The supply chain of 2025 will re-imagine the way the world does business with digital factory and retail technologies. Within the next decade, we will move away from mass-produced products to more meaningful, personal items created in small quantities, and Panasonic IoT solutions and robotics will make this all possible.
When you think about accelerating impacts and long-term solutions to current supply chain challenges that impact the 3P’s (people, planet and profit), we need to adopt and develop sustainable frameworks with a holistic life-cycle perspective. There is a ton of innovation happening in the CPG space (Levi’s, Unilever, PepsiCo, etc.)
Shifting from the current ‘take-make-waste’ linear model to the circular economy is critical for businesses to continue to thrive and meet society’s needs. Waste volumes are projected to increase from 1.3 to 2.2 billion tons by 2025, and with nearly 9 billion consumers on the planet including 3 billion new middle class consumers by 2030. The challenges of addressing waste and meeting increasing demand are unprecedented. Therefore it is imperative businesses continue to re-evaluate raw materials, design, manufacturing, consumption, and end of life to keep materials and products continuously flowing through closed loop systems.
How is your company innovating in product life cycle management from design and inception to sustainable product packaging? How are you personally adopting a sustainable mindset in your home, the daily choices you make as a consumer to move toward a circular economy? The bigger question is how are YOU INFLUENCING this change?
The history of Supply Chain Management has evolved since its’ roots in the early 1900s. From improving labor processes of basic material handling and freight transportation, to more sophisticated approaches of balancing cost and efficiency trade-offs, the concept of a supply chain is no longer siloed. It requires integration of supplier-customer relationships, process synchronization, and data harmonization in a complex, dynamic network that is susceptible to vulnerabilities in a global environment. Critical processes to this relationship include real-time communication, collaboration, trust, and transparency that yield mutually beneficial outcomes and competitive advantage. In today’s world, there is a growing prevalence in leading firms advancing toward the adoption, development and implementation of Blockchain technology as a backbone of business operations. This case dives a bit deeper into Blockchain, a novel technology with the strong potential to revolutionize the Global Supply Chain. The goal of this analysis is to discuss: 1) the key technical and economic aspects of Blockchain, 2) the current Blockchain innovators, barriers, and obstacles to Marketplace acceptance, 3) the business case for Blockchain, and 4) future applications and implications of Blockchain technology.
Sustainable, much like organic, is used loosely as a marketing ploy. More often than not, countless companies use a concept called “green-washing.”Green washing is when a company, government or other group promotes green-based environmental initiatives or images but actually operates in a way that is damaging to the environment or in an opposite manner to the goal of the announced initiatives. This can also include misleading customers about the environmental benefits of a product through misleading advertising and unsubstantiated claims.
I use this greenwashing index greenwashingindex.com/about. There are more robust reporting initiatives, but this site is simple and give tips to the basic consumer on how to spot greenwashing and outlines the methodology behind the index. I highly recommend EcoVadis for larger organizations looking to integrate a desktop, cloud-based sustainable compliance solution. EcoVadis operates the first collaborative platform providing Supplier Sustainability Ratings for global supply chains. With a focus on maintaining quality and integrity, EcoVadis has managed to also grown quickly to meet this increasing need. Since its founding in 2007, EcoVadis has become a trusted partner for procurement organizations in more than 150 leading multinationals worldwide including Verizon, Nestlé, Johnson & Johnson, Heineken, Coca-Cola Enterprises, Nokia, L’Oréal, Bayer, Alcatel-Lucent, ING Bank, Air France-KLM, Centrica/British Gas, BASF, and Merck. Combining People, Process and Platform, EcoVadis has developed the industry-leading team, innovative technology, and a unique CSR assessment methodology that covers 150 purchasing categories, 110 countries, and 21 CSR indicators. More than 30,000 companies use EcoVadis to reduce risk, drive innovation and foster transparency and trust between trading partners. EcoVadis is driven by a diverse team of over 300 talented professionals from 40 nationalities committed to a real impact on the environmental and social practices of companies around the world.
I’m sharing great insights from Innovation Enterprise via Micha Veen who explains simple tips to master supply chain excellence pinned Operational Innovation.
Operational or Supply Chain Excellence has been one of the buzzwords that is often heard around senior Supply Chain Execs. However, is excellence the right terminology, or do we need to rename ‘excellence’?
