Case Study Managing a Merger at Lightning Networks

After receiving regulatory approval from the European Union, Lightning Networks, a major wireless carrier, and SatTV, the largest satellite TV provider in Europe, completed their 50 billion euro merger in 2016. After initial skepticism when the deal was first announced, analysts had warmed to the idea of synergies in the merger. Lightning expected to benefit from the large customer base of SatTV, and the company announced that it expected significant annual cost savings within three years of the merger. Simone Durand, senior VP of supply chain at Lightning, was charged with identifying some cost reduction opportunities. She decided to focus her initial attention on the distribution networks the two companies used to fulfill the demand for installation and repair products. The merger offered an opportunity to combine the two distribution networks.

The Current Distribution Network

Any new installation or repair by Lightning or SatTV required a set of products for the technician to complete the job. Rather than carrying these products with technicians, both companies had decided to centralize product inventories in a few locations. Annual product demand for the two companies across six regions in Europe was as shown in Table 1.

Lightning had served its product needs from three warehouses located in Madrid, Spain; Rotterdam, Netherlands; and Krakow, Poland. SatTV had served its product needs from three warehouses located in Toulouse, France; Munich, Germany; and Budapest, Hungary. Each facility was specialized to handle either wireless or satellite products because of the historical focus of the company it belonged to. The specialization, capacity, and annual fixed cost for each facility were as shown in Table 2. The capacity of each warehouse is given in terms of how much annual demand it can handle. From Table 2, observe that the Madrid warehouse can serve a demand of up to 370,000 units. The variable cost of shipping one unit (either wireless or satellite) from each warehouse location to each market is shown in Table 3.

The Network Options

Simone had a short-term and a long-term decision to make. In the short term, she had to decide whether to make all the warehouses flexible or not. Making all warehouses flexible required an investment equivalent to an additional annual cost of 200,000 euros. Flexible warehouses, however, could be used to serve the demand for both wireless and satellite products.

In the longer term, Simone had to decide whether to restructure the distribution network. She could choose to close some warehouses, leave others open as they were, or double the capacity of some warehouses. Doubling the capacity of a warehouse would increase its annual fixed cost by 80%. Thus, if the capacity of the Madrid warehouse was doubled, its annual fixed cost would be 1,080,000 euros.

Closing a warehouse would also incur some cost, thus reducing the annual fixed cost that could be saved. Simone's team estimated that closing a warehouse would save 80% of the annual fixed cost. Therefore, closing the Madrid warehouse would still result in an annual cost of 120,000 euros because only 80% of the fixed cost is saved.

1- What is the annual cost if Lightning uses the current network (with warehouses specialized as inTable2) optimally to meet European demand?

2- Should Simone make all warehouses flexible, given the additional cost of 200,000 euros per year?

3- What supply chain network configuration do you recommend for the long term if demand is as inTable1? Should any warehouses be closed? Should any warehouses see their capacity doubled?

Table 1 Annual Demand in Europe for Lightning Networks (wireless) and SatTV (satellite)

Zone	Wireless Demand	Satellite Demand	Zone	Wireless Demand	Satellite Demand
Northwest	200,000	120,000	Middle South	120,000	120,000
Southwest	100,000	100,000	Northeast	150,000	110,000
Middle North	220,000	100,000	Southeast	90,000	100,000

Table 2 Warehouse Specialization, Capacities, and Fixed Costs

Location	Specialization	Capacity	Fixed Cost (euro/year)
Madrid	Wireless	370,000	600,000
Rotterdam	Wireless	420,000	650,000
Krakow	Wireless	310,000	520,000
Toulouse	Satellite	280,000	475,000
Munich	Satellite	290,000	488,000
Budapest	Satellite	250,000	425,000

Table 3 Variable Distribution Cost per Unit in Euro

	Northwest	Southwest	Middle North	Middle South	Northeast	Southeast
Madrid	2.50	1.50	3.00	2.75	4.00	4.50
Rotterdam	1.75	3.00	1.50	3.00	2.50	3.50
Krakow	3.25	4.00	2.50	3.00	2.00	2.50
Toulouse	2.00	2.00	2.75	2.50	3.75	4.00
Munich	2.25	3.00	2.25	2.50	2.75	3.00
Budapest	3.50	3.75	2.50	2.50	2.50	2.00

