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Level up: evolving automation into autonomous decisioning

(Image credit: Image Credit: MNBB Studio / Shutterstock)

As human beings, free will is one of our most defining traits. The ability to assess the information available and independently decide on a course of action makes us who we are. But what does that ability actually mean - what is a ‘decision’? In essence, it means identifying and selecting one potential scenario from many, moving towards one clear set of actions rather than another – for example, going for a walk rather than sitting on the sofa.

The same ability to decide is essential to good business. Businesses face thousands of decisions every day. Making good choices in those situations is essential to business success. Companies have business rules and other control logic to help them make the best decisions about the best actions – company policy might dictate that a certain offer is always made to customers at a certain point in the purchase journey, for example. But that doesn’t mean it’s easy to build that logic. And as the world goes digital, the number of decisions that have to be made is increasing all the time.

So to what extent can we outsource the power of decision-making? And is that even possible? The answer lies in automation.

Making decisions means solving problems

First, I would like to point out two contradictory decision scenarios: the engineering and the business perspective. From an engineering standpoint, decision making is a problem-solving activity to identify and analyse the available set of actions, then determine the most appropriate option given the existing expected conditions and constraints.

From a business perspective, increasing customer expectations, easily changed perceptions, unforeseen competition from disruptive players that didn’t exist a few years ago and new business models are all bringing new challenges to the entire value chain. Organisations have to accommodate that complexity and speed of change without sacrificing productivity, quality and timely delivery. It’s all a matter of coping with the speed of change while remaining competitive.

Both groups understand that they can automate decisions to make the process more efficient and valuable. In the business world, we call this ‘decision management’. Decision management systems capture, automate and govern frequent and repeatable business decisions.

In a digital economy, business decisions – such as those related to the increasing focus on delivering great customer experience at all points of contact (omnichannel campaigns, special offers, dynamic pricing, buy online and pick up anywhere and so on) – are subject to frequent changes and have a short life cycle. As a result, analytical models tasked with making these decisions need to be dynamic and – increasingly – independently able to grow with the business through machine learning (ML).

The tools for the job

But intelligent decisioning requires even more than that. You must be able to trigger the determined action set and capture the actual results. And, finally, you need to ensure traceability, auditability and explainability of the entire decision-making process.

With that in mind, there are several high-level capabilities you need for performing intelligent decisioning. First of all, you need to be able to diagnose and identify the situation, before applying your business rules. To achieve this, you’ll need to define viable actions with artificial intelligence (AI) and machine learning models and other decision logic elements. These in turn can then determine the optimal or best action set – or at least an acceptable task to resolve the situation.

Categorising events

Let us begin with data collection. The key is to gather as much data as possible. In manufacturing, for example, you need to get data from each machine or production element on the shop floor. This includes operational and execution information like the arrival of materials, start times of each programmed task, tooling used, task progression, stop time, completion time and, finally, departure of the finished product.

You would also need to collect sensor information from tools, including machine temperature, vibration, tension, stress and energy consumption. Next, you stream all this data for processing and analysis.

Identifying critical issues

Within the stream, the system normalises or standardises the data to prepare it by filtering, combining and aggregating. Then we can assess and analyse the data by applying advanced analytics, AI and ML models to identify and infer the situation. From there, critical event detection and prediction is all done at sub-second speed.

If a critical event is found, that information is sent to the decision engine. The system also stores model information, including input data, past data (lag size), model details and outcomes. And all this happens while analysis and processing continue.

Making a decision

The decision is the result of a decision flow in which the system applies a set of business rules and models (advanced analytics, AI, ML) to evaluate event criticality and impacts. Within those decision flows, the system can call in other data sources – for example, a knowledge base – to assist in the decision-making process.

The input data for the decision flow should be stored, as well as all the relevant input and output information of each decision step, including model details and outcome. This is all linked to the triggering events to ensure explainability.

Acting on your information

So, now that the best decision has been determined, the final step is about what to do. Thus it’s the time to automatically trigger the execution of the appropriate protocol, a set of actions or tasks meant to address or resolve the detected situation. All the relevant information should be recorded to ensure explainability. Capturing the actual outcome of the applied protocol is key to monitor the decision flow for relevance and fitness, and for re-feeding process autonomy.

The above-mentioned pace of change requires more and more complex decisions to be made in short time frames. Some of those decisions could even be required to be made at the event moment – for example, continuous quality control/assurance will reduce production defects and rework or product return.

It's essential for businesses to evolve their systems from automation to fully autonomous decisioning in order to achieve greater efficiency and flexibility. As the number of decisions that must be taken in a short space of time continues to rise and with customers expecting split-second service with no loss of quality, autonomous analytics are crucial for business success. Organisations need to make sure they use all available data coming in from customers, partners, IT systems, production environments, supply chains and anywhere else that’s relevant.

With the right AI and machine learning models embedded in the decision-making process, organisations can achieve truly intelligent automated decisions – and drive value as a result.

João Oliveira, Principal Business Solutions Manager, SAS