Low-touch, or touchless planning, forecasting and other types of supply chain knowledge work automation have been around for a while now. A trend that started with Robotic Process Automation (RPA) in the back-office, automating dull and repetitive tasks like invoice matching, evolved into cognitive automation and autonomous execution of more advanced supply chain processes using intelligent agents.

I envisioned autonomous IBP in 2016 and published a Foresight article discussion what’s needed for autonomous planning in 2019. Deployment and adoption of these solutions is happening in the market, albeit not at lightning speed yet. 

Since writing those pieces, I came to see that automation doesn’t remain a differentiator for long. Sure, it will bring productivity improvements, but with an ever-diminishing return. Just think about what’s left to automate in an autonomous warehouse. The same will happen to knowledge work. There is only so much planning and decision making you can automate. There are relative preferences for automation, augmentation and human-centricity in decision making.  

On the other hand, learning has no boundaries. I find it much more important and interesting to develop operating models where human and machine can continuously learn and improve together.

Kasparov called this ‘a new form of collaboration’. His law envisions that a superior human-machine interface will beat superior human knowledge and superior technology managed with inferior human-machine collaboration. His vision has been proven in an experiment with radiologists.

This new type of collaboration and learning, supported by new human-machine processes, will open up a whole new world for us. We had our first peek into it with LLM’s through ChatGPT. Advocates of reducing the planning workforce with up to 80% are constraint in thinking about automation and productivity. They have not thought about a new era of learning and machine-collaboration for the knowledge worker, and the jobs this will create. Creative destruction increases jobs and decreases unemployment rates.

We will develop new ways of working and create new roles, that will replace existing roles and make knowledge work more interesting. The planner of the future works closely with the machine to continuously learn, in ways we can’t imagine yet. Not just functional or business learnings, but also self-learning. Improved awareness about one’s judgments and actions and after some reflection on feedback from the machine, one’s thinking.

When we include psychology and behavioral economics in planning & decision processes and provide the human specific feedback on judgments and actions taken, a continuous learning feedback loop can be created. This will not only improve the outcomes of the process, as per Kasparov’s law, it can increase self-awareness for a human with an open mind.

If we take this a step further, we can even extract psychometrics evaluations from the decisions and actions a human takes. Supported by a coach, these learnings can be taken out of the work environment into the personal life to become an enriched human. We might feel exposed and vulnerable at first, on the other hand, this new type of collaboration provides opportunity to improved self-actualisation, the highest form of Maslow’s hierarchy of needs. It was Maslow’s who said, “In any given moment we have two options: to step forward into growth or to step back into safety.” Automation has limits, if we dare to step forward, our learning, self-development and growth has no boundaries.

P.S.

In an upcoming OpEd in Foresight Q3 (July), I’ll describe a simple forecasting process where a planner can learn from its own judgements by including behavioural economics in a continuous feedback loop between human and machine.