Tesla Model 3 Product Launch Forecast and Distribution Center AnalysisGroup 4

Joseph GarciaAbhinay Reddy Gudimetla

Aaron NorvalSatwinder Singh Thind

Prepared on May 2nd, 2016 for Dr. LongEngineering Management 5614

Introduction

Tesla gained widespread attention in automobile industry combining world range, safety, performance, cost, style and spaciousness into electric cars. Tesla operates over 80 stores in USA and gets its main supply from the production unit “Tesla Factory” in Fremont, California, and it also has a specialized production plant in Lathrop, California. Their mission statement is to “accelerate the world’s transition to electric mobility with a full range of increasingly affordable electric cars”. They are successful in implementing this by releasing wide range of cars into the market and moving into larger, more competitive markets at lower price points. To continue this, Tesla announced the release of a new model “Tesla Model 3” on March 31, 2016 and the cars are scheduled to be delivered by the end of 2017.

Tesla Model 3 is an all-electric four-door compact sedan car which is aimed to deliver an all-electric range of atlas 215 miles starting at $35,000. Model 3 is a part of Tesla’s three step strategy starting at higher price and move progressively towards lower price. It has gained immense popularity with the number of reservations made after the launch. It made the heads turn with 325,000 reservations which is more than triple the number of Tesla Model S cars that has come into production in 2012.

Scope

The objective of this project is to evaluate the current demand of the electric vehicles in the United States in order to forecast and prepare for the demand of the upcoming Tesla Model 3. The project is divided into 3 parts. First the project will forecast the demand for the Tesla Model 3 over the next 4 years. The second part will find the most optimal locations for new distribution centers in the Northeast, Southeast, and Midwest regions of the United States with the gravity model. The model will account for the approximated proportional demand of each of Tesla’s dealerships and their geographic locations. The final third part will develop a method to most efficiently use the truck fleets and shipping the cars and aftermarket parts from distribution centers to

dealerships at optimized cost. Although further data is needed to implement this technique, the method is a feasible way of minimizing the transportation cost of the new distribution centers.

Discussion of Methods

In November 2015 Tesla announced the third quarter of 2015 results and it produced a record 13,091 vehicles, and also revised its target sales for 2015 to between 50,000 and 52,000 vehicles, including both of its models available for retail sales. The company expects to achieve an average production and deliveries of 1,600 to 1,800 vehicles per week for Model S and Model X combined during 2016, adding up to 80,000 to 90,000 new Model S and Model X vehicles in 2016. As a result of the high demand for Model 3 and to meet the demand, Tesla Motors announced its decision to advance its 500,000 total unit build plan (combined for Model S, Model X, and Model 3) to 2018, two years earlier than previously planned.

In 2014, Tesla’s annual delivery record, is checked in at 33,157 units. For the entirety of 2015, Tesla delivered 50,580 cars, an impressive figure that just managed to surpass the low-range of Tesla’s delivery projection of 50,000 to 55,000 vehicles. Tesla deliveries year over year increased by 52%, a striking figure given that some analysts have been quick to proclaim that anyone who already wants a Tesla likely already owns one but there is a delay associated with this.

Despite all the positives, the problems and challenges are obvious, not least for logistics and the supply chain. This included plant locations, quality control, quality holds, supplier issues, premium freight and capacity issues. Tesla has only ever made 100,000 cars in its history and its factories are scaled for considerably lower levels of production. Tesla’s “gigafactory”, a 5.5m-sq.ft space in Nevada, which will produce batteries for Tesla’s cars, is set to start production in 2017, but factories producing the cars will either have to significantly step up, or more will need to be built.

Tesla’s Fremont plant in California is capable of producing 1,000 cars a week and for a car manufacturer whose only American production plant is in Fremont, California, the prospect of millions of dollars of parts and components sitting in cargo ships or port docks were unsettling. Elon Musk stated that “We will need to build a factory in Europe to serve long-term regional demand as Fremont reaches max capacity.”

