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Warehouse Planning and Capacity Optimisation

Date post: 18-Nov-2014
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Shows the strategic planning for location of warehouses and design the optimal network for transportation to demand points.Won the first prize at DOMS, IIT Chennai
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Team – Planners Members –Sunitha A –Srikant Rajan Institute – Institute for Financial management and Research (IFMR) Varna – IPL Challenge
Transcript
Page 1: Warehouse Planning and Capacity Optimisation

Team – Planners

Members

–Sunitha A–Srikant Rajan

Institute – Institute for Financial management and Research (IFMR)

Varna – IPL Challenge

Page 2: Warehouse Planning and Capacity Optimisation

Agenda

• Supply Chain– Influencing Factors– What it means for LUMA?

• Network Design– Influencing factors – Interrelationship between factors

• Modeling the Solution

• Optimal Design

• Improving the Solution

• Final Observations

Page 3: Warehouse Planning and Capacity Optimisation

Supply Chain• Seasonality of Demand

– High, assumed to be at peak before and during IPL season

• Range of quantity – No huge variations in quantity across demand points

• Variety – High , teams and players

• Channel for Product Sale – Limited retail space such as specialty stores and merchandisers

(Assumed)

• Implied Demand Uncertainty – Higher as compared to items such as salt, steel but lower than

technology intensive products such as palm tops

Page 4: Warehouse Planning and Capacity Optimisation

LUMA – Supply Chain

Integratedsteel mill

DellHighlyefficient

Highlyresponsive

Apparel

Automotiveproduction

LUMA

Thus the supply chain should factor in both responsiveness and efficiency

Page 5: Warehouse Planning and Capacity Optimisation

Facility Location

• Manufacturing• Storage/Warehousing *

•Where?•How Many?

Market & Supply Allocation

• Transportation Costs• Service Level – Responsiveness Vs Efficiency• Facility Costs

• Fixed• Variable

Routing• Demand Points

•Distance•Density

• Product Demand & Value

Network Design Decision variables

Page 6: Warehouse Planning and Capacity Optimisation

Designing Distribution Network

• Factors Influencing Distribution Network Design– Customer needs that are met– Cost of meeting customer needs

Number ofFacilities

Response Time

Number of Facilities

Cost

Inventory

Facility Transportation

Page 7: Warehouse Planning and Capacity Optimisation

LUMA • Customers are clustered and then assigned to warehouse• Warehouse

– Store inventory– Transfer point

Manufacturing Unit

Warehouses Demand Points

Milk Runs

Page 8: Warehouse Planning and Capacity Optimisation

Modeling the Solution

• Warehouse selection is binary• Qty transported through a warehouse

–does not exceed qty received from plant –does not exceed its capacity

• Qty transported –to warehouse –to markets is integer

• Total quantity supplied from all warehouses

to markets should cover the demand

Constraints

• Which warehouse• Quantity to be transported

– Plant to Warehouse –Warehouse to market

Decision variables

• Minimize (Facility costs + Transport costs)Objective fn

Page 9: Warehouse Planning and Capacity Optimisation

Optimum Design - LUMA

• Warehouse – Locations– Capacity

• Transportation Quantity– From Manufacturing unit to warehouse– Warehouse to demand Points

• Costs– Warehouse Leasing Costs– Transportation Costs

• From Manufacturing unit to warehouse• Warehouse to demand Points

Page 10: Warehouse Planning and Capacity Optimisation

Warehouses Small Large

Ahmedabad X

Ludhiana

Indore X X

Lucknow

Vijayawada X

Bhubaneswar

Coimbatore

Ahmedabad Indore Vijayawada

Small 19 23

Large 48 46

Warehouse Locations & Capacity

Number of trucks from Plant to Warehouse

Note – Rounded to the next integer

Page 11: Warehouse Planning and Capacity Optimisation

Number of Trucks – Warehouse To Markets

70607050Vijayawada

3114241258Indore (L)

40592010Indore (S)

