Multifaceted Businesses: Part Two

New Technologies + New Business Models = New Opportunities

Abstract

Technology advancements and business model evolution are reshaping industries and competitive landscapes, generating new threats and opportunities for businesses to diversify and differentiate.

Article

This article is an excerpt fromThe Rise of Multifaceted Businesses, available forfree download here.

New Technologies + New Business Models = New Opportunities

In Part One of this series, we looked at the evolution of business models and what is driving it. Here we examine how this phenomena is impacted by the emergence and growing use of IoT, Big Data, AI/ ML, Network Orchestration, and subscription services.

IoT/Big Data

When a product, machine, or system is outfitted with sensors, connectivity, and intelligence, it enables remote monitoring and predictive maintenance which can significantly lower the cost of service, while simultaneously dramatically improving uptime. This is foundational for profitably providing service level agreements (SLAs), longer warranties, full-service leases profitably, and ultimately product-as-a-service. Perhaps even more powerful, the insights and data generated by IoT can provide the intelligence to offer many new types of value-add services. These can drive high-value improvements for the customers of manufacturers and distributors. For example, their customers who buy equipment that leverages IoT and Big Data, could also realize lower energy consumption (decreased cost and carbon footprint) and improved safety/fewer accidents using IoT-enabled services from the manufacturer or distributor. Other IoT-enabled services might provide the equipment user with insights into their own work patterns and how they are using the equipment, the ability to improve their own forecasts and lower inventory levels, and ultimately improve their customer’s satisfaction by improving the reliability of their operations.

Fleet Advantage — Using IoT with Trucks to Offer Higher Value Services and Outcomes

Source: ID 18029139© Natalia Bratslavsky| Dreamstime.com

Fleet Advantage leases trucks for vehicle fleets. They combine the data generated by the trucks’ onboard computers with maintenance and route data from their customers to track fuel econo­my, hours of service, driver behaviors, and much more. They can detect when an engine wasn’t tuned up properly or a driver is staying too long in the lower gears, thereby using more fuel than necessary. They can also detect unsafe driving habits so that specific drivers can receive training, thus reducing accidents. Consequently, beyond just providing the vehicles, they offer value-add services that reduce fuel consumption (the largest operating cost for fleets), lower mainte­nance costs, mitigate truck breakdowns, and decrease the number of accidents. Fleet Advantage’s value-add services have boosted their revenue and enabled them to realize higher margins than their competition. For more on this example, see Using IoT to Transform a Service Business.

Artificial Intelligence/Machine Learning

Over the past decade, there have been tremendous advances in artificial intelligence and machine learning (AI/ML). These are now being used in the majority of enterprise applications. This growth is being driven by three main factors: 1) the enormous growth of on-demand computing power, 2) new developments in AI/ML algorithms, and 3) massive new quantities of detailed data. The internet creates a rapidly growing aggregation of extremely diverse data. Digitization of company’s processes provides company-specific data. IoT is creating massive amounts of fine-grained data about the physical world.

Predictive Maintenance

Most uses of AI/ML by manufacturers and distributors are initially focused on internal use cases, such as to automate and improve their operations, improve forecasts, better understand their competition, and so forth. Beyond the internal opportunities, AI/ML provide opportunities for manufacturers and distributors to dramatically expand the palette of services they offer. For example, manufacturers of mission critical equipment are using machine learning to sense and predict equipment failures well before they actually happen, enabling the problem to be fixed at leisure during planned downtime. The customer/equipment user can plan ahead and build extra inventory or otherwise frontload activities needing that equipment before the planned shutdown. In contrast, the unplanned failure of the equipment requires the manufacturer or distribu­tor (whoever is providing the support) to fly in emergency technicians and parts at great expense. Meanwhile, their customer’s operation is losing money and productivity, and their customer is losing patience, every hour that equipment remains down. With AI/ML-driven predictive maintenance capabilities, the equipment manufacturer can guarantee extremely high uptimes, which is valuable for any mission-critical equipment user. Customers are willing to pay a premium for that kind of reliability and predictability of their operations.