Based on the above statistics, Tesla can narrow down to two options to meet the future demand in an efficient and responsive way. The first option is to set the dealership and the second option is setting up distribution centers in convenient locations.

Under U.S. law, manufacturers can control the retail price of their products under certain conditions. As long as the manufacturer specifies its conditions up front, the retailer must either take the deal or pass and not sell the product in question. Manufacturers understand that forcing retailers to hold the line on price likely means lower sales volumes, but believe that they gain reputation as a “quality” brand and can earn more from fewer, higher priced sales than if they allowed retailers more freedom. Such behavior does not benefit consumers, as this pricing is specifically designed to be anti-competitive and to maintain higher prices than would otherwise appear in the marketplace.

The auto dealers’ claim that they will bring price competition but the process is weakened by the presence of existing competition from other luxury car dealers. Given that Tesla’s Model S costs from $70,000 to $90,000 depending on options it seems likely that their customers are well-educated and financially competent. They understand that other luxury cars are for sale and can easily find out the prices of those competitors. If Tesla is charging a higher price for their car in its website, customers choose to buy a different model available from the retailer. The idea that competition between Tesla dealers would be fiercer than the competition between, say, Tesla, BMW, and Mercedes dealers is unlikely. Tesla customers are also quite able to sell their old cars to an auto dealer if they choose.

Similar restraints exist for other auto manufacturers. If you use a car company website to custom build the exact model you want, it displays a price, but any sale comes through a selected local dealer. Here the manufacturers have franchise contracts signed with the dealers, they cannot set up another amount due to market needs. This situation leads to losses to Tesla when it has to customize the car with more advanced parts to serve the customer need.

After all, if Tesla could force retailers to sell only at a specific price, then from the consumer’s point of view including a retailer separated from the manufacturer adds no advantage. In order to avoid this, Tesla planned to set up its own showroom and this decision made room for lots of speculations from the retailers.

Meeting the customer demands is a big problem associated with the Tesla stores and galleries. Most of them are located in commercial locations and are not permitted to take the orders or display any price related information which would be a trait of dealership. The sole purpose of the stores is to promote the Tesla cars and take orders which are usually done online and the orders are directly delivered to the customers. This shipment originates from California which adds up to the delay in delivering the cars. If there is a huge number of orders from a single state the dealers do not have the space to accommodate the orders that are shipped.

This problem can be solved by distribution centers which do not have any legal restrains as stated for the other option. The cars can be stored and sent for delivery based on the customer demand. This promotes timely delivery and handling huge orders without disappointing the customers. As there is a good platform for receiving the orders but not an option to store them in the dealerships, DC’s would be a good option to help storing and distributing the orders.

Although Tesla has always done things differently, as production is set to significantly ramp up, the question is whether it will have to become more like a normal Original Equipment Manufacturer(OEM) in regards to logistics and supply chain, or will it continue to change the automotive industry in even more ways?

Our work is to design the supply chain which is responsive and efficient in meeting demands and deliveries. We propose a mechanism to effectively manage the deliveries by meeting the demands. The plan is to strategically set up distribution centers covering three different regions Northeast, Southeast and Midwest in the United States using the gravity model. The idea behind this implementation is to maintain inventory to fulfill the customer needs and to be more responsive. This enables the shipment to be delivered from production unit to distribution centers and from the distribution centers to the showrooms efficiently and with less cost.

Part 1: Demand Forecast

Forecasting the demand of the Tesla model 3 was an interesting challenge for several reasons, the biggest of which is that there is no easily comparable data to use. The most sold electric car in the US is currently the Tesla model S which has sold just over 100,000 units worldwide and only 60,000 in the US. The model 3 already has 325,000 preorders, over 3 times as many units as have been sold of any electric car thus far and more than the total number of electric cars sold yearly in the US. The number of yearly electric car sales was given a simple regression analysis ignoring the model 3 launch to predict the size of the market at the time of the launch and onwards through 2020. These were listed in table 1.

