Prescriptive Analytics

Knowing what will happen is not enough. Organisations need to go beyond predictive analytics, which only tell them what will happen. Ensuring the frontline can implement and sustain the identified opportunities is the way forward.

Many organisations work with their data scientist and/or outside firms to develop predictive analytics, but they often do not make them practical enough or actionable at the frontline. As they embark on these macro change programs, they tend to fall short on adoption by the frontline and typically do not deliver the expected value.

At a foundational level, this is because there is a primary focus on tools & technologies and having ‘perfect’ data rather than driving operational improvement outcomes.

Proof of value

Ours is a proven strategy for how to go from reactive to predictive and from predictive to prescriptive. It centres on a simple pilot that demonstrates ‘proof of value’ before making substantial investment.

Proof of value informs a carefully designed, phased and short‑term gated approach to ensure hard dollars are not wasted on initiatives that provide little value. It focuses on value drivers and levers that will provide positive outcomes: What have we learned from the data? What are the insights? How can they be applied today? What must operators at the front line change or be able to do? What is missing? What uplift is achievable if a Virtual Expert is implemented?

Unlocking the value of prescriptive analytics tells an organisation when something happens, why and what they should do to correct for it – providing operational recommendations that are fit for purpose and drive accelerated outcomes. Operations teams will always be ready to adjust current settings to beat the averages while improving process stability with controlled optimisation.

Data analytics programs are commonly driven by data scientists or outside firms; however, data scientists on their own are often unable to give frontline workers the insight needed to understand and implement what the output of models are telling you.

Without the input from subject matter experts (SMEs), you have data with limited insights around how best to leverage it. The back‑and‑forth process, between data scientists and SMEs, is what demystifies the data analytics ‘black box’ – leading to consensus and action plans that can be understood and justified across the organisation, and most importantly, at the frontline.

Cutting through complexity

Standard analytics tools are quite complex and require data science expertise. Our team at DataStories delivers an easy‑to‑understand tool to internalise insights. This, in addition to involving SMEs early and often, greatly accelerates our rate of implementation to guarantee ROI and overall program success.

We ensure the technology used fits the need and that operators effectively use it to deliver desired outcomes.

The complexity of transformation programs is often underestimated. Many programs are not being adopted because the frontline does not know how to use the data. Buying technology is easy, but changing behaviours is difficult.

Many organisations lack the alignment, capability and willingness to implement required changes. To make advanced analytics improvements last, 75% of the focus should be on the frontline understanding what the data is telling them, why it is important and how they can effectively use it to improve.

‘Wiring’ the organisation for success

To ensure model insights are fully integrated and used effectively to improve daily decision making while driving high performance, the frontline must be fully upskilled with robust training and on‑the‑job coaching. Roles and accountability should be clearly defined and key stakeholders aligned. KPIs tracking progress towards targets should be established, made visible on performance dashboards and regularly reviewed.

A robust data architecture and infrastructure must be established to ensure data flows continuously update the model, and data collection limitations are proactively addressed.

Demonstrating value through a successful analytics implementation in one area facilitates buy‑in across the organisation – making a powerful case to identify new use cases for prescriptive analytics, either within the existing site or extending to other sites or regions.

Significant performance and stability uplift achieved and sustained by the operations is what contributes to retention strategy. It requires upskilling and intellectually challenging process engineers to ensure they are retaining systematised knowledge about how best to run the plant given daily changing conditions.