Due to globalization, continuous creation of new small ‘global’ businesses that can compete with established organizations, leading supply chain organizations have started to look beyond ‘operational excellence’, best-in-class, benchmark data and industry metrics, towards using a combination of their own internal and tailored external relevant data to continuously review, assess, and adopt evolving leading-edge processes, technologies and behaviors to stay ahead in this ever increasing competitive business landscape.
This new approach, named Operational Innovation, has become an effective methodology to deliver transformational impact through the following elements…
Innovative solution design
Instead of spending a lot of time and effort in designing the optimal operational and supply chain solutions, successful organizations focus on creating a solid solution foundation, which is constantly reviewed and improved with cross functional teams to deliver cross-divisional, fit-for-purpose solutions.
Instead of phased hand-offs between subject matter experts, technology specialists, operational teams, sales, finance, etc., leading innovative supply chain solutions should be created through continuous close collaboration with all impacted process participants at every stage of the supply chain journey.
Use of Robotics and Blockchain Technologies
A recent article (How will manufacturing robotics change in 2017) describes how robotics will change the industry as early as 2017. The article describes how by 2019, 35% of leading companies in logistics, health, utilities, and resources will start implementing robotics to automate their operations. Additionally, supply chain blockchain technology has started to be utilized in supply chain organizations to deliver additional benefits. A recent article describes clearly the impact that Blockchain has on Supply Chain.
End-to-end Solution integration
The key to delivering true Supply Chain Innovation is the manner in which organizations integrate end-to-end processes, technologies, data, and internal vs. external organizational units. Due to the external focus on innovative technologies, many organizations are still only focused on technology integration, but leading businesses have started to explore how different cloud solutions can be integrated across their partners and customers, creating hybrid learning organizational models which go beyond the traditional joint venture organization models.
Continuously generate value
In supply chains it’s crucial to continuously generate value. Through the use of innovative technologies, solution partnerships, operational models, etc. leading supply chain organizations are known to continuously review, adapt and improve their supply chain environment to deliver operational innovation. It allows supply chains to continuously deliver ‘new and improved’ excellence.
In today’s world, Supply Chain Excellence is not enough. There is no ‘end-station’. It’s critical for supply chain organizations to adopt an ongoing innovation journey, which requires people with the right mindset, experience levels, attitude and curiosity to deliver supply chain innovation….
This is a recent article written by a brilliant colleague Harry Goodnight. Great insights and perspectives on blockchain technology and why decentralization is a good thing for modern supply chains.
In business and economics, decentralization often refers to the ability to participate in a market and exchange value between peers without the interference of a third-party intermediary who most likely controls and restricts barriers of entry. As Ethereum co-founder Vitalik Buterin explains in his blog post “The Meaning of Decentralization,” blockchain is politically and architecturally decentralized, meaning no entity one controls it and there’s no central point of failure in its infrastructure. In this way, a decentralized supply chain would allow for a frictionless vehicle of business-to-business value exchange amongst even the smallest players in the industry.
Decentralization is defined as the transfer of power away from a central location or authority. As a concept, it is not new; as a business model, however, it is a powerful idea. Some sociologists claim that decentralization and centralization theories have actually been occurring in cycles for the last 4,000 years, causing the rise and subsequent fall of ruling states and empires. Throughout history, the core theory behind decentralization has remained the same: dispersing power from authorities and empowering smaller, individual entities with the ability to act in their own self-interest.
Why decentralization is necessary for modern supply chains
This is especially necessary in the supply chain industry, which has historically suffered from a number of issues that hinder its efficiency. Its main roadblock is that current supply chains are unable to become agile, which poses a significant problem in a market in which they must be able to change their configurations quickly and continually to meet the constantly-changing dynamics of supply and demand. Another major disadvantage is that methods of communication tend to vary greatly, with some companies still relying on manual paperwork. As a result, data storage becomes locked away in in proprietary systems that don’t allow for collaboration.
Supply chain companies also tend to face cultural and organizational issues, such as executing operating plans due to corporate goals, board restrictions and the competitive nature of the market. Consequently, companies have revoked social contracts, mistreated skilled laborers and underutilized their professional talent assets.
