When cellphones were first released in the mid-1980s, a handset would set you back $ 4000 (€3378.98) — the equivalent of almost $ 10,000 (€8447.45) today. Here, Markus Brettschneider, group senior vice president and general manager for global food & beverage applications for ABB, explains how smaller food manufacturers can take advantage of low-cost digital technology.
When the first cellphone was released in 1983, this breakthrough technology was reserved for high-ranking business people and the social elite. Yet, decreasing technological costs have led to cellphones becoming arguably the most common technology available today. In fact, the UN’s 2014 telecommunications figures revealed that there are almost as many cellphone subscriptions as there are people on Earth.
This is indicative of a technological trend known as quality-adjusted price, which ties closely into the concept of Moore’s law. As technology rapidly develops at a pace that leads to significant performance increases year on year, the cost of that technology decreases at a similar pace.
This presents an important opportunity for smaller businesses to take advantage of newer technologies that were previously only accessible to large companies. In the food production industry, for example, there has been a significant increase in the adoption of digital technologies and software among larger businesses.Yet, food manufacturing companies of all sizes can tap into the productivity and efficiency benefits offered by digitalisation.
The digital food plant
While equipment and robotics have been the key drivers of plant improvement in past decades, the rise of the industrial internet of things (IIoT) has placed greater importance on software and insight. In particular, many plant managers now use digital solutions to monitor the status of equipment to mitigate performance problems.
For example, most food processing plants will have automated at least one part of the production line with a conveyor system. As with any piece of equipment, parts of this system will gradually wear down from repeated use over time. For critical components such as the motor, this leads to a slow decline in performance and risks downtime due to breakage.
Plant managers must therefore undertake predictive maintenance to address any issues before they become problems. To do this effectively, plant managers must have accurate performance data from the conveyor’s low-voltage motors.
Rather than invest in new systems that feature IIoT functionality, businesses can install multi-function sensors to collect and analyse performance data.
For example, the ABB Ability Smart Sensor for motors allows engineers to digitalise food production plants with minimal expenditure. These sensors fit directly onto the motor’s frame and monitor key performance factors such as temperature and vibration. This data is transmitted to the cloud, where it is analysed and reports are generated for plant engineers.
What the data provides is new insight into the health of motors used in production, enabling a shift from reactive maintenance to predictive maintenance. With a simple “stop light” system of green, yellow and red lights, fleet motor status is simple to assess.
Just as cellphones are no longer exclusive to the likes of CEOs of listed companies, digitalisation is not reserved for large food […]
What a year! 2017 brought us transformation and excitement in the Internet of Things (IoT) space.
It’s been a true transformation. We’ve seen almost every industry invest in IoT, and leading industries are quickly moving to implement IoT solutions that drive the bottom line. Consumer products, like wearables and connected electronics, are certainly a large part of the market. But IDC estimates more than 80 percent of IoT spend through 2020 will be on B2B applications and use cases.
That’s why IoT will be one of the primary drivers of the digital transformation in 2018 and beyond. Using IoT, successful companies will create a self-learning environment. In turn, those will drive digital disruption in the physical world. New business models will emerge, along with changes in work processes, productivity improvements, cost containment and enhanced customer experiences.
With all this in mind, I want to share what I believe will be the top five IoT trends in 2018.
Trend #1 Digital Twin
In the Industrial Internet of Things (IIoT), businesses will need to rethink their tools if operations, supply chains and value propositions are to remain competitive. The IBM Institute of Business Value report, “Thinking out of the toolbox,” highlights the realization by executives that digital data holds the promise to eliminate guessing and start understanding operations.
A significant finding: More than half (54 percent) of the respondents prioritized digital for ‘Product quality monitoring and predicting failures.’ And 52 percent said ‘Manufacturing plant optimization.’
What is a Digital Twin?
A key tool to improve operations with digital data is the Digital Twin.
