Warehouse Planning & Capacity Optimisation

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Presentation Description

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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Presentation Transcript

PowerPoint Presentation:

Team – Planners Members Sunitha A Srikant Rajan Institute – Institute for Financial management and Research (IFMR) Varna – IPL Challenge

Agenda:

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:

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 :

LUMA – Supply Chain Integrated steel mill Dell Highly efficient Highly responsive Apparel Automotive production LUMA Thus the supply chain should factor in both responsiveness and efficiency

PowerPoint Presentation:

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:

Designing Distribution Network Factors Influencing Distribution Network Design Customer needs that are met Cost of meeting customer needs Number of Facilities Response Time Number of Facilities Cost Inventory Facility Transportation

LUMA :

LUMA Customers are clustered and then assigned to warehouse Warehouse Store inventory Transfer point Manufacturing Unit Warehouses Demand Points Milk Runs

Modeling the Solution:

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:

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

PowerPoint Presentation:

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:

Number of Trucks – Warehouse To Markets 7 0 6 0 7 0 5 0 Vijayawada 3 11 4 2 4 12 5 8 Indore (L) 4 0 5 9 2 0 1 0 Indore (S) 0 0 1 11 0 16 18 3 Ahmedabad Kolkata Hyd Mumbai Mohali Jaipur Delhi Chennai Bangalore

Costs:

Costs Facility costs 1600000 Transportation cost from plant to warehouse 783600 Transportation cost from warehouse to market 2013900 Total Costs 4397500

PowerPoint Presentation:

Ahmedabad Plant Ahmedabad Large WH Indore Large WH Indore Small WH Vijayawada Small WH

PowerPoint Presentation:

Ahmedabad Plant Ahmedabad Large WH Indore Large WH Indore Small WH Vijayawada Small WH Kolkata Jaipur Chennai Mumbai

PowerPoint Presentation:

Ahmedabad Plant Ahmedabad Large WH Indore Large WH Indore Small WH Vijayawada Small WH Chennai Mohali Mumbai Bangalore Delhi

PowerPoint Presentation:

Ahmedabad Plant Ahmedabad Large WH Indore Large WH Indore Small WH Vijayawada Small WH Kolkata Jaipur Hyderabad Chennai Mohali Mumbai Bangalore Delhi

PowerPoint Presentation:

Ahmedabad Plant Ahmedabad Large WH Indore Large WH Indore Small WH Vijayawada Small WH Kolkata Jaipur Hyderabad Chennai Mohali Mumbai

Improving the Solution:

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:

Saving Matrix - Indore 0 Kolkata 690 0 Hyd -145 520 0 Mumbai 640 25 -155 0 Mohali 330 10 -195 925 0 Jaipur 620 45 -155 1290 865 0 Delhi 1080 1265 435 45 15 70 0 Chennai 815 1240 695 10 5 25 1980 0 Bangalore Kolkata Hyd Mumbai Mohali Jaipur Delhi Chennai Blore

Warehouse to Markets:

1. Indore (S) 2 2 1.3 Jaipur 3 4 3.2 Kolkata 5 5 5 Mumbai 8 9 8.4 Mohali 1 1 .8 Chennai Improved Actual Required Net Savings = 1 truck over 1080 km + 1 truck over 925 km @ 15/km Warehouse to Markets

Warehouse to Markets:

2. Vijayawada 6 7 6.8 Kolkata 6 6 5.4 Mumbai 6 7 6.6 Jaipur 5 5 4.1 Chennai Improved Actual Required Net Savings = 1 truck over 680 km + 1 truck over 1060 km @ 15/km Warehouse to Markets

Warehouse to Markets:

Warehouse to Markets 3. Ahmadabad 1 1 0.8 Mumbai 10 11 10.4 Mohali 16 16 15.3 Delhi 18 18 17.9 Chennai 3 3 2.7 Bangalore Improved Actual Required Net Savings = 1 truck over 230 km @ 15/km

Warehouse to Markets:

4. Indore (L) 4 4 3.5 Mumbai 2 2 1.6 Mohali 11 11 10.8 Hyderabad 4 5 4.3 Chennai 3 3 2.7 Kolkata 4 4 3.5 Jaipur 12 12 11.7 Delhi 8 8 7.7 Bangalore Improved Actual Required Warehouse to Markets Net Savings = 1 truck over 1980 km @ 15/km

Net Savings:

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:

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:

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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