Table 1

Year Total Electric Car Sales Forecasted Tesla Model 3 Sales

2017 44,847 191,436

2018 56,272 229,436

2019 68,642 274,979

2020 81,956 329,563

In the end it was decided to find the average growth in sales each year for Tesla and its competitors and apply this rate of growth to the model 3 starting at the number of US preorders. Currently about 59% of Tesla’s sales are in the US alone so it was assumed that the demand in the US in 2017 would be equal to 59% of the current number of preorders or 191,436 cars. After looking a several different ways to forecast the demand it was determined that we would find the average growth rate of sales between the years and apply that same growth rate to the model 3. While looking at yearly sales of the Tesla Model S and its direct competitors an average growth rate of 20% each year was found for electric car sales and this rate was applied to the new model 3 starting at 191,436 cars in 2017. The forecasted values are in table 1.

Part 2: New Regional Distribution Centers and the Gravity Model

The locations of the new regional distributions centers will be determined by the associated demand and location of the Tesla dealerships in each region. This will be accomplished with the gravity model which relates the cost of shipping the supplied demand for a distance between a source and a market. An Excel file has been attached with this document where you can see the full use of the gravity model.

In order to obtain data to be implemented into the gravity model, the following two assumptions had to be made. The first assumption is that transportation costs are the same across each region of the United States. This means that the cost of shipping one unit of demand one mile is the same in each region. For example, the shipping cost per mile of one car from New York City to Massachusetts would have to be the same as shipping to Washington DC. This assumption allows us to ignore price fluctuations in gasoline or toll road expenses that might otherwise complicate the model. The second assumption is that the demand for Tesla cars in each dealership is proportional to the number of plug-in electric vehicle (PEV) registrations in 2014 in each state divided by the number of dealerships in that state. This means that although actual demand data for each dealership cannot be obtained, it will suffice to assume that the proportional difference in the number of registered PEV’s betweens two dealerships is the same as the proportional difference in demand. In order to obtain the

number of registered PEV’s belonging to a particular dealership amongst many in one state, we also assume that the demand is divided equally amongst each dealership of that state. While this may not be the case in the real world, the lack of dealerships across each state causes many cities with dealerships to pick up the demand of cities without dealerships. While the exact extent to which a dealership picks up this additional demand is unknown, it is reasonable to assume that Tesla placed dealerships so that they fulfill demand equally. This is evidenced by the fact that Tesla has placed multiple dealerships in some cities to satisfy higher demand.

Implementation of the gravity model proceeded as follows. State PEV registrations were obtained from energy.gov, the Office of Energy Efficiency and Renewable Energy, for the year 2014. Dealership longitudes and latitudes were taken from Google Earth and were converted to radians. The registrations were divided equally amongst each state’s dealerships. An arbitrary distribution center at 0 North, 0 West was set for each region. The distance in miles between each dealership and this distribution center was calculated using the haversine formula for computing distances between two longitudinal and latitudinal coordinates. The haversine formula is shown below where lat1 and lon1 correspond to the dealership location, lat2 and lon1 correspond to the distribution center location, and r is the approximate radius of the Earth, 3961 miles.

dlat = lat2 - lat1dlon = lon2 - lon1a = SIN( dlat / 2 )^2 + COS( lat1 ) * COS( lat2 ) * SIN(dlon / 2 )^2-c + pi = 2 * ATAN2( SQRT( a ), SQRT( 1 - a ) )c = ABS( ( -c + pi ) - PI() )d = r * c

Each distance was multiplied by the respective proportional PEV registrations to obtain the demand times distance for each dealership. The sum of these represents the total demand times distance for an entire region. This sum was minimized using the Excel solver function. The values chosen to change were the arbitrary longitude and latitude coordinates given for the new distribution center. The result was the optimum longitude and latitude coordinates for each region shown below.