Digital Twins are a huge next step in the world of IoT. In brief, the digital twin is a virtual doppelganger of the real-world thing. (Read more about Digital Twins.) In a software-everywhere world, Digital Twin technology will help organizations bridge the divide between the physical and digital.
The Digital Twin serves as a looking glass into what’s happening within physical assets. They also give insight into changes required for the future. Leveraging your IoT investments, and IBM Watson, the Digital Twin visualizes the hidden insights and dependencies of usability, traceability and quality. And all of these will eventually be part of your operations revolution. Eventually, with sensors everywhere, operations and interactions could be customized for every client.
Ultimately, the Digital Twin accelerates the product development timeline at reduced costs. As the digital counterpart of a physical product, the Digital Twin allows product developers to create, test, build, monitor, maintain and service products in a virtual environment. In short, the Digital Twin empowers organizations to shift to an operations-centric view. Proactive and predictive maintenance enables front line personnel to act before costly delays or failures occur and keep product development.
Trend #2 Blockchain
In 2018, Blockchain will play a major role by enhancing security, making transactions more seamless and creating efficiencies in the supply chain. (If you’re not familiar with the term, check out the blockchain cheat sheet.)
I expect the coming year will be one in which we see companies start to leverage blockchain in three key ways:
Build trust. Blockchain can help build trust between the people and parties that transact together. Watson IoT blockchain enables devices to participate in blockchain transactions as a trusted party. While Person A may not know device B and may not trust it implicitly, the indelible record of transactions and data from devices stored on the blockchain provide proof and command the necessary trust for businesses and people to cooperate.
Reduce costs. IoT and blockchain enable participants to reduce monetary and time commitment costs by ultimately removing the “middle man” from the process. Transactions and device data are now exhibited on a peer-to-peer basis, removing most legal or contractual costs.
Accelerate transactions. IoT and blockchain enable more transactions overall because it removes the middle man from the process. Organizations reduce the time needed for completing legal or contractual commitments through smart contracts.
Transforming your business with blockchain
Blockchain for IoT can transform the way business transactions are conducted globally by providing a trustworthy environment. These transactions are automated and encoded while enterprise-level privacy is preserved, offering security for all parties.
With IBM Watson IoT Blockchain, information from IoT devices is used in transactions. These blockchain-based solutions help organizations improve operational efficiency, transform customer experience, and adopt new business models. And it’s all done in a secure, private and decentralized manner. That means greater value for every participating organizations, a goal we should all strive for in 2018.
Trend #3 Security
As we rely on connected devices to make our lives better and easier in 2018, security is a must. All participants in the IoT ecosystem are responsible for the security of the devices, data and solutions. This means that device manufacturers, application developers, consumers, operators, integrators and enterprise businesses should all follow best practices.
IoT security requires a multi-layered approach. From a device point of view, it starts with design and development. Hardware, firmware/software and data must stay secure through the entire product lifecycle. It’s the same approach whether you’re a security analyst or operations person responsible for IoT solutions. IoT’s full potential will only be reached if security challenges are addressed. That requires a combination of interoperability, education and good design—and a proactive, not reactive approach to designing security features.
IBM’s approach to security
At IBM, we take security very seriously. We understand the intricacies of IoT. And we have the combined expertise from across our entire organization to explore the issues and provide best practices. Our thought leaders from IBM Research, Security and IoT joined forces on a comprehensive overview of IoT Security. Read our latest POV on cognitive security for the Internet of Things for the implications, best practices and standards of IoT security.
Many IoT implementations still require on-prem implementations. But in 2018, there will be more (and very clear) instances where Software as a Service (SaaS) is a viable option. Next year, I believe we’ll see more companies choose the SaaS approach to quickly create and prove out a variety of IoT scenarios at lower investment levels.
How to benefit with SaaS
Here are three major benefits that SaaS brings to an IoT deployment and why I predict that it’s a trend to watch:
Organizations will realize benefits more quickly. Maybe you’re just getting started. You’re collecting and sifting through telemetry data to discover new insights. Or maybe you’re ready to unleash machine learning on heaps of data to predict future machine failures. Either way, SaaS gives you the option to be up and running in hours, not months or years.
