The Winding Road toward the “Autonomous” Supply Chain (Part 1)
September 16, 2016 Leave a comment
There is a lot of buzz about the “autonomous” supply chain these days. The topic came up recently at a conference I recently attended where a topic of discussion was the supply chain of 2030. But, before we turn out the lights and lock the door to a fully automated, self-aware, supply chain decision machine, let’s take a moment and put this idea into some perspective. I’ve heard the driverless vehicle used as an analogy for the autonomous supply chain. However, orchestrating the value network where goods, information and currency pulse between facilities and organizations, following the path of least resistance may prove to be considerably more complex than driving a vehicle. Most sixteen-year-olds can successfully drive a car, but you may not want to entrust your global value network to them.
Before you can have an autonomous supply chain, you need to accelerate what I call the Detect, Diagnose, Decide cycle. In fact, as you accelerate the cycle you may learn just how much autonomy may be possible and/or wise.
Detect, Diagnose, Decide
The work of managing the value network has always been to detect challenges and opportunities, diagnose the causes, and decide what to do next –
- Detect (and/or anticipate) market requirements and the challenges in meeting them
- Diagnose the causes of the challenges, both incidental and systematic
- Decide the next best action within the constraints of time and capital in relevant time
The Detect, Diagnose, Decide cycle used to take a month. Computing power, better software, and availability of data shortened it to a week. Routine, narrowly defined, short-term changes are now addressed even more quickly under a steady state – and a lot of controlled automation is not only possible in this case, but obligatory. However, no business remains in a steady state, and changes from that state require critical decisions which add or destroy significant value.
Data Is the Double-edged Sword
The universe of data is exploding exponentially from networks of organizations, people and things. Yet, many companies are choking on their own ERP data, as they struggle to make decisions on incomplete, incorrect and disparate data. So, while the need for the Detect, Diagnose, Decide cycle to keep pace grows more ever more imperative, some organizations struggle to do anything but watch. The winners will be those who can capitalize on the opportunities that the data explosion affords by making better decisions through advanced analytics (see Figure 1). The time required just to collect, clean, and synchronize data for analysis remains the fundamental barrier to a better detection, diagnosis and decisions in the value network.
A consolidated data store which can connect to source systems and on which data can be programmatically “wrangled” into a supra data set would be helpful in the extreme. While this may seem like an almost insurmountable challenge, this capability exists today. For example, the Opalytics Cloud Platform enables you to use Python to automatically validate, reconcile and synchronize data from various sources, forming the foundation of a better Detect, Diagnose, Decide cycle.
Thanks for taking a moment to stop by. As we enter this weekend, remember that life is short, so we should live it well.
I’ll be back next week with Part 2.