This mismanagement has serious financial consequences: for instance, $4.2 trillion is locked up in net working capital in today’s supply chains. By allowing today’s virtual supply chains to break from the company-centric, server-based environments in which they currently find themselves, they will become less brittle, more scalable and fully leverage the underutilized skills and assets available in modern-day business networks. Even a 1% improvement in Invoice-to-Cash cycle times would immediately return about $42 billion in cash to operations.
How can blockchain remedy the issues of centralization in supply chains?
When looking at its positive implications, blockchain is the most logical next step for supply chain managers and logistics providers. Blockchain was brought to the mainstream through cryptocurrencies like bitcoin and Ethereum. It creates an unchangeable digital ledger that provides a record of financial transactions in chronological order. This technology has been increasingly adapted to address gaping deficiencies in other fields, from education to voting to real estate. Through blockchain, massive networks of decentralized autonomous individuals and organizations can grow and operate seamlessly within a decentralized, distributed operating platform.
Blockchain also provides an efficient and viable solutions to the aforementioned hurdles that are restricting today’s supply chain. Specifically, it offers opportunities to synchronize processes that occur within supply networks, resulting in reduced Cost-of-Goods-Sold (COGS) and more cash freed from working capital.
The solution to many of these recurring issues in supply chain primarily involves people. By creating networks of skilled individuals and decentralized autonomous organizations, immense value can be brought to companies, supply chains, and customers. These networks align economic incentives so that everyone prospers, based on their contributions of time, skill, and intellectual property. These contributions are monitored and administered through outcome-based smart contracts on the blockchain. This new vision of decentralization has the potential to radically transform the supply chain space.
Sharing point of view from Greg Brady is the CEO and founder of One Network Enterprises, a global provider of a secure and scalable multi-party business network. For more information, contact the author at email@example.com or visit http://www.onenetwork.com. Enjoy!
There’s a lot of buzz and hype about artificial intelligence (AI) in supply chain management (SCM). That’s understandable given its potential. AI can offer a huge benefit to supply chain managers, but only if it is based on solid fundamentals that take into account the diverse and dynamic nature of today’s modern supply chains. More importantly, it needs to consider the availability of the timely and accurate data needed to make smart decisions.
Before addressing what AI can do, it is critical to first understand what it is. In the simplest terms, AI is intelligence exhibited by machines, or when machines mimic or can replace intelligent human behavior, such as problem solving or learning. In essence, AI is machines making decisions whether that is deciding which chess piece to move where, or how to adjust an order forecast based on changing demand.
Despite its benefits, when looked at through the lens of a supply chain executive, AI is relatively useless unless it’s able to add value to support better decision-making.
Why AI Hasn’t Delivered in SCM
In the race to use AI, many companies have made attempts to implement it, but the results have been disappointing. This is because typical SCM systems today:
- Require armies of expensive planners
- Run complex engines at each step in the process and at each node in the supply network
- Are usually in conflict with other functions and/or partners
- Miss huge opportunities hidden in the network because they are locally sub optimized
- Work on stale data and thus promote bad decisions
- Use dumbed-down, over-simplified problem models that do not relate to the real world
These SCM limitations have severely suppressed return on AI investments. For example, typical Retail/CPG supply chains still carry 60-75 days of inventory. The average service level in the store is about 96 percent, with promoted item service levels much lower at the 80 percent range. The Casual Dining segment on the other hand, carries around 12 – 15 days of inventory with relatively high waste and high cost-of-goods-sold. So, unless AI can make a significant impact on these metrics, it’s simply not delivering.
Key Requirements for AI in Supply Chain Management
Having worked with hundreds of supply chain executives, on dozens of software implementations, I’ve studied the AI issue a lot. What I have found is there are eight criteria that are required for a successful AI implementation. Miss one of these and you’ll be lucky to achieve mediocre outcomes, but when you meet them all, you can indeed achieve world class results. For the AI solution to offer optimal value in supply chain, it important to ensure the following:
1. Access to Real-Time Data
To improve on traditional enterprise systems with older batch planning systems, new AI systems must eliminate the stale data problem. Most supply chains today attempt to execute plans using data that is days old, but this results in poor decision-making that sub-optimizes the supply chain, or requires manual user intervention to address. Without real-time information, an AI tool is just making bad decisions faster.
