The purpose of this report is to investigate the use of Business Intelligence (BI) at Tesla, and identify future challenges faced by the company to further understand how they can affect the organizational environment.
Our first step was to analyse Tesla’s business model and break it down into 5 keys areas of operation: (Manufacturing, Marketing, Customer Interface, Car Interface and Utilities/Technologies) we have used these as lenses through which to assess the BI implementation at Tesla.
We began by describing Tesla’s background as a Silicon Valley-based company that manufacturers not only cars and battery packs but also infrastructure components.
Secondly, this analysis goes deeper into the organisations decision-making strategies for a better understanding of its behaviours and how they have contributed to its success and ability to disrupt a mature and established industry.
Given that this report heavily focuses on Business Information we have then provided insights into the Information Systems used within Tesla and how specifically they have provided the platform that has enabled BI advances and competitive advantage.
We have drilled down into some of the specific BI Issues faced by a company as heavily technologically dependant as Tesla and highlighted the insights into current and future challenges that they have given us.
In conclusion Tesla are utilizing BI as a critical driver of their vertical integration, enabling their extremely fast growth and market expansion. Their meteoric rise must be tempered with robust risk and compliance management to ensure that their very strengths do not become their biggest weaknesses. If however they continue to walk this fine line, then they will not only be successful in their chosen industry but will be a key contributor to the future of our environment.
Organization Background
Tesla’s mission is to accelerate the transition of the world to sustainable energy. Tesla believes that the sooner the world no longer relies on fossil fuels and targets a zero-emission future, the better. To create a sustainable energy ecosystem, Tesla manufactures electric vehicles and energy solutions, Powerwall, Powerpack and Solar Roof .
Figure 1 – Tesla Mission and Vision Statement
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Martin Eberhard and Marc Tarpenning created Tesla Motors in 1997, the name was a compliment to engineer and physicist Nikola Tesla, a pioneer in production, transmission, and application of electric power. Tesla was set up at a very turbulent time for the electric car industry, in early 2000 the industry had destroyed several designs, including the prototype of GM EV-1, according to “Eberhard and Tarpenning wanted to prove that electric cars could be better than fuelled cars”. In 2004 Elon Musk, now CEO, invested $6.3 million in Tesla. The financial support allowed the brand to produce “not only not only all-electric vehicles but also infinitely scalable clean energy generation and storage products”
Since its beginning Tesla always attracted the media attention, not only for its innovative cars, but also for its financial results. In 2019 the company reported record numbers in terms of sales and production. Tesla generated $24.6 billion revenue, and an annual vehicle delivery and production records of 367,656 and 365,232, respectively . In July 2020 Tesla became the world’s most valuable car maker, with a market value of $209.47bn (£165bn), overcoming Toyota by $4bn .
Figure 2 – Tesla Quarterly Revenue Versus Profit/Loss
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We have analysed Tesla’s Business Intelligence utilization through a breakdown of key areas of areas and components that make up their business model, this report will investigate further each of these areas.
Management Decision Making
Manufacturing
Stein states that “to be rational is to reason in accordance with principles of reasoning that are based on rules of logic, probability theory, and so forth” . According to (Edwards, 1954; Parker & Fischhoff, 2005) cited by normative decision theories prescribe how people should be making decisions emphasizing four fundamental processes: Belief assessment, Value assessment, Integration and Metacognition.
Tesla’s approach to decision making is to go to through as much detail as possible before concluding. Tesla’s classical/normative decision-making approach has been reported, Elon Musk poses six key questions before making any big decision. Quoted below :
“Ask a question
Gather as much evidence as possible
Develop axioms based on the evidence, and try to assign a probability of truth to each one
Draw a conclusion based on cogency to determine: Are these axioms correct, are they relevant, do they necessarily lead to this conclusion, and with what probability?
Attempt to disprove the conclusion. Seek refutation from others to further help break your conclusion.
If nobody can invalidate your conclusion, then you are probably right, but you are not certainly right.”
Marketing
Refusal to enter the dealership model by Tesla can easily be understood as a great usage of the OODA Loop model. This model can be described as 4 stage process from which a company can gain competitive advantage over their opponents through a cycle of observation, orientation, decision, and act. Regarding this model, the author affirms that “by being able to respond more quickly to the changing environment and act, the individual or organization has a greater likelihood of success” .
With that said, Tesla has done it beautifully. They observed their competition and understood what they should not do and act on it.
Customer Interface
Tesla have employed an extremely high level of vertical integration in their business model, particularly in comparison with others in the industry. The control of their supply chain when it comes to the technology delineates them from their competitors who are increasingly using an on demand’ Software As A Service (SAAS) model, this is an example of Tesla’s use of the normative decision making process.