A B C D E F G H I J K L M N P Q R S T Warehouse Capacity and Fixed Cost Annual Fixed 2 Location Capacity Cost Madrid 370,000 600,000 4 Rotterdam 420,000 650,000 Input: The green cells should be filled based on the information given in the case Krakow 10,000 520,000 Decision variables: The blue cell should be empty. The values in those cells will be filled after running Solver. Toulouse 280,000 475,000 Constraints: The orange cells should include formulas before running Solver. 7 Munich 290,000 488,000 Objective function: The orange cells should include formulas before running Solver. 0 00 Budapest 250,00 425,000 10 Regional Demand by Product Middle Middle 11 Northwest Southwest North South Northeast Southeast 12 Wireless 200,000 100,000 220,000 120,000 150,000 90,000 13 Satellite 120,000 100,000 100,000 120,000 110,000 100,000 14 Distribution Costs Middle Middle 16 Northwest Southwes North South Northeast Southeast 17 Madrid 2.50 1.5 3.00 2.75 4.0 4.50 18 Rotterdam 1.75 3.0 1.50 3.00 2.50 3.50 19 Krakow 3.25 4.00 2.50 3.00 2.00 2.50 20 Toulouse 2.00 2.00 2.75 2.50 3.75 4.00 21 Munich 2.25 3.0 2.25 2.50 2.7 3.00 22 3.50 3.75 2.50 2.50 2.50 2.00 23 24 Distribution Quantity Variables (Wireless) Middle Middle Capacity 25 Northwest Southwest North South Northeast Southeast Constraint 26 Madrid 27 Rotterdam 28 Krakow 29 Toulouse 30 Munich 31 Budapest Demand 32 Constraint 200000 100000 220000 120000 150000 90000 33 34 Distribution Quantity Variables (Satellite) Middle Middle Capacity 35 Northwest Southwest North South Northeast Southeast Constraint 36 Madrid 37 Rotterdam 38 Krakow 39 Toulouse 40 Munich 41 Budapest Demand 42 Constraint 120000 100000 100000 120000 110000 100000 43 44 45 46 47A B D E F G H J K Warehouse Capacity and Fixed Cost M N P Q R S Annual Fixed 2 Location Capacity Cost 3 Madrid 370,000 500,000 4 Rotterdam 20,000 $50,000 Input: The green cells should be filled based on the information given in the case 5 Krakow 310,000 520,000 Decision variables: The blue cell should be empty. The values in those cells will be filled after running Solver. 6 Toulouse 280,000 175,000 Constraints: The orange cells should include formulas before running Solver. Munich 290,000 188,000 Objective function: The orange cells should include formulas before running Solver. 00 Budapest 250,000 425,000 10 Regional Demand by Product Middle Middle 11 Northwest Southwest North South 12 Wireless Northeast Southeast 200,000 100,000 220,000 120,000 13 150,000 0,000 120,000 100,000 100,000 120,000 14 110,000 100,000 15 Distribution Costs Middle Middle 16 Northwest Southwest North South 17 Northeast Southeast 2.50 1.50 3.00 2.75 4.0 4.50 18 Rotterdam 1.75 3.00 1.50 3.00 2.50 3.50 19 Krakow 3.25 4.00 2.50 3.00 2.00 2.50 20 Toulouse 2.00 2.00 2.75 2.50 3.75 21 Munich 4.00 2.25 3.00 2.25 2.50 22 Budapest 2.75 3.00 3.50 3.75 2.50 2.50 2.50 23 2.00 24 Distribution Quantity Variables (Wireless) Middle Middle Capacity 25 Northwest Southwest North South 26 Northeast Southeast Constraint 27 Rotterdam 28 Krakow 29 Toulouse 30 Munich 31 Budapes Demand 32 33 34 Distribution Quantity Variables (Satellite) Middle Middle 35 Northwest Southwest North South 36 Madrid Northeast Southeast 37 Rotterdam 38 Krakow 39 Toulouse 40 Munich 41 Budapest Demand 42 Constraint 43 44 Total Cost 45A B D E F G H I J K L M N 0 P Q R S T Warehouse Capacity and Fixed Cost Annual Fixed 2 Location Capacity Cost Madrid 370,000 600,000 Rotterdam 420,000 650,000 Input: The green cells should be filled based on the information given in the case Krakow 10,000 $20,000 Decision variables: The blue cell should be empty. The values in those cells will be filled after running Solver. Toulous 280,000 475,000 Constraints: The orange cells should include formulas before running Solver. Munich 290,000 488,000 Objective function: The orange cells should include formulas before running Solver. D 00 Budapest 250,000 425,000 10 Regional Demand by Product Middle Middle 11 Northwest Southwest North South Northeast Southeast 12 Wireless 200,000 100,000 220,000 120,000 150,000 90,000 13 Satellite 120,000 100,000 100,000 120,000 110,000 100,000 14 15 Distribution Costs Middle |Middle 16 Northwest Southwest North South Northeast Southeast 17 Madrid 2.50 1.50 3.00 2.75 4.00 4.50 18 Rotterdam 1.75 3.00 1.50 3.00 2.50 3.50 19 Krakow 3.25 4.00 2.50 3.00 2.00 2.50 20 Toulouse 2.00 2.00 2.75 2.50 3.75 4.00 21 Munich 2.25 3.00 2.25 2.50 2.75 3.00 22 3.50 3.75 2.50 2.50 2.50 2.00 23 24 Distribution Quantity Variables (Wireless) Middle Middle Open / Increase Capacity| Capacity 25 Northwest Southwest North South Northeast Southeast Close (0/1) (0/1) 26 Madrid Constraint 27 Rotterdam 28 Krakow 29 Toulouse 30 Munich Budapest Demand 32 Constraint 200000 100000 220000 120000 150000 90000 33 34 Distribution Quantity Variables (Satellite) Middle Middle 35 Northwest Southwest North South Northeast Southeast 36 Madrid 37 Rotterdam 38 Krakow 39 Toulouse 40 Munich 41 Budapest Demand 42 Constraint 120000 100000 100000 120000 110000 100000 43 44 Total Cost 45 46