There’s a lower cost of entry. A typical IoT solution is comprised of several components spanning many technologies. There is device-side firmware, multiple connectivity technologies, server-side logic, vast amounts of data, and machine learning. Do you have the budget to develop and manage all that infrastructure from day one of your IoT project? An IoT SaaS implementation makes it easier to start slowly and grow a solution over time.
There’s also Increased flexibility. Don’t limit your evaluation of the SaaS solution to your initial IoT needs. Given the uncertainty of how your business will leverage IoT, now is a great time for some experimentation. In the new year, take advantage of SaaS capabilities to push your IoT project further or to try multiple scenarios.
Trend #5 Cognitive Computing
Last on my trend list, but certainly not least, is Cognitive Computing. The Internet of Things is at the threshold of a tremendous opportunity. For over a decade we’ve connected things with unique IP addresses. But the commoditization of sensors, processors and memory now make it possible to makes everyday things more than just connected … they can be intelligent.
Beyond traditional IoT implementations, cognitive computing increases the amount of data to improve the learning environment. That, then, increases the possibilities of what can be done with edge analytics – making sensors capable of diagnosing and adapting to their environment without the need for human intervention.
Another huge advantage of cognitive IoT: the ability to combine multiple data streams that can identify patterns. With that, they give much more context than would otherwise be available.
Unlocking IoT value
Cognitive IoT, AI and machine learning enable enterprises to unlock IoT value. An exploding amount of IoT data requires a new approach to gather, analyze and understand it all. And that massive amount of sensor and device information can be used to enhance what’s already known. Plus, it can also uncover new insights capable of transforming industries.
While making sense out of dark data and edge data paves our way to revolutionary ideas and technologies, it requires a cognitive approach. One that can effectively handle increasingly large inputs while generating meaningful output. Programmable systems thrive on prescribed scenarios using predictable data. But their rigidity can limit their usefulness when addressing the ambiguity and uncertainty of IoT data. Cognitive systems, however, are not explicitly programmed. They learn from interactions with people and from experiences with their environment. And in doing so, they become able to keep pace with the complexity of the Internet of things, identifying data correlations that would otherwise go unnoticed.
Looking forward to 2018
I expect these five things to play a major role in enterprise IoT in the coming year. And I fully expect other trends will emerge that aren’t even on the horizon yet. Because IoT is evolving so rapidly, there’s always something new!
As we look ahead to 2018, I hope you’re as excited about the IoT world as I am. And if you have thoughts on these or other trends that you believe will drive IoT transformations, let me know. I’d enjoy hearing from you.
Until now, the Internet-of-Things revolution has been, with notable outlier examples, largely theoretical and experimental. In 2018, we expect that many existing projects will show measurable returns, and more projects get launched to capitalize on data produced by billions of new connected things. With increased adoption there will be challenges: Our networks were not built […] IoT – Cisco Blog
When it comes to digital business, Andrew McAfee knows a thing or two. A principal research scientist at MIT, prolific writer, and management expert, McAfee is a leader in understanding and explaining how digital technologies are changing business, the economy, and society.
At the recent SAP Leonardo Live event in Chicago that focused on digital transformation, McAfee urged his audience to throw out the business playbook they’ve been using for the past 30 years.
“The right way to run a factory in the steam era became a really, really bad way to run it in the era of electrical power,” he said. “Similarly, during a technology transition — and afterwards — the advice you used to follow becomes bad advice.”
McAfee explained that fast, profound shifts are occurring in three key areas: process, company, and industry. And he provided a new playbook to help companies navigate those changes and succeed.
Process: From people to machines
The traditional wisdom about process, which McAfee defines as “getting stuff done,” is to let machines handle the routine work like accounting or record keeping, and have people use their accumulated wisdom to make the judgements calls. This is the playbook of yesterday.