Northeast:

40.7408°N, 74.0616°WHudson, outside of Jersey City near NYC

Southeast:33.8094°N, 84.3830°WFulton, outside of Atlanta, GA

Midwest:

41.8903°N, 85.1952°WBronson, MI

These locations represent the most cost effective locations for supplying demand to each region’s Tesla dealerships. All regions avoided major bodies of water. In the event that the optimal distribution center for a particular region was in water, a constraint would have been made in the solver function. This constraint would be expanded as necessary until the coordinates appeared on land. This was the preferred alternative to attempting to set specific bounds for an irregularly shaped body of water. However in the case of the three chosen regions of the United States, neither method was required. The locations themselves are about what one could expect from PEV registrations data. The coordinates are near large population centers that either had multiple dealerships or had significantly more demand than the dealerships of neighboring states. As will be shown in the third part of the project, although these locations minimize transportation costs, more analysis is required to minimize the inventory cost associated with the new distribution centers and their respective dealerships.

Part 3: Functionality of DCs

Our motive behind analyzing the functionality is to reduce the overall cost of the supply chain network. The network involves shipping cars from Tesla Factory to distribution centers and from distribution centers to dealerships. In this paper, we have tried to optimize the total cost of supply chain operations of shipping from a distribution center to dealerships. The overall cost of these operations will include the inventory carrying cost of all dealerships, fixed order cost and transportation cost for shipping cars and

after-market parts to dealerships from a distribution center. This goal can be achieved by formulating the overall cost problem into a mathematical model.The overall cost of supply chain network starts from calculating total cost for a single dealership.A single dealership’s total cost without transportation cost:

Y (Qi) = H iQi2

+ Ai

H i = holding cost of dealership i per unit per unit timeQi = quantity to be shipped from DC to dealership i.Ai = Fixed order cost for dealership i.

The first term of equation is inventory carrying cost for a single supplier and second term is the fixed cost incurred every time a dealership places an order. The individual costs of dealerships contribute to supply chain network cost.

All distribution centers have a truck fleet consisting of different types of trucks to transport products. The transportation cost for different types of trucks is different depending on their capacities. To use the truck fleets more efficiently we decided to divide the dealerships associated with a distribution center in each region into districts. There will be a dedicated truck fleet for each district at all distribution centers. The dealerships in a district will have equal replenishment cycles but the quantities may differ from each other depending on the demand from a dealership.

The distribution centers are located close to dealerships, therefore, using trucks for transportation is the best way to be responsive and cost effective. The maximum number of districts will be equal to the number of dealerships because each dealership can be a district by itself and the minimum number of districts will be 1 i.e. all dealerships in one district. Let a district is indexed by j, a variable can be defined for selecting dealerships in a district, let there are n number of dealerships that are indexed by i, therefore,

X ij = {1, if dealership i is∈district j0otherwiseLet there are m types of truck indexed by k and each district has Z number of k type trucks.Therefore, Z jk is the number of type k trucks in district j

Let type k truck has capacity C k and full truck load transportation cost of Rk. The demand from each dealership is indexed by λ i.The total cost for transporting products from DC to dealerships can be given as:Total Cost:

Y (Qi , X ij , Zkj) = ∑

j=1 , i=1

n H iQiX ij2

+ ∑j=1 , i=1

n

A i X ij + ∑j=1 , k=1

n ,m

Rk Zkj

• First term is the inventory cost

• Second term is the fixed order cost

• Last term is the trucking cost

The total cost equation is our objective function and the goal is to minimize it. The mathematical model will be:Minimize - Y (Qi , X ij , Zk

j)

Subject to- ∑j=1

n

X ij = 1 (where j = 1, 2, 3……………n)

∑i=1

n

Qi X ij = ∑i=1

n

λi X ij (where i = 1, 2, 3……….n)

∑i=1

n

Qi X ij ≤ ∑k=1

m

C k Zkj (where i = 1, 2….n, k=1, 2…..m)

ZkjIntX ij Binary Qi≥ 0

This is a Mixed Integer Non-Linear Model where the first constraint in model shows that a dealership can be in one district only at a time, the second constraint shows that the quantity shipped to each district cannot be less than its demand and the third constraint defines that the quantity to be shipped to a district cannot exceed the capacity of the trucks available. The number of truck in each district will be integers and Xij is binary. This problem will give us:

which dealership should be in which district how much quantity to be shipped to each district how many different types of trucks are required Minimum cost of the Distribution operations

Example: Let us suppose there are 5 dealerships associated with a distribution center and each them has different demand, there are 4 types of trucks with different capacities and transportation costs. We want to find which dealerships should be included in which district and how many trucks are needed to get the job done at minimum cost possible.

