Warehouse Planning and Capacity Optimisation

Team – Planners

Members

–Sunitha A–Srikant Rajan

Institute – Institute for Financial management and Research (IFMR)

Varna – IPL Challenge

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

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

LUMA – Supply Chain

Integratedsteel mill

DellHighlyefficient

Highlyresponsive

Apparel

Automotiveproduction

LUMA

Thus the supply chain should factor in both responsiveness and efficiency

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

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

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

– Store inventory– Transfer point

Manufacturing Unit

Warehouses Demand Points

Milk Runs

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

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

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

Number of Trucks – Warehouse To Markets

70607050Vijayawada

3114241258Indore (L)

40592010Indore (S)

00111016183Ahmedabad

KolkataHydMumbaiMohaliJaipurDelhiChennaiBangalore

Costs

Facility costs 1600000

Transportation cost from plant to warehouse 783600

Transportation cost from warehouse to market 2013900

Total Costs 4397500

Ahmedabad Plant

AhmedabadLarge WH

Indore Large WH

IndoreSmall WH

VijayawadaSmall WH

Ahmedabad Plant

AhmedabadLarge WH

Indore Large WH

IndoreSmall WH

VijayawadaSmall WH

Kolkata

Jaipur

Chennai

Mumbai

Ahmedabad Plant

AhmedabadLarge WH

Indore Large WH

IndoreSmall WH

VijayawadaSmall WH

Chennai

Mohali

Mumbai

Bangalore

Delhi

Ahmedabad Plant

AhmedabadLarge WH

Indore Large WH

IndoreSmall WH

VijayawadaSmall WH

Kolkata

Jaipur

Hyderabad

Chennai

Mohali

Mumbai

Bangalore

Delhi

Ahmedabad Plant

AhmedabadLarge WH

Indore Large WH

IndoreSmall WH

VijayawadaSmall WH

Kolkata

Jaipur

Hyderabad

Chennai

Mohali

Mumbai

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

Saving Matrix - Indore

0 Kolkata

6900 Hyd

-1455200 Mumbai

64025-1550 Mohali

33010-1959250 Jaipur

62045-15512908650 Delhi

108012654354515700 Chennai

81512406951052519800Bangalore

KolkataHydMumbaiMohaliJaipurDelhiChennaiBlore

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

2. Vijayawada

676.8Kolkata

665.4Mumbai

676.6Jaipur

554.1Chennai


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



3. Ahmadabad

110.8Mumbai

101110.4Mohali

161615.3Delhi

181817.9Chennai

332.7Bangalore


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

4. Indore (L)

443.5Mumbai

221.6Mohali

111110.8Hyderabad

454.3Chennai

332.7Kolkata

443.5Jaipur

121211.7Delhi

887.7Bangalore



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

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%

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

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

Documents

Warehouse Planning and Capacity Optimisation