Intelligence-based Services/Optimization

Distributors and manufacturers can also use AI/ML to understand their customer’s usage patterns, to help the customer optimize their business. A truck manufacturer might use AI/ML to help the fleet owner improve fuel economy and safety, optimize maintenance, and optimize their load planning. A distributor could also use AI/ML to provide market intelligence services to their customers. Since the distributor has a much broader view of the market than their customers, they can use AI/ML — combining the data they have internally about product purchases, with external data such as major events, fluctuations in underlying commodity markets and indexes, and other casual data — to sense and anticipate market shifts, shortages, impending price shifts, etc. and alert their customers about risk and opportunities, as well as recommended actions. The possibilities for use of AI/ML to create new value and services is virtually endless.

Ecube Labs—Using Machine Learning to Reduce Trash Collection Costs

Source: Image by Ecube

Ecube makes trash cans for use in public spaces. These are solar powered, wirelessly-connect­ed smart trash cans with a built-in trash compactor and fill-level sensors. On top of all that, they’ve implemented machine learning to understand usage patterns, to more accurately predict when the trash should be picked up and optimize pickup routes. The end result is a significant (up to 80%) reduction in the number of trips and miles driven by trash collection trucks, cleaner public spaces without over­flowing trash bins, and elimination of insect and vermin problems. Ecube’s customers are buying a lot more than just a trash can (the ‘thing’). They are buying valuable desired out­comes—reduced cost and carbon footprint of their trash collection operations, less traffic on their city streets (fewer trucks), and more attractive public spaces for citizens.

Network Orchestrator Business Model

Network Orchestration is a relatively new business model, based on leveraging the internet and other technology to connect a network of sellers (or resource providers) to buyers (or resource consumers). Well known examples include Uber, Airbnb, Amazon, Facebook, and Alibaba. Many orchestrators provide a form of brokerage, with highly automated buyer-seller matching enabled by technology. The orchestrator is typically ‘asset light’; i.e. they don’t have to buy the cars needed to provide a ride service, or buy the houses needed to provide a bed and breakfast service, or buy the consumer products needed to provide an enormous online marketplace with ‘endless virtual aisles’ of merchandise. According to a research study1 published by the Wharton School, Network Orchestrators had 50% higher ROA, 2½ times the profit margin, four times the stock valuation (price to revenue ratio), and grew over 10X as fast in their first year when compared with asset-intensive companies such as manufacturers and distributors.

 

Asset Builders

Service Providers

Technology Creators

Network Orchestrators

Description

Make, market, distribute, and sell physical goods

Hire employees who provide services to customers

Develop and sell intellectual property such as software, analytics, and technology

Create a network in which participants interact and share in the value creation

Example Companies

Walmart, Ford, FedEx

Aetna, JP Morgan, Accenture

Microsoft, Oracle, Amgen

TripAdvisor, Red Hat, Uber

2015 Multiplier

1.5

2.5

4.7

5.8

2015 Profit Margin

27.9%

47.1%

61.6%

69.5%

2015 Return on Assets

1.6%

2.0%

-0.9%

2.4%

2015 1 Year Sales Growth

1.3%

9.0%

13.2%

17.0%

Source: Wharton School, Networks and Platform Based Business Models Win in the Digital Age
Table 1 – Comparison of Network Orchestrators with Asset Builders and Other Traditional Companies

These are extraordinary differences in financial performance between asset-building companies and network orchestrators. However, it requires significant changes to culture, systems, processes, and human capital for an asset-building company to develop a network orchestration capability. Nevertheless, in some cases the stakes (e.g. survival of a company or industry) and potential rewards (e.g. dominance of an industry) are so high, that some manufacturers and distributors are jumping into the fray. For example, some predict that the adoption of self-driving cars by ride-hailing companies (e.g. Uber and Lyft) will lead to a sharp decline in car ownership. That is causing automobile manufacturers to reconsider their current business model and look at a different model where a major portion of their revenue comes from ride-hailing and delivery services. They will still be manufacturing cars but in addition are investing in developing new network orchestration business models.