“Profound shifts are occurring in three key areas: process, company, industry”
McAfee explains that in most companies, decisions have typically been based on the highest-paid person’s opinion, or “HiPPOs.” They follow their gut, past experiences, and education, but they are being threatened by what McAfee calls “the Geek” — people who use data to make decisions.
“When the Geek needs to make a tough call, they gather evidence, do the best analysis they can, then they follow the evidence — even if it doesn’t go along with their gut or their experience,” McAfee explains.
“But here is where things get interesting,” he says. “In 136 studies of decision making by HiPPOs versus Geeks, 48 percent of the time HiPPOs added nothing over Geeks’ approach. Furthermore, 46 percent of the time HiPPOs provided an inferior decision. HiPPOs were only clearly better in eight percent of the cases. We need to make HiPPOs an endangered species.”
McAfee believes that with artificial intelligence (AI) and machine learning, “Now we have a new toolkit to help us sift through these crazy amounts data, see patterns, and make very sophisticated, accurate judgements in extremely complicated situations.”
He explained that AI and machine learning technologies have leapfrogged much further ahead today than anyone could have anticipated, and are ready to take over making judgement calls.
“Go is 3,000-year-old Asian strategy game. Computers have been laughably bad at Go. Until last year, when the world’s best Go player became a computer,” said McAfee.
Analyzing the game played by AlphaGo, a Google AI company, experts focused on one particular move — move 37 — that made no sense to human players but ultimately helped the machine win. The lesson learned? AlphaGo doesn’t just play the game better than we do, it plays differently than we do.
McAfee is optimistic: “Together with machines, we’re going to make progress in some very difficult areas. And when we rewrite the business playbook, remember: machines are demonstrating excellent judgement.”
Company: From core to crowd
“For about 25 years we’ve been telling business that to succeed they need to strengthen their core — ‘core competency, core strength, core capabilities,’” said McAfee. “The idea of the core is a small number of things that differentiate you from competitors, realize value for customer, help you succeed in your markets.”
But, he explains, now there are millions of interconnected adults on the internet and if you can activate the energy of the crowd, amazing things can happen.
McAfee provided an example where a Harvard Business School expert on crowd sourcing and innovation Karim Lakhani worked with the National Institute of Health (NIH) and Harvard Medical School to try and improve the ability to sequence human white-blood cell genomes. They got good results.
But when Lakhani opened up an online competition to the crowd as an algorithmic challenge they got amazing results in both accuracy and speed. McAfee says the top results, “showed an improvement that was three orders of magnitude faster, without sacrificing accuracy,” compared to the NIH and Harvard Medical School results.
“We’re seeing companies that don’t focus on growing their core. They embrace the crowd from the start,” said McAfee. “We will see how this plays out. But when we rewrite the business playbook, we need to remind ourselves: the crowd is surprisingly wise.”
Industries: From industry to platform
“I grew up in McKinsey understanding the playbook rule: There is no substitute for knowing an industry inside and out. For the past 30 years, the business playbook has said industry structure determines successful business models,” said McAfee.
But in three very different industries McAfee argues that platform is making the difference when it comes to disruptive innovation.
Take the smart phone industry: The defining moment was when Apple opened up the App Store as a platform for outside developers. For urban transportation, it was Uber and now group fitness is being transformed with ClassPass, a platform that allows people to take classes at gyms by subscribing as members to ClassPass, not the gym.
McAfee explains: “ClassPass says, ‘Don’t join a gym. Sign up with us. You can pick whatever classes you want and get variety.’ To gyms they say, ‘you have some empty spaces. We can fill them. You won’t get the full price but some revenue is better than none.’”
Like with Apple and Uber, the platform for ClassPass brings together products, services, sellers, and consumers.
If platforms work, McAfee believes there are many advantages: You get the network effects of increased demand, companies can control the rules of engagement. With an open platform, you can crowd-source innovation and get additional information, which is used to create better pricing and matching of services.