This vertical integration is also present on the financial side. Tesla now offers, a smooth customer journey providing a single point of contact for all car related financial services. Access to its own data allows it to make more robust risk decisions. This novel solution is an exemplar of non-programmed decision making, allowing them the agility to bypass the traditional insurance industry as soon as it became inconvenient.. It demonstrates that despite its flat structure, Tesla is an Autocratic A2 style organization according to the Vroom-Yetton model.
Car Interface
Tesla autonomous driving solution is another interesting use of classical decision making. Tesla clearly felt that Autonomous driving would give them a USP, and in order to disprove this conclusion and follow classical decision making, they took the high risk approach of including the technology in all cars from day 1 even though the feature was not enabled.
This allowed them to gather data and eventually ratify the business decision to release Autonomous driving as a feature in 2014. “This interface is currently being validated before being released to customers, and we look forward to its progressive deployment”.
Complimentary Innovation/Technologies
One of the key criticisms of Electric vehicles is that if everyone switched to them, we would not have the infrastructure to produce and more importantly store the electricity needed. The advantage of Tesla’s classical decision-making process is that as part of step 4 this issue would have been recognized and is what has driven Tesla to create their Powerwall, Solar and Utility storage businesses.
The UK has officially approved Tesla as an electric distributer and as which they have started to provide under local authorities of the UK. According to Elon Musk’’ Tesla energy, which is using in automaking will become a distributed global utility and it will help automobile business to grow.
System and Information Technology
Figure 3 – An overview of the Tesla Information Systems – derived in our research. (NB – This is not exhaustive and is merely intended as a visualization aid/guide)
Manufacturing
The company relies on the use of information systems and robots to automate, innovate, and increase its production line, due to this there are only 43 steps/stations, 25-33% of the number found in traditional auto manufacture For example the Smart Paint-shop uses a series of computer-generated designs, along with 4D modelling, to create a high-quality paint application while reducing energy usage.
Tesla has developed its own ERP system called Warp Drive. “Warp centralizes, integrates, and streamlines business processes in Supply Chain, Product Planning, Inventory, Sales Order Management, Asset, Finance, and more” In November 2020, Tesla announced the implementation of AI-powered software that will allow Tesla to upload a CAD package of the desired model, which will plan production, procure parts and services, produce detailed work instructions and offer instant pricing.
Tesla’s fully automated facilities and the “design ourselves” approach gives Tesla’s higher flexibility, allowing the company to build cars from scratch, optimize efficiency, simplify and increase production.
Marketing/Sales
From a marketing perspective, Google Analytics is one of the main sources of analysing and reporting on data gathered through social media and the organization’s website. Tesla run this platform on their website so that patterns and all sorts of data can be manipulated regarding their established customers and future ones. From tracking customers behaviour to acquisition, conversion and retention rate, Google Analytics makes it possible for businesses to be on top of trends and predict user movements.
Customer Interface
Tesla’s originally used the SAP ERP module, however as Tesla scaled up and hit production issues they moved to an in-house ERP platform which gave Tesla two advantages, it was bespoke, and it was quicker to implement.
Initially Tesla used Salesforce as its CRM system, but again they have replaced it with a bespoke Tesla designed system, this appears to be in line with a new trend in the marketplace which Tesla are joined in by VW and Lyft where specialization is used to stand out.
Tesla’s decision in moving their Customer Interface systems in-house is key from a BI perspective as they control their data exclusively, for example using predictive modelling across systems allows them to notify users to visit a service centre for repairs before actual failure. Tesla are in essence using BI to provide proactive as opposed to reactive maintenance.
Car Interface
Tesla’s Autonomous driving is a huge Data asset, every car collects data for Tesla’s use in Artificial Intelligence and Machine learning. This gives Tesla 2 advantages over rival Waymo, that this is real world miles from real drivers rather than dedicated data collection vehicles and the sheer volume of data.
It could be argued that Tesla’s Full Self Driving computer is itself a miniaturized real time Data Warehouse receiving millions of inputs from various sources and acting on the conclusions reached autonomously. Tesla refer to it as their Neural Network (see fig 4). The ‘computer’ used to run it is actually a purpose-built chip referred to as a System On a Chip (SOC) and is 21 times faster and more powerful than any competitors.