2. Access to Community (Multi-Party) Data
The ability to access data outside of the enterprise or, more importantly, receive permission to see the data that is relevant to your trading community, must be made available to any type of AI, Deep Learning or Machine Learning algorithms.
Unless the AI tool can see the forward-most demand and downstream supply, and all relevant constraints and capacities in the supply chain, the results will be no better than that of a traditional planning system. Unfortunately, this lack of visibility and access to real-time, community data is the norm in over 99 percent of all supply chains. Needless to say, this must change for an AI tool to be successful.
3. Support for Network-Wide Objective Functions
The objective function, or primary goal, of the AI engine must be consumer service level at lowest possible cost. This is because the end-consumer is the only consumer of true finished goods products. If we ignore this fact, trading partners will not get the full value that comes from optimizing service levels and cost to serve, which is obviously important as increased consumer sell-through drives value for everyone.
A further enrichment of the decision algorithm should support enterprise level cross-customer allocation to address product scarcity issues and individual enterprise business policies. Thus, AI solutions must support global consumer-driven objectives even when faced with constraints within the supply chain.
4. Decision Process Must Be Incremental and Consider the Cost of Change
Re-planning and changing execution plans across a networked community in real time can create nervousness in the community. Constant change without weighing the cost of the change creates more costs than savings and reduces the ability to effectively execute. An AI tool must consider trade-offs in terms of cost of change against incremental benefits when making decisions.
5. Decision Process Must Be Continuous, Self-Learning and Self-Monitoring
Data in a multi-party, real-time network is always changing. Variability and latency is a recurring problem, and execution efficiency varies constantly. The AI system must be looking at the problem continuously, not just periodically, and should learn as it goes on how to best set its own policies to fine tune its abilities. Part of the learning process is to measure the effectiveness “analytics,” then apply what it has learned.
6. AI Engines Must Be Autonomous Decision-Making Engines
Significant value can only be achieved if the algorithm can not only make intelligent decisions but can also execute them. Furthermore, they need to execute not just within the enterprise but where appropriate, across trading partners. This requires your AI system and the underlying execution system to support multi-party execution workflows.
7. AI Engines Must Be Highly Scalable
For the supply chain to be optimized across an entire networked community of consumers to suppliers, the system must be able to process huge volumes of data very quickly. Large community supply chains can have millions if not hundreds of millions of stocking locations. AI solutions must be able to make smart decisions, fast, and on a massive scale.
8. Must Have a Way for Users to Engage with the System
AI should not operate in a “black box.” The UI must give users visibility to decision criteria, propagation impact, and enable them to understand issues that the AI system cannot solve. The users, regardless of type, must to be able to monitor and provide additional input to override AI decisions when necessary. However, the AI system must drive the system itself and only engage the user on an exception basis, or allow the user to add new information the AI may not know at the request of the user.
AI in the Real World Today
Sounds good in theory, but how does it work out in practice? Now that we have addressed the key fundamentals, let’s look at how some actual companies have achieved applying these criteria.
For instance, one of the major problems in Casual Dining is anticipating and meeting demand for the restaurants, corporate owned or franchised. This is especially important during Limited Time Offers (LTOs). Using the eight criteria outlined above, a global, casual dining company connected to a real-time, multi-party network, and was able to rapidly achieve their objective function – excellent customer service at the lowest cost.
The company constantly monitors Point-of-Sale (POS) data, and is using AI agents to recognize and predict consumption patterns of consumers. In addition, intelligent AI agents create the demand forecast and then compare it to the actual demand in real-time. When there is significant deviation, the agents make the decision to adjust the forecast, and additional agents adjust replenishments. They then propagate those adjustments across the supply chain to trading partners in real time at all times considering the cost of change and the propagation impact.
This drove a remarkable improvement in forecast accuracy. During promotions, the company achieved over 85 percent forecast accuracy at the store level and even higher at the DC level. This represents at least a 25 percent improvement over traditional approaches.
Intelligent agents also optimize restaurant orders autonomously by recognizing the impact of projected restaurant traffic trends and impact on LTOs and therefore the orders. The system runs on an exception basis but allows the managers to review the decision criteria and override orders where the managers may have local information such as inventory issues or local store traffic issues. This has resulted in much faster order placement and order accuracy of over 82 percent, which reduces both inventory and waste dramatically while increasing service levels to the consumer. This is a significant improvement to all other known implementations in the marketplace.
