inventory Turnover Ratio Before and After Optimization
3.5 Inventory Control Under Demand Variation - Continuous Review Policy with reorder
points - Periodic Review Model used for beverages & packaging items - Stockouts reduced by
22%; Holding costs lowered by 18%
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Metric
Before Implementation After Implementation
Inventory Turnover
3.5
5.2
Stockout Rate
22%
9%
Holding Cost Reduction –
18%
Table 2: Inventory performance improvements after model implementation
3.6 Queuing Theory - M/M/1 model for single prep station - M/M/S model for multiple brand
stations - Average wait time: reduced by 2.4 minutes
Parameter
M/M/1 (Single Prep) M/M/S (Multi-Station)
Avg Wait Time 5.8 min
3.4 min
Queue Length
6.2
3.1
Utilization Rate 0.84
0.72
Table 3: Performance comparison between queuing models
3.7 Project Management & Time Optimization - AON network for kitchen setup process Critical activities: plumbing, gas setup, equipment installation - CPM used for crashing to meet
30-day setup target - Cost-Time trade-offs evaluated using QM for Windows
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Figure 7: AON Network Diagram for Cloud Kitchen Setup Project
Activity
Normal Time (days) Crashed Time (days) Additional Cost (₹)
Plumbing
4
2
3,000
Equipment Setup
5
3
5,000
Gas Fitting
3
2
1,500
Table 4: Cost-time tradeoffs using CPM crashing
3.8 PERT Estimation for Service Delivery - Applied to last-mile delivery reliability - Estimated
delivery times showed 95% probability within 28 minutes
Figure 8: PERT Bell Curve Showing Probability of Delivery Time
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4. Results
•
Kitchen Utilization: Improved from 38% to 56%
90
80
70
60
50
40
30
20
10
0
Peak Hours
Non-Peak Hours
Before
After
Figure 9: Improvement in kitchen asset utilization post-OM implementation
•
Delivery Time: Reduced by 9–12 minutes
•
Order Fulfillment Rate: Increased from 88% to 96%
•
Inventory Holding Costs: Reduced by ~18%
•
Labor Efficiency: Idle time reduced by 15%
•
Inventory Turnover: Increased by 48.5%
5. Strategic Recommendations and Performance Implications
5.1 Recommendations - Leverage seasonal demand to pre-plan stock and labor - Use traffic and
weather data in real-time forecasting - Adopt modular kitchen equipment for space flexibility Outsource non-core food items during peak loads
5.2 Performance Implications - 15–20% improvement in ROI - Enhanced service differentiation
via reduced delivery times - Lower cost per order improves profitability for new brands
6. Conclusions
This report demonstrates the power of OM frameworks in addressing real-world inefficiencies in
cloud kitchens. From strategic location planning to optimized layouts and capacity planning, our
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integrated model addresses both operational and financial goals. The model is scalable and ideal
for Tier 2/3 cities with high food delivery potential but limited infrastructure.
7. References
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Chase, R. B., Jacobs, F. R., & Aquilano, N. J. (2020). Operations and Supply Chain
Management. McGraw-Hill Education.
•
Heizer, J., Render, B., & Munson, C. (2020). Operations Management. Pearson.
•
Chopra, S., & Meindl, P. (2019). Supply Chain Management: Strategy, Planning, and
Operation. Pearson.
•
Zomato Annual Report 2023
•
Swiggy Blog and Investor Presentations
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Slack, N., Brandon-Jones, A., & Johnston, R. (2020). Operations Management. Pearson.
•
Stevenson, W. J. (2020). Operations Management. McGraw-Hill Education.
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APA 7th referencing style applied throughout.
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