This blows apart the distinct industry-sector differences people used to assume fueled growth and replaces it with the mandate to find the right platform for your business.
McAfee concludes, “I am pretty confident that the successful businesses of tomorrow are going to have a lot more machines, platforms, and crowds in them than today. I am really confident that following the industrial-age business playbook is a really good recipe for failure.”
Business model innovation has become an increasingly hot topic in management circles, and understandably so. No management activity is more important than having clarity about how the organization creates, delivers, and captures value. It requires, among other things, knowing what customers want, how value can be best delivered, and how to enlist strategic partners to achieve maximum benefit.
Although the ability to develop strong value propositions can enable companies to “get by,” in our view many of today’s most successful businesses are those that are able to place themselves in the “sweet spot” of business model scalability. Scalability is about achieving profitable growth and is therefore a fundamental consideration for managers and investors alike. If managers are incapable of factoring scalability attributes into their business model design, they risk being left behind, much the way bookstores owned by Borders Group Inc. were eclipsed by Amazon.com Inc.
Over a five-year period, we studied scalability in the context of more than 90 Scandinavian businesses and also examined the experiences of a number of well-known businesses, including Google, Apple, and Groupon. (See “About the Research.”) In the course of our research, we identified five patterns by which companies can achieve scalability. The first pattern involved adding new distribution channels. The second entailed freeing the business from traditional capacity constraints. The third involved outsourcing capital investments to partners who, in effect, became participants in the business model. The fourth was to have customers and other partners assume multiple roles in the business model. And the fifth pattern was to establish platform models in which even competitors may become customers. Based on these patterns, we have developed a framework for identifying potential levers for business model scalability, along with a road map that managers can use to improve their business models.
Over and above the need to create value propositions that are difficult for competitors to replicate, managers need to develop business models that are capable of achieving positive and accelerating returns on the investments made. When companies restructure or invest in acquisitions, it’s common for them to identify synergies that reduce costs and simplify workflows and product offerings. However, simply thinking in terms of synergies isn’t enough; such synergies don’t necessarily lead to improvements in business model scalability. To achieve scalability, managers and entrepreneurs need to remove capacity constraints. They have opportunities to do this in a variety of ways: by collaborating with partners, by encouraging partners to play multiple roles in the business model, by creating platforms to attract new partners, or even by working with current competitors.
Accelerating Returns to Scale
What do we mean by “scalable”? We use the term scalability to identify where changes in size or volume are possible and seem worthwhile. Scalability refers to a system’s ability to expand output on demand when resources are added. Linking scalability to business models provides us with a framework for discussing and estimating business potential, which is important to both executives and many stakeholders because, among other things, it has implications for hiring and skill development. Another important characteristic of scalability is that the organization has sufficient flexibility to grow while incorporating the effects of external pressures, such as new competitors, altered regulation, or macroeconomic pressure.
The first dimension of scalability is the degree to which increased input can create higher output. The second dimension of scalability relates to the ability of the business model to accelerate the returns on the additional investment. Accelerating returns to scale are typically found in business models where new resources, capabilities, or value propositions provide completely new properties to an existing industry.1 Amazon.com’s retailing business model offers a good example. For example, the company’s algorithms introduce customers to products they may not have considered but might be of interest to them as they shop online.
In those situations where returns to scale are declining rather than increasing, managers should figure out how quickly to exit the business. If the returns are falling precipitously, it might make sense to pull out quickly. Even when returns are flat, further investments may be unattractive. As a general rule, executives should invest capital where they can generate increasing returns to scale.
Scalability Patterns in Business Models
A scalable business model is one that is flexible and where the addition of new resources brings increasing returns. In the course of our research, we searched for business model attributes that were sufficiently flexible to cope with internal demands and external forces and where the potential wasn’t constrained by physical or material assets (such as labor shortages, machine capacity, cash liquidity, or storage capacity). Below we will examine the five patterns of business model scalability individually.