Because the algorithms are highly scalable, they are processing over 15 million stocking locations continuously throughout the day.
Prior to the AI-based, multi-party execution system, restaurant managers had to interact with nine different ordering systems and manually create their own orders based on general guidelines, rules of thumb, and spreadsheet-based or manual calculations.
With AI implemented on a sound foundation, this company can now anticipate, manage, and serve demand at the lowest possible cost. During LTO’s, when demand fluctuations would overwhelm a restaurant manager, intelligent agents monitor demand in real time, and autonomously orchestrate the supply chain to align supply with demand. Thus, the company can meet its goal and maintain high service levels while reducing cost to serve.
These are not isolated results. Also in the food marketplace, another CPG-Retail implementation achieved 99 percent in-stock, in-store, with 25 days of supply (DOS) across the supply chain. The inventory results are less than half the standard DOS in this marketplace and 3 percent points higher in in-store in-stocks
AI-based solutions are being deployed at two large automotive tier one suppliers with results ranging from 16 – 40 percent reductions in inventory as well as significant reductions in expedited freight costs.
AI Delivers Value in SCM Today
As you can see, laying the proper groundwork for AI pays huge dividends. There’s no doubt that AI offers even greater promise in the future, but, as these results show, there are significant benefits and dramatic results waiting for companies that focus on the fundamentals and put AI to use today.
The beauty of AI-based solutions is that they learn and drive continuous improvement over time. They get more precise and sophisticated as they gather more data and more experience. The sooner you start, the better the results you’ll see in future, and the further ahead you will be. With the right AI solution in place, you can outpace your competitors today, and be well positioned for reaping even bigger rewards of AI’s promise tomorrow. ~Greg Brady
I’m sharing insights from Amy Augustine. She elaborates on trends in sustainability, particularly clean-energy policy. Enjoy!
As we confront a new political climate that is inspiring both uncertainty and rising citizen action, I am more convinced than ever that business must play a critical role in achieving a sustainable, equitable and clean-energy future. Bold leadership, as well as individual and collective action from influential companies and investors, is critical to ensure continued progress in achieving the ambitions of the historic Paris Climate Agreement and the U.N. Sustainable Development Goals.
Fortunately, companies we engage with here at Ceres continue to demonstrate that sustainability is not just good for the bottom line; it is the bottom line. Despite backward steps in Washington, there is unprecedented clarity in the business community – especially from the Fortune 100s – that building a healthy, low-carbon economy is irresistible and irreversible. Examples of this are popping up everywhere, although still not at the pace and scale we need.
These seven key corporate trends are ready for primetime and will be critically important in advancing our sustainability goals, no matter the political winds in Washington.
1. Corporate support for clean-energy policy is accelerating
Corporate energy buyers want renewable energy – and not just to help them meet their own greenhouse gas reduction goals. Renewable energy prices are increasingly cost-competitive in many parts of the country, and they remove the long-term risks associated with fossil fuel energy price volatility.
More than 900 companies and investors are calling on President Trump and Congress to keep the U.S. in the Paris Climate Agreement and to support low-carbon policies in the U.S. And nearly 100 global companies have signed on to to the RE100 initiative, a commitment to source all of their energy from renewables.
Lacking a national carbon mitigation strategy, states and cities will continue to be the platforms on which we’ll see meaningful clean-energy progress. In Michigan, Ohio and Virginia, among other states, companies are helping to shape policies that strengthen and increase access to renewable energy, leading to more clean-energy investment and jobs in those states.
Stay tuned for our upcoming Power Forward report this spring documenting these trends among Fortune 500 companies.
2. More investors expect companies to disclose climate-related risks and opportunities
The Task Force on Climate-Related Disclosures (TCFD) – whose leadership includes Ceres member companies such as Bloomberg LP and JPMorgan Chase – recently published a specific guidance on how companies should evaluate and disclose climate risks in financial filings.
Investors and global stock exchanges are taking notice, especially in regard to how carbon-intensive companies are analyzing business impacts under scenarios where carbon pollution is reduced at levels that would limit global warming to 2-degrees Celsius or less – the goal of the Paris Climate Agreement.
More than ever, investors are aiming these questions at energy-intensive companies like ExxonMobil and Chevron, which are already struggling financially as global oil demand is waning.