Figure 4 – Neural Network Architecture
Complimentary Innovation/Technologies
Tesla revealed Autobidder in 2020 which provides the ability for independent electricity producers, utilities, and relevant partners to make money from their batteries. It is a controlling and transferring platform that provides management of assets based on value; and gives the ability to optimize portfolio and take advantages in market pricing. Autobidder has been operating at Hornsdale Power Reserve (HPR) in south Australia and it could provide competition and decreased energy prices through market bidding.
Tesla as a solar energy company is leveraging the Internet of Things (IoT) to enable them to keep track of the power generation by solar panels and also the usage cycles and data from their power walls. This will allow them to generate more accurate information on power generation and usage. This information can be helpful for them in management and maintenance activities and provide key analytics in terms of future investment.
Figure 5 – Information Technology Argument Summary
Relevant BI Issues
Data Protection – With the increasing collection of data across many geographies this presents a significant regulatory overhead to Tesla in terms of ensuring that they are abiding by all relevant Data Protection guidelines and regulation and most importantly protecting their users from the adverse use of Data and ensuring that their data security is robust enough to avoid the significant risks of theft and/or vehicle sabotage.
Compliance – Tesla are increasingly becoming a financial player in addition to their core business, this brings with it a need to comply with a wider and potentially more restrictive set of compliance standards. There are further risks associated with this, for example the Directors and Officers insurance of the business is already underwritten directly by Elon Musk’s fortune due to an inability to source this security from the normal institutions.
Safety and Liability – There are significant safety issues because of autonomous driving, and liability issues as ‘will a crash be attributed to the driver, or to Tesla or to the wider integrated safety system’. Soon, where autonomous vehicles are standard, these networks will most likely also communicate with cars from other suppliers as well as other technologies such as traffic cameras, road-based sensors or cell phones.
Current and FUture Challenges
Environmental – Not a challenge but an opportunity, the anticipated re-entry to the Paris Climate accord by the USA is just one clear indicator of travel towards a more sustainable future that Tesla is well placed to take advantage of. Closer to home we are seeing this manifest in the UK government policy to ban Fossil powered cars, now accelerated to 2030 instead of 2040.
Organizational – Tesla employ a flat structure with much empowerment to employees. There is a minimal hierarchy with Elon Musk at the peak with 10 Department Heads beneath him and then is relatively flat from there, this allows Tesla an environment that nurtures innovation and pace. However there is a significant risk that as the staff numbers grow this model will become inefficient.
3rd Party Integration – Whilst Tesla has great in house systems their move towards internally developed systems has impacted their ability to interface with their suppliers systems, they could improve their value chain and reduce operating costs by interfacing with supplier systems and using more advanced inventory management techniques.
Brain Drain – Tesla face the risk of loss of innovations and competitive edge to competitors via defection of high-level executives. Silicon Valley is an environment extremely fertile to start-ups and lends itself to new start-ups leveraging similar innovations to Tesla. A good example of this is new start-up Tekion launched by Tesla’s former CIO Jay Vijayan, which is a SAAS CRM platform that allows car manufacturers to integrate their customer journey through a retail cloud, essentially providing a virtual vertical integration of automotive supplier, manufacturer and dealer networks without needing them to share a single corporate entity.
Manufacturing and Automation – Tesla manufacturing is highly dependent on software systems, “The Model 3 body line is now 95% automated, including the transfer, loading, and welding of parts” Systems are often complex, have defects and are subject to hacker attacks. Complexity and deficiencies of systems has delayed the launch and production of Model 3, which was caused by a software issue in the assembly line. Issues with the software meant that it had to be re-designed from the ground up and significant sections of the plant had to be redone.
COnclusions
Whilst the data and BI systems bring efficiency and greater insight, they have proven to lack sometimes in creativity and flexibility. In both Tesla and other tech firms, the future of Business Intelligence has a rigorous management decision-making mechanism in place. Such mechanism in turn would ensure that the best combination of Artificial and Human Intelligence is used to achieve the companies’ goals.
Given that the biggest growth available to Tesla in the future will come from leveraging their Autonomous driving and IoT technology in home and utility energy provision, the most significant area for development for them is managing the associated data protection and security risks to ensure that their biggest advantages do not become their greatest liability.
Tesla Vertical Integration is one of the key contributors to their success not just in terms of the obvious advantages this brings in terms of supply chain optimization, but also in terms of how the controlled and closed loop nature of their in-house Information Systems design means that their collection and processing of data is superior both in scale and ease of use/processing, when compared to their competitors. This gives them a deeper data analytics ability and responsiveness which will help them keep their competitive edge and spot opportunities for further synergistic diversification in the future.
What else has Elon and his Data Analytics seen in our futures that the rest of us have missed?