Pattern A: Add new distribution channels. While the notion of selling through multiple distribution channels isn’t novel, it’s useful to understand what happens when an additional channel is added. As long as the implementation of a new distribution channel does not cannibalize sales in existing channels, adding a new sales channel can allow a company to spread the costs of overhead and reap benefits from increased sales.
We found this to be the case at Copenhagen Seafood A/S, a Danish supplier of fresh fish. The company, which had traditionally sold only to high-end restaurants, added the sale of fresh fish directly to retail customers, enabling it to offer restaurant-quality seafood to individuals at reasonable prices. Because restaurants typically ask for specific cuts of fish, the percentage of waste can be high. By adding the retail channel, Copenhagen Seafood was able to cultivate a new clientele with people who relished the opportunity to buy from a seafood supplier closely associated with some of the city’s best-known restaurants.2
Pattern B: Explore ways to work around traditional capacity constraints. Scalability often means finding ways to overcome traditional capacity constraints. Obviously, constraints vary from industry to industry. In the pharmaceutical industry, the constraints might involve the cost of establishing research infrastructure and the ability to develop new products and receive approval for new products. However, when viewing constraints from the perspective of business model innovation, companies should ask themselves if they can find ways to work around existing constraints. In the private banking sector, for example, a company might bypass capacity constraints by focusing on customer relationship activities and outsourcing infrastructure management to others. In a similar vein, a consulting company with a business model focused on hourly billing for large government organizations explored bypassing that constraint by marketing standard outputs and simpler reports to a new customer segment consisting of smaller businesses.
Pattern C: Shift capital requirements to partners. Every organization needs to prioritize its investments and determine which are most critical. CFOs are encouraged to optimize the cash liquidity constraints, cash flow, and working capital attributes of their business models. Given that many companies place a high value on cash, business models that shift capital requirements to strategic partners can be desirable.3
One company we studied was Sky-Watch A/S, a company based in Støvring, Denmark, that develops and manufactures drones suited for a variety of industrial settings. Sky-Watch’s business model has fewer resource constraints than some of its close competitors thanks to management’s decision to concentrate on turning the core platform into an open platform that allows customers and strategic partners to add their own hardware and software.
Pattern D: Leverage the work of partners. Companies need to pay attention to what their customers and strategic partners value. Managers should use this knowledge to optimize the value proposition of the products and services they offer to customers. The key is to find smart ways to leverage the resources of partners. For example, Tupperware Brands Corp., based in Orlando, Florida, is famous for leveraging a community of sales representatives who have an interest in selling the company’s food-storage products to a widening circle of people. Groupon Inc. likewise turns customers into partners by giving them incentives to spread the word about the company. Similar strategies can be leveraged for distribution methods, building customer loyalty, giving access to resources, and performing other activities according to the value configuration of the business model.
Pattern E: Implement platform models. A variation on leveraging partners involves using platform-based business models. Platform models are based on collaboration and can take different forms. For example, PrintConnect.com of Würselen, Germany, operates a web-based workflow platform for printing and packaging that links partners across the value chain. Some platform business models predate the web: Visa Inc., which connects businesses with credit card users, is an example.
When looking at business model innovation from a platform perspective, an important question is, “How do we turn competitors into partners or perhaps even customers?” For example, The Relationship Factory,4 a company based in Aarhus, Denmark, that organizes professional networking groups for managers, opted for a platform model to achieve business model scalability. It makes its software platform available to competitors on a private-label basis, thereby providing the company with a supplemental and recurring revenue stream on top of its traditional service-based activities. While competitors continue to rely heavily on their sale of service hours, the company is able to generate incremental revenue by selling “ease of use” to its competitors as well as benchmarking data across the industry.