3. Companies are advancing human rights reporting and performance
Companies are facing unprecedented scrutiny on their human rights performance and reporting. In 2015, the nonprofit group Shift that helps organizations to implement the U.N. Guiding Principles on Business and Human Rights (UNGPs), developed the UNGP reporting framework, which companies such as Ericsson, Nestle, and Unilever are already utilizing to strengthen human rights reporting and performance.
Ceres is now collaborating with Shift to advance corporate adoption and implementation of the framework to drive improved human rights performance across direct operations and global supply chains.
4. Water risks are rising on the investor agenda
Water crises such as prolonged droughts and extreme precipitation events – been in California, lately? – were again among the top five global impact risks in an annual report from the World Economic Forum.
Increasingly, companies operating in water-stressed regions are proactively taking action to conserve and protect water sources. General Mills, Gap and PepsiCo, are among a growing cadre of companies engaging with California policymakers on the urgency for stronger water management policies in this water-starved state.
5. Competence on sustainability is becoming a measure of board effectiveness
Corporate boards have a key authority and responsibility to boost corporate attention on long-term sustainability risks like climate change. Large investors are increasingly focused on the role board members can play on sustainability. U.S. pension funds CalPERSand CalSTRS, for example, both recently updated their governance principles to explicitly request that company boards have stronger experience and expertise on climate risk management.
In the coming months, investors and other stakeholders will be looking to engage with key governance experts within companies on this topic, including corporate secretaries and general counsel.
6. SDGs will be a bigger driver of strategy and action
In 2015, more than 190 world leaders committed to 17 Sustainable Development Goals (SDGs) aimed at ending extreme poverty, eliminating longstanding inequalities and fighting climate change.
Worldwide momentum behind these internationally supported goals continues to gain strength, and at the upcoming Ceres Conference we will hear from Novozymes, BASF and Intel about how they are aligning their commitments and business strategies with this global vision.
7. Sustainable sourcing is becoming the new norm
Access to reliable, affordable supplies of key inputs is threatened by climate change, water scarcity risks, and the use of unethical practices like deforestation and forced labor. Agricultural supply chains are feeling some of the biggest pressures, leading to stronger action by investors and companies themselves to push for strategies to assess and manage these risks.
This spring, Ceres will release an interactive tool called Engage the Chain to help investors and companies better understand wide-ranging agricultural commodity risks.
No doubt, company actions on all of these fronts will continue to evolve – and, hopefully, accelerate. Such leadership is more essential than ever.
It’s no secret that Amazon is revolutionizing the retail industry. But what does that actually mean?
Which retailer is Amazon targeting now? Amazon newest target isn’t a retail chain at all — it’s your local convenience store.The company rolled out a new service today called Amazon Instant Pickup, which lets customers order basics like chips, soda and toothpaste. You can then pick them up from an Amazon locker in just two minutes.
Isn’t mimicry the sincerest form of flattery? Not if you’re a retailer that wants to stay in business. Just ask Dick’s Sporting Goods ( ). Dick’s earnings report disappointed Wall Street on Tuesday. The retailer lowered its full-year profit forecast today because of “a challenging retail environment.” Its stock fell more than 20%. Sound familiar? Last month, Amazon filed a patent to launch a competing meal-kit delivery service. Blue Apron’s shares plunged 11% following the news. And grocery stocks got clobbered after Amazon announced plans to buy Whole Foods ( ) for $13.7 billion back in June.
Is Amazon a death sentence for traditional retailers? Not necessarily. Retailers like Home Depot ( ) are surviving by selling things you can’t buy on Amazon. Today, Home Depot reported record sales last quarter and bolstered its outlook for 2017. Home owners and professional builders alike still prefer to go to stores to test out home products, especially big ticket items like flooring and appliances.
A NEW commodity spawns a lucrative, fast-growing industry, prompting antitrust regulators to step in to restrain those who control its flow. A century ago, the resource in question was oil. Now similar concerns are being raised by the giants that deal in data, the oil of the digital era. These titans—Alphabet (Google’s parent company), Amazon, Apple, Facebook and Microsoft—look unstoppable. They are the five most valuable listed firms in the world. Their profits are surging: they collectively racked up over $25bn in net profit in the first quarter of 2017. Amazon captures half of all dollars spent online in America. Google and Facebook accounted for almost all the revenue growth in digital advertising in America last year.
