It’s easy to acknowledge the game-changing role digital technologies are playing in the modern economy. The challenge, to which most companies have yet to rise, is figuring out how to fully capture the different kinds of value that these technologies offer. Developing a strategy for digital transformation that fully leverages this value is also not easy. Without a comprehensive assessment of what digital technologies can offer, firms tend to assume that any application of modern digital technologies will lead to a digital transformation. Consequently, many of them make ad-hoc business decisions about the use of digital technologies and end up struggling even to maintain competitive parity, despite substantial investments.
To get an indication of the full range of value that digital technologies can offer, consider the following four examples, each of which highlights the strategic advantages available at a different tier of digital transformation.
Tier One: Operational efficiencies. Ford adopts new automated vision-based inspection of paint jobs in its plants through augmented and virtual reality, the Internet of Things (IoT), and AI. Using these technologies, the company improves blemish detection and reduces defects in its cars. In this case, data is generated by new technologies from factory assets, and AI uses this data to detect and prevent manufacturing defects in real time.
Tier Two: Advanced operational efficiencies. Caterpillar installs sensors on its construction equipment products to track how each of them is used at a construction site. It finds, for instance, that customers use their motor graders to level lighter gravel more often than to level heavier dirt. Utilizing this insight, the company introduces a cost-efficient motor grader primarily designed to level gravel rather than dirt.
Like Ford in the previous example, Caterpillar here benefits from operational efficiency gains by improving product-development productivity. The difference, however, is that the company’s sensor data comes from customers using their products, not from manufacturing plant assets. That customer dimension, of course, poses additional challenges. The efficiency gains in this tier also extend beyond asset utilization.
Tier Three: Data-driven services from value chains. GE tracks product-sensor data from their jet engines, analyzes it using AI, and offers real-time guidance for pilots to fly in ways that optimize fuel efficiencies. GE then appropriates a part of their customers’ cost savings through new annuities from “outcome-based” revenues. Their customers, in other words, pay GE a part of what they save from fuel efficiencies, in addition to what they pay for the product.
Here the initiative entails changing the prevailing business model from one that’s designed to produce and sell products to one that provides data-driven services to digital customers. GE’s R&D, product development, sales, and after-sales service units are all digitally connected to receive, analyze, generate, share, and react to sensor and IoT data from thousands of discrete products in real time. Because this drives new revenue streams, it does more than just enhance operational efficiency.
Tier Four: Data-driven services from digital platforms. Peloton uses product-sensor data from its exercise equipment to create a community of users and to match individual users with suitable trainers. Peloton’s products generate user-interaction data, which the company then uses to facilitate exchanges between its digital customers and various third-party entities outside the realm of its value chains. AI algorithms match specific users to suitable trainers analyzing product-user interaction data, very much like how Uber matches riders with drivers using data from their apps.
Like GE in the previous example, Peloton here is generating new revenues from its data-driven services — but by extending its products into digital platforms. This tier of digital transformation is the most challenging for industrial-era legacy firms, and for firms operating with value-chain-driven business models and little experience with digital platforms.
To think properly about these four tiers of transformation, the first step is to recognize that modern digital technologies have two notable value drivers: data in its new expansive role, and emergent digital ecosystems. Let’s explore them briefly in turn.
Data used to be episodic (generated by discrete events such as the shipment of a component from a supplier), but increasingly it is becoming interactive (generated continuously by sensors and the IoT to track information). This continuous tracking of assets and their operational parameters can boost productivity. If you use sensors to track and maintain temperature levels while super-heating molten steel, you can improve your quality and yield. If you embed sensors within certain products, you can revolutionize the user experience. Think of how smart mattresses track users’ heart rates, breathing patterns, and body movements, and then adapt their shape in real time to improve users’ sleep. Or how sensors embedded in cars can provide feedback that helps people drive more carefully.
More fundamentally, this interactivity reverses the roles of products and data. Data has traditionally supported products, but, increasingly, products are now supporting data. Products no longer just deliver functionality, help build a brand, or generate revenue; they now also serve as conduits for interactive data and wellsprings for new customer experiences.
To leverage interactive data’s new expansive role, firms also need networks of data generators and recipients. Such networks can emanate from sensor and IoT-enabled connectivity that amount to digital ecosystems.
Two main kinds of digital ecosystems have emerged, neither of which existed before modern advances in data and digital connectivity. One kind is the production ecosystem, which encompasses digital linkages within value chains. By linking sensor and IoT data from cars to spare-part suppliers, warehouses, and service dealers, for example, car companies can offer predictive maintenance services. The other kind is the consumption ecosystem, which involves networks outside of a firm’s value chain. Consider smart light bulbs on street lamps that are designed to sense gun shots: Their consumption ecosystems include a network of camera feeds, 911 operators, and ambulances, all of which together help to improve street safety.
Both production and consumption ecosystems, fueled by interactive data, drive new value. As the figure below shows, this holds across the four tiers of digital transformation discussed above. The first three tiers rely on production ecosystems, and the fourth on consumption ecosystems.
To determine your optimal digital-transformation strategy, assess your need to engage at each of the four tiers in the figure above and then focus on investments that will help you harness the benefits of interactive data and digital ecosystems.
Tier one is a must, as most firms can benefit from operational efficiencies. The vast majority of digital-transformation initiatives take place in this tier, which is especially important if operational efficiencies are a big part of a firm’s strategic thrust. Oil and gas businesses, for instance, run oil wells, pipelines, and refineries that require investments worth billions. If these firms decide to use IoT devices and AI to find reserves, and to maintain pipelines and refinery assets, they can save up to 60% of their operational costs. Key challenges in this tier include installing widespread interactive data generation in asset utilization and breaking silos around data sharing.
Tier two is imperative for companies selling products that have the potential to access interactive data from users, which can be leveraged for strategic advantage beyond what is available at tier one. Tier two becomes the final stop if available product-user interactive data is not amenable for revenue-generating services. Many consumer-packaged goods fall into this category. The primary use of interactive data in such businesses is to improve advertising or product-development efficiencies.
Tier Three is for companies who recognize that they can generate data-driven services from products and value chains. Such firms must enrich their production ecosystems to broaden their strategic advantage from operational efficiencies to new data-driven services.
In this tier, firms cross an important barrier: Instead of using data just for operational efficiencies, they use it for revenue generation. If your company doesn’t have access to a consumption ecosystem, tier three is the final stop for you. Sensor and AI-equipped dishwashers can anticipate component failures to offer predictive services, for example, but they’re hard to connect digitally to complementary objects and to extend into digital platforms. That said, many firms miss opportunities in this tier. They overlook their product’s consumption ecosystems or consider it too risky to extend their products into digital platforms. Many of the rivals of Peloton and Nordic Track have fallen into this trap.
Finally, Tier Four is strategically important for any firm whose products have emerging consumption ecosystems. Firms that stay within their production ecosystems in such scenarios risk being commoditized. Extending products into digital platforms is their key challenge.
Of course, not every firm will want or be able to engage in transformations on all of the four tiers discussed in this article. Some may opt to focus on just one or a few — but every firm must nonetheless remain aware of the expanding universe of new possibilities. Opportunities abound, and a thoughtful digital-transformation strategy, based on the framework presented here, will help companies remain relevant in the modern world.
This content was originally published here.