Supply chain experts were once relegated to back-office functions such as procurement, production, and shipping. Today, they are considered to be among the most valuable employees in almost any company.
Take, for example, the likes of Tim Cook (CEO of Apple), Mary Barra (CEO of General Motors), and Gerry Smith (CEO of Office Depot). All came up as supply chain people. All now lead organizations that operate on a global scale.
But if you think these CEOs are focused on the back office, think again. Like every global organization in today’s digital economy, they’re all focused on the customer experience.
D2O and the intelligent enterprise
Being a supply chain person means being deeply involved in the very core of what your company does. And if improving the experience that customers have of your company is part of your business goals – and it should be – the supply chain is the place to start.
Why? Because supply chain spans almost everything you do as a company – from product design and business planning to manufacturing, delivery, and even operations. At SAP, we call this the design to operate (D2O) lifecycle.
Companies that take the pains to introduce collaboration, visibility, and agility across all phases of the D2O lifecycle are those that can deliver better experiences by detecting shifts in consumer demand and responding effectively to what customers want. This kind of organization is an intelligent enterprise.
From chain to network
The intelligent enterprise uses data to drive business decisions that improve the customer experience. What stands in the way are silos.
In the past, when supply chain experts pursued improvement projects, they tended to focus on their own silo. They might streamline R&D – or maybe optimize production processes. Certainly, there has been integration with adjacent silos to facilitate handoffs, but not much beyond this.
When you’re working according to a supply “chain” metaphor, each silo is responsible only for its links. A better metaphor is the supply network – where information flows as needed across D2O silos, helping you to model, manage, and master complexity.
The example of a digital twin shows the advantages of the supply network nicely. For years, R&D teams have designed products and assets using computer-aided design (CAD). These designs can be turned into digital twins of assets sold – digital twins that are updated by IoT sensors that work (during the operations phases) to send back data regarding demand for new products (where planning teams take notice).
The updated digital twin is, essentially, a collection of asset-specific data. By combining data from all assets of a category (all pumps or coffee machines sold, let’s say), you can create a network of digital twins. You can then run machine learning algorithms on the data in this network to identify patterns and predict outcomes. This, of course, helps you move from preventive maintenance (replace parts on a schedule) to predictive maintenance (replace parts when they are predicted to fail).
How does this enhance the customer experience? As a result of predictive maintenance, you can now optimize the lifetime value of parts for your customers – while maximizing uptime. This is just one example of how integration across D2O phases in the context of a vibrant supply network can help your company do new things in new ways that delight your customers.
New skillsets, new mindset
Doing new things in new ways requires a new skillset. The endless duties of old-school supply chain management are giving way to automation based on near-universal access to data and a single data platform that serves as a single source of the truth.
The supply chain of the future (already here for leading companies today) is one in which day-to-day processes run on their own and workers are alerted only when problems occur. Management by exception is nothing new, but today’s technology makes it better than ever before.
The result is more time for supply network experts to “dig for diamonds” in the data to help optimize processes, gain an advantage, or learn new things about customers. Thus, data scientists are in huge demand.
More than ever, advantages are gained by the application of data technologies such as machine learning – which can do much more than predictive maintenance. Machine learning can also be used to identify the probability of bottlenecks due to weather or other events. It can cut inventory carrying costs. It can ensure supplier quality. It can also improve demand forecasts.
The “new” doesn’t stop there. Experience with other advanced technologies is needed, too. Blockchain technology can prevent fraud in the supply chain. Augmented reality can reduce the need for training by assisting maintenance crews with guidance on demand. Robotic units in the warehouse can optimize routes and deliver what’s needed – leaving the packing and picking to the humans.
Across all of these areas, the skillset – and mindset – needed will focus on the ability to design and manage flows of supply network information. Experts who can analyze information, work with data, use machine learning algorithms, and drive supply network performance in the direction of serving customers better – these will be the supply network experts most in demand in the experience economy.
Download the IDC report “Leveraging Your Intelligent Digital Supply Chain” to find out how an end-to-end digital supply chain – from design and planning to manufacturing, logistics, and operations – addresses the operational pressures of shorter product cycles, design variability, fluctuating demand, and faster delivery expectations.