Such dominance has prompted calls for the tech giants to be broken up, as Standard Oil was in the early 20th century. This newspaper has argued against such drastic action in the past. Size alone is not a crime. The giants’ success has benefited consumers. Few want to live without Google’s search engine, Amazon’s one-day delivery or Facebook’s newsfeed. Nor do these firms raise the alarm when standard antitrust tests are applied. Far from gouging consumers, many of their services are free (users pay, in effect, by handing over yet more data). Take account of offline rivals, and their market shares look less worrying. And the emergence of upstarts like Snapchat suggests that new entrants can still make waves.
But there is cause for concern. Internet companies’ control of data gives them enormous power. Old ways of thinking about competition, devised in the era of oil, look outdated in what has come to be called the “data economy”. A new approach is needed.
Quantity has a quality all its own
What has changed? Smartphones and the internet have made data abundant, ubiquitous and far more valuable. Whether you are going for a run, watching TV or even just sitting in traffic, virtually every activity creates a digital trace—more raw material for the data distilleries. As devices from watches to cars connect to the internet, the volume is increasing: some estimate that a self-driving car will generate 100 gigabytes per second. Meanwhile, artificial-intelligence (AI) techniques such as machine learning extract more value from data. Algorithms can predict when a customer is ready to buy, a jet-engine needs servicing or a person is at risk of a disease. Industrial giants such as GE and Siemens now sell themselves as data firms.
This abundance of data changes the nature of competition. Technology giants have always benefited from network effects: the more users Facebook signs up, the more attractive signing up becomes for others. With data there are extra network effects. By collecting more data, a firm has more scope to improve its products, which attracts more users, generating even more data, and so on. The more data Tesla gathers from its self-driving cars, the better it can make them at driving themselves—part of the reason the firm, which sold only 25,000 cars in the first quarter, is now worth more than GM, which sold 2.3m. Vast pools of data can thus act as protective moats.
Access to data also protects companies from rivals in another way. The case for being sanguine about competition in the tech industry rests on the potential for incumbents to be blindsided by a startup in a garage or an unexpected technological shift. But both are less likely in the data age. The giants’ surveillance systems span the entire economy: Google can see what people search for, Facebook what they share, Amazon what they buy. They own app stores and operating systems, and rent out computing power to startups. They have a “God’s eye view” of activities in their own markets and beyond. They can see when a new product or service gains traction, allowing them to copy it or simply buy the upstart before it becomes too great a threat. Many think Facebook’s $22bn purchase in 2014 of WhatsApp, a messaging app with fewer than 60 employees, falls into this category of “shoot-out acquisitions” that eliminate potential rivals. By providing barriers to entry and early-warning systems, data can stifle competition.
Who ya gonna call, trustbusters?
The nature of data makes the antitrust remedies of the past less useful. Breaking up a firm like Google into five Googlets would not stop network effects from reasserting themselves: in time, one of them would become dominant again. A radical rethink is required—and as the outlines of a new approach start to become apparent, two ideas stand out.
The first is that antitrust authorities need to move from the industrial era into the 21st century. When considering a merger, for example, they have traditionally used size to determine when to intervene. They now need to take into account the extent of firms’ data assets when assessing the impact of deals. The purchase price could also be a signal that an incumbent is buying a nascent threat. On these measures, Facebook’s willingness to pay so much for WhatsApp, which had no revenue to speak of, would have raised red flags. Trustbusters must also become more data-savvy in their analysis of market dynamics, for example by using simulations to hunt for algorithms colluding over prices or to determine how best to promote competition.
The second principle is to loosen the grip that providers of online services have over data and give more control to those who supply them. More transparency would help: companies could be forced to reveal to consumers what information they hold and how much money they make from it. Governments could encourage the emergence of new services by opening up more of their own data vaults or managing crucial parts of the data economy as public infrastructure, as India does with its digital-identity system, Aadhaar. They could also mandate the sharing of certain kinds of data, with users’ consent—an approach Europe is taking in financial services by requiring banks to make customers’ data accessible to third parties.
Rebooting antitrust for the information age will not be easy. It will entail new risks: more data sharing, for instance, could threaten privacy. But if governments don’t want a data economy dominated by a few giants, they will need to act soon.