Supply Chain Report 2017: State of Automation

Table of Contents
Introduction: State of the Industry
State of Analytics: Defining End to End Transparency 
State of Automation: Digitization is Not Digitalization (below)

These days, terms like “digital” and “digitization” are bandied about like popcorn hopping from a kettle on a hot summer day. As the words fill the air, they lack clarity. They are not actionable. One of the reasons is that there is no common definition across the industry.

At Supply Chain Insights, we define the “digital supply chain” as one that redefines the atoms and electrons of today’s processes. Here are some examples (also in Figure 4):
    •    Uberization: sharing platforms for collaborative economies.
    •    Redefining business to business activity through blockchain (multi-tier finance, lineage, track & trace).
    •    Additive manufacturing (3D printing of finished goods spare parts).
    •    Digital manufacturing (combining robotics, wearables and the Internet of Things) to redefine maintenance downtime.

Technologies Driving Process Advancement

Let’s start by recognizing that “digitization” is not “digitalization,” although both can improve automation.

What’s the difference? When we digitize a process, we translate a physical state into a digital signal. This can be used to improve automation but is not necessarily digital.

In contrast, digitalization takes this signal as a process input and translates it — and perhaps a myriad of other inputs — through process logic into a digital output. This takes many forms. The process of mapping signals is important, but it’s not the end state.

Let’s take an example. An EDI document transmits and digitizes data, but current EDI processes do not enable the digital supply chain. Why? The signals are not bi-directional or processed at the speed of business.

Another example is sensor data in manufacturing. Most plants have multiple programmable logic controllers and sensors. These technologies transmit outputs, but our current processes don’t use them to drive a digital manufacturing transformation.

In moving manufacturing to a digital strategy, companies use digital signals to redefine maintenance down times; sense shifts in speed to alert based on prescriptive analytics (or bots) for quality; and transmit the signals to robotics and wearables for hands-free operations.

These are just a few examples, by the way. The full list of how signals can be used in manufacturing is endless. The challenge is translating these signals into digital process outputs.

Unlearning What You’ve Learned
The movement to a digital supply chain makes much of what we already know obsolete. And that includes many of our traditional ways of thinking. This transition can be very uncomfortable for supply chain teams.

One example involves the building of outside-in processes, which of course makes standard inside-out thinking obsolete. Today’s processes are very focused on orders and shipments.

But does the digital supply chain require an order? If a drone uses pattern recognition to monitor real-time inventory, how does that change warehouse management? If cognitive engines replace and redefine supply chain planning, what is the role of collaborative forecasting? If machine learning can read master data, do we need to hard-code data? What is the role of ERP as we transition from transactional process flows relying on an ERP-based SCM platform to thinking systems using open source-based cognitive computing?

The opportunities to reinvent the possible and drive new value are endless. Here are a few.

1. Accelerate Value Networks. While companies talk value networks, the current focus of automation within organizations is enterprise-centric. As a result, companies are driving marginal improvement and treading the same ground over and over again.

The transition from EDI to blockchain enables banking disintermediation and the secure, real-time sharing of data between trading partners.

Use of these technologies requires the definition of multi-tier process capabilities. For example, how should companies share demand data? What are the possibilities for supply chain finance? In this transition, what is the role of banking? How do we collaborate in new ways and build trust? I firmly believe that the human element will be the toughest obstacle here.

2. Disintermediate 3PLs and Redefine BPO Relationships. Machine learning and rule automation will eliminate the need for a business process outsourcing relationship and redefine third-party logistics. New business models will evolve. Smart 3PLs will lead the transformation. We will have to learn to touch things — paper, Excel spreadsheets, and keyboards — less often.

3. Evolve the Learning Organization. The gulf between IT organizations and line-of-business leaders at many organizations today is wide. The role of IT has been more focused on “keeping the lights on” and, as a result, there is little funding for innovation. In parallel, line-of-business leaders are struggling to keep up with the pace of innovation.

The companies that will move fastest will recognize that this shift requires a redesign of organizational thinking to embrace technology. This includes partnering with best-of-breed technologists, building small cross-functional teams to test new tools, and undertaking structured/active learning focused on process innovation.

Companies need to fund process innovation. Today, most only fund product innovation. There has been an assumption that software brings “best practices,” so the focus has been on implementation. In building the digital supply chain, this is no longer the case (see Figure 5).

Evolution of the Digital Supply Chain
Evolution of the Digital Supply Chain

One large driver is the progression of analytics from descriptive to autonomous decision support. This evolution is enabling self-driving vehicles, sensing in business networks and sensing capabilities for rule matching; examples include allocation, available to promise, route tendering, inventory matching, order deduction matching. This drives an improvement in enterprise agility.

Today’s supply chains respond, but they do not sense. The ability to sense, learn and then drive an intelligent response is a fundamental underpinning of many of these shifts to the digital supply chain.

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