00111016183Ahmedabad

KolkataHydMumbaiMohaliJaipurDelhiChennaiBangalore

Page 12: Warehouse Planning and Capacity Optimisation

Costs

Facility costs 1600000

Transportation cost from plant to warehouse 783600

Transportation cost from warehouse to market 2013900

Total Costs 4397500

Page 13: Warehouse Planning and Capacity Optimisation

Ahmedabad Plant

AhmedabadLarge WH

Indore Large WH

IndoreSmall WH

VijayawadaSmall WH

Page 14: Warehouse Planning and Capacity Optimisation

Ahmedabad Plant

AhmedabadLarge WH

Indore Large WH

IndoreSmall WH

VijayawadaSmall WH

Kolkata

Jaipur

Chennai

Mumbai

Page 15: Warehouse Planning and Capacity Optimisation

Ahmedabad Plant

AhmedabadLarge WH

Indore Large WH

IndoreSmall WH

VijayawadaSmall WH

Chennai

Mohali

Mumbai

Bangalore

Delhi

Page 16: Warehouse Planning and Capacity Optimisation

Ahmedabad Plant

AhmedabadLarge WH

Indore Large WH

IndoreSmall WH

VijayawadaSmall WH

Kolkata

Jaipur

Hyderabad

Chennai

Mohali

Mumbai

Bangalore

Delhi

Page 17: Warehouse Planning and Capacity Optimisation

Ahmedabad Plant

AhmedabadLarge WH

Indore Large WH

IndoreSmall WH

VijayawadaSmall WH

Kolkata

Jaipur

Hyderabad

Chennai

Mohali

Mumbai

Page 18: Warehouse Planning and Capacity Optimisation

Improving the Solution

• Milk Runs– Availability of unused capacity in trucks used for transportation– Combination of logistics chain in a single vehicle

• To increase vehicle capacity utilization • Reduce transportation costs

• Modus Operandi– Distance Matrix – Each warehouse to market– Savings Matrix – Each Warehouse to market– Rank Savings– Identify unused capacity in outbound trucks– Combine outbound trucks , based on priority of savings

Page 19: Warehouse Planning and Capacity Optimisation

Saving Matrix - Indore

0 Kolkata

6900 Hyd

-1455200 Mumbai

64025-1550 Mohali

33010-1959250 Jaipur

62045-15512908650 Delhi

108012654354515700 Chennai

81512406951052519800Bangalore

KolkataHydMumbaiMohaliJaipurDelhiChennaiBlore

Page 20: Warehouse Planning and Capacity Optimisation

1. Indore (S)

221.3Jaipur

343.2Kolkata

555Mumbai

898.4Mohali

11.8Chennai

ImprovedActualRequired

Net Savings = 1 truck over 1080 km + 1 truck over 925 km @ 15/km

Warehouse to Markets

Page 21: Warehouse Planning and Capacity Optimisation

2. Vijayawada

676.8Kolkata

665.4Mumbai

676.6Jaipur

554.1Chennai

ImprovedActualRequired

Net Savings = 1 truck over 680 km + 1 truck over 1060 km @ 15/km

Warehouse to Markets

Page 22: Warehouse Planning and Capacity Optimisation

Warehouse to Markets

3. Ahmadabad

110.8Mumbai

101110.4Mohali

161615.3Delhi

181817.9Chennai

332.7Bangalore

ImprovedActualRequired

Net Savings = 1 truck over 230 km @ 15/km

Page 23: Warehouse Planning and Capacity Optimisation

4. Indore (L)

443.5Mumbai

221.6Mohali

111110.8Hyderabad

454.3Chennai

332.7Kolkata

443.5Jaipur

121211.7Delhi

887.7Bangalore

ImprovedActualRequired

Warehouse to Markets

Net Savings = 1 truck over 1980 km @ 15/km

Page 24: Warehouse Planning and Capacity Optimisation

Net Savings

• Net Distance Saved = 5955 km

• Rate of distance travel = Rs15/km

• Net Cost savings = 89325

Reduction in transportation cost by milk runs = 4.4%

Page 25: Warehouse Planning and Capacity Optimisation

Final Observations

• The optimal solution varies slightly based on initial values of decision variables.

• Current warehouse capacity is capable of satisfying demand till 2012

• Incorporate additional warehouse based on latest forecasts (2013 onwards)

• Existence of unused warehouse capacity after 2010,

• If holding costs are known, warehouse planning may be better.

• Larger capacity trucks to transport to warehouses as – Demand at warehouses is large– Gain in per Km cost with volume

Page 26: Warehouse Planning and Capacity Optimisation

Effect of new demand points

• Increase in net demand < Available slack( un - utilized capacity) with warehouses

• Max slack available with Indore (Centrally located)

• Tradeoff between

– increased cost in having a new facility – Saving transportation costs – Service level– Slack

• So we recommend to use existing network


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