A Road Map to Business Model Scalability
The patterns we have discussed above describe how companies can adjust their business models to make them scalable. While traditional thinking typically leads to synergy effects and, at best, positive returns that are linear to the investments, some of the companies we studied showed that it was possible to redesign business models to achieve accelerating returns. However, achieving accelerating returns is not easy. It requires thinking strategically in terms of the value propositions of stakeholders, strategic partners, and customers involved in the immediate business ecosystem. Aligning and leveraging the competencies and motivations of these stakeholders can lead to better cooperation. It can also build greater trust and loyalty among partners, which will pay off in the long term.
To implement the patterns for scalability, it is often necessary to identify activities and resources where collaborating with partners is advantageous and can strengthen the offering’s value proposition to customers. These patterns can assist managers in rethinking how their business models make use of partners, customers, and other stakeholders. Rather than just relying on traditional analytical exercises such as analyzing cost structures, product-segment profitability, and market-segment growth, managers can work on achieving business model scalability by asking a different set of questions. The questions will often lead to the identification of new partners and potentially new roles.
We suggest that companies pursue three steps:
1. Identify potential strategic partners. Scalability typically involves connecting strategic partners to the value proposition, either through sharing activities or resources. Given that scalability requires thinking beyond simply sharing costs, executives should ask themselves the following:
Are there potential strategic partners that could perform activities in our business model — or provide resources to it — in ways that would help improve the value proposition to our customers?
2. Ask questions that reveal a road map to scalability. Asking questions can trigger ideas about how to reconfigure a business model. When encountering novel ways of doing business, managers should analyze how such a business model would play out for their own company. We have found that the following questions can be helpful:
How does this novel business model challenge our existing way of thinking about the business?
What would we need to do differently to implement this business model?
Which other companies excel at what we are trying to do, and what can we learn from them?
What are the key value drivers of this particular business model?
Could this business model lead to scalability?
Based on the ideas you are able to generate, we recommend using the following questions to help clarify potential avenues for scalability:
Are there potential strategic partners that can offer features (at minimal or no cost to our company) that enrich the existing value proposition to our customers, while receiving value themselves?
Are there alternative configurations that free the business model from existing capacity constraints?
Would it make sense to establish a platform for other businesses to buy into — and thus create alternative ways of generating revenue?
Is it possible to change the role of existing stakeholders and utilize them in multiple roles in the business model?
Who would pay for either access to our customer base or knowledge about our customers and their characteristics?
Which mechanisms are in place to create customer lock-in?
How agile is our company in reacting to threats from new entrants or new technologies?
3. Analyze the scalability attributes of business model options. When all of the ideas generated have been presented, executives should facilitate a discussion to start to evaluate potential business models. They should analyze the attributes of the various options and consider how they might be configured to achieve accelerating returns on investments.
Traditionally, some companies have developed business models that focus on achieving economies of scale while other companies have been more geared toward creating economies of scope through differentiation. We have found that scalability goes beyond this traditional distinction and that identifying the sweet spot of business model scalability involves identifying accelerating returns on input.
In cases of declining returns to scale, managers should focus on downsizing the business so as not to cannibalize existing value. In cases where the returns on additional inputs are constant, managers should attempt to find ways to increase returns or invest excess capital elsewhere. When the business is able to generate positive, albeit linear, returns on additional inputs, the existence of synergies can make this a favorable place to be, although the company may be stuck with a business model that is at best average. In this case, managers should attempt to improve their business model using one of the five patterns described above.
Having a road map for business model scalability can be enormously helpful for managers, whether they are involved in developing new business models from scratch or innovating, rejuvenating, or redesigning existing business models. Although much of the recent research about business model innovation examines the alignment between value propositions and customer needs,5 business model scalability depends on close alignment between the value proposition and strategic partners.
The patterns we have identified as gateways to scalable business models (for example, enriching value propositions, removing capacity constraints, and changing the role of stakeholders in business models) provide avenues for managers to explore. Identifying business model configurations that allow for such characteristics should be a top priority for managers as they develop and review their corporate strategies.