Dealerships1 2 3 4 5

Holding Cost 50 45 55 48 60Fixed order cost 15 20 15 15 15Demand 100 110 250 180 160Trucks type Capacity of

trucksCost of transportation

1 19 $80 2 39 $110 3 47 $400 4 67 $200

Solution: For this problem the deciding variables areQi, X ij and Zkj

The notation used in formulation can be defined as:Let dealerships are indexed by i= 1, 2, 3, 4, 5Let districts are j= 1, 2, 3, 4, 5

Locating dealerships into districts; LetX ij = {1, if dealership i is∈district j0otherwiseTruck type is indexed by k= 1, 2, 3, 4, with capacity C kand costRk.Using the given data, the problem can be formulated as:

Minimize: ∑j=1 , i=1

5 H iQiX ij2

+ ∑j=1 , i=1

5

A i X ij + ∑j=1 , k=1

5,4

Rk Zkj

Subject to: ∑j=1

5

X ij = 1

∑i=1

5

Qi X ij = ∑i=1

5

λi X ij

∑i=1

5

Qi X ij ≤ ∑k=1

4

C k Zkj

ZkjIntX ij Binary Qi≥ 0The Excel solver can be used to solve this problem. The values of variables come out to be:Q1 = 100, Q2= 110, Q3 = 250, Q4 = 180, Q5 = 160X11 = 1, X51 = 1X25 = 1, X35 = 1, X45 = 1Z21 = 3, Z4

1 = 3, Z25 = 11, Z4

5 = 2The results for quantity to be shipped to dealerships are satisfying the demand of each dealership.The X ijvariable results show that the dealership 1 and 5 are kept in district 1 and dealerships 2, 3, 4 are kept in district 5.

The district 1 will have 3 type 2 trucks and 3 type 4 trucks.The district 5 will have 11 type 2 trucks and 2 type 4 trucks.The distribution done by using these districts and truck fleets will work at minimum supply chain network cost for satisfying the demand per unit time, which is $23590.

Conclusion

In the United States Tesla needs to be able to meet demand of 191,436 model 3’s in 2017, 229,436 in 2018, 274,979 in 2019, and 329,563 in 2020. This is based off the number of preorders in the United States and an average sales growth rate of approximately 20% in the country. To help meet this demand, supply dealerships and service stations, and improve overall responsiveness in the supply chain we recommend the construction of regional distribution centers in the United States. Through the use of a gravity model optimal locations have been found for the midwest, southeast, and northeast areas of the country. Furthermore a method has be determined to find the optimal operating cost of potential distribution centers. The next step for Tesla should be to choose several potential locations near each optimal location. Each potential location’s total cost must be calculated by adding the cost of land, construction cost, optimal operating cost, and cost of shipping the distribution center. While looking at potential locations it is important to take the presence of railways into account for potential savings when shipping from the factory to each distribution centers. Once the total cost of each potential distribution center has been found the best available one can be chosen to satisfy demand and minimize cost.

References

"FACT #876: JUNE 8, 2015 PLUG-IN ELECTRIC VEHICLE PENETRATION BY STATE, 2014." Energy.gov. Office of Energy Efficiency & Renewable Energy, 8 June 2015. Web. 9th,May2016. <http://energy.gov/eere/vehicles/fact-876-june-8-2015-plug-electric-vehicle-penetration-state-2014>.

" Earth." Google Earth. N.p., n.d. Web. 09 May 2016.

"Calculate Distance and Bearing between Two Latitude/Longitude Points Using Haversine Formula in JavaScript." Movable Type Scripts. N.p., n.d. Web. 09 May 2016. <http://www.movable-type.co.uk/scripts/latlong.html>.

"Model 3." Tesla. N.p., 2016. Web. 9 May 2016.