Automobile Manufacturers Launching Carsharing, Ride-hailing, and Delivery Networks

The automotive manufacturing industry is going through major disruptions to business models. They have traditionally built and sold cars (the ‘thing’), but the outcome people want is to get from point A to point B.2 Automakers have been expanding their palette of services, moving into Mobility-as-a-Service (MaaS). GM has had a car-sharing service (Maven) for over two years, created its own ride-hailing platform (the service is still to be launched), and is planning to launch a robo-taxi service in San Francisco. Ford built a fleet management center to provide ride-hailing and delivery services in Miami. Daimler and BMW recently launched a billion-dollar joint venture, combining Daimler’s Car2Go carsharing with BMW’s route-management and booking services, taxi ride-hailing service, car-sharing, and parking services.

The Subscription Economy

Another trend is the rise of subscription-based businesses, where customers pay a fixed amount on a periodic (often monthly) basis to receive a regular flow of goods or services. These can be broadly grouped3 into three types of subscription services: 1) Replenishment — Regular resupply of same or similar items, 2) Curation — Receive a new and different set of items each period, to facilitate discovery, 3) Access — providing exclusive subscriber-only products, deals, and perks. Examples of subscription services span many different industries and include offerings such as Amazon Subscribe & Save, Dollar Shave Club, Ipsy, and Blue Apron. In addition to retailers embracing the subscription model, some manufacturers are starting to offer subscription-based services such as P&G’s Gillette On Demand, Hotel Chocolat’s4 Tasting Club, Sisley Paris’ Beauty Subscription, and Fender Guitar’s Play (online guitar lesson service). Subscription-based business models can provide higher repeat business, more predictable revenue, and more loyal customers. With this predictability, forecasts become more accurate and inventory levels can be better managed. Fulfillment can be planned further ahead, thereby lowering fulfillment/shipping costs, especially compared to the same-day “I must have it right now” buying patterns that are becoming commonplace.

Caterpillar—Subscription Service based on IoT and Machine Learning

Caterpillar (a major manufacturer of mining, construction, and agricultural equipment) offers subscription services that take in data from sensors on its customers’ equipment and use machine learning to provide intelligence across the customer’s fleet of equipment. It is part of their Cat Connect Services, which includes several services:

  • Equipment Management—including multi-site remote equipment monitoring (improving utilization and maintenance), benchmarking (of fuel consumption, run/idle time, uptime), inspections (conducted by CAT), fluid analysis, condition monitoring, and customized maintenance and repair plans and execution.
  • Productivity Services—Utilization reporting, geospatial mapping (3D map/topography models, created from drone imagery flying over the construction or mining site), benchmarking (equipment health, operations, resource allocations, and site productivity), operator and site supervisor training, and productivity monitoring and optimization (site design, resource allocation, crew scheduling, maintenance practices fleet configuration).
  • Safety Services—Identifying gaps between leader and worker perceptions, monitoring operators’ sleep patterns, and align work schedules to minimize fatigue.

CAT’s subscription services are one part of their rich set of offerings, comprising their multifaceted business model.

IoT, AI/ML, Networks, and subscription models are often blended together in various combinations, as illustrated by the examples above. These enable new high value services to be offered by manufacturers and wholesalers, with resulting increases in differentiation, profitability, and customer loyalty. However, many new capabilities are required for an asset-based company to bring these new services to market.

In Part Three, the final installment of this series, we look at what new capabilities are required to successfully evolve to new business models.

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1 Lind, Beck, Wind (2016), The Network Imperative, Harvard Business Review Press. Summary article retrieved March 6, 2019. — Return to article text above

2 There is also an emotional and experiential side to cars for many people which may entice them to keep buying vehicles. However, some studies indicate that younger people have a more utilitarian attitude to transportation, without the emotional attachment to owning a vehicle that their parents may have had. Car companies want to be positioned to meet the needs of this next generation, with mobility services fulfilling the way they want to travel. — Return to article text above

3 Chen, Fenyo, Yang, Zhang, Thinking inside the subscription box: New research on e-commerce consumers, McKinsey & Company. — Return to article text above
4 Hotel Chocolat is a bean-to-bar chocolate manufacturer. This means they process their own cocoa beans in-house. — Return to article text above


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