Prescriptive Analytics

Empower your team to make better data-driven decisions with advanced analytics

There are three main kinds of analytics, each helpful for different business objectives. Knowing the distinctions between them is key to unleashing potential from your data.​

Descriptive analytics examines what happened in the past by looking through data sets for patterns and trends. It turns the stream of facts your business collects into information you can act on, plan around, and measure. ​

Predictive analytics aims to predict likely outcomes and make educated forecasts from historical data. Predictive analytics extends trends into the future to see possible outcomes and anticipate changes in the outputs before they are observed. However the insights it generates are limited to the data it receives. ​

Prescriptive analytics uses past and real‑time data from a variety of sources to identify future outcomes and how to improve them. It provides actionable insights and recommendations to help you determine what should happen to achieve the best possible outcomes. ​

Prescriptive analytics has the greatest potential to improve how businesses are run. ​

We apply machine learning in manufacturing and heavy industry environments to empower operators. This allows them to transform their workplaces into smart plants and make the best decisions to improve production, rate, yield, and more.​

Knowing what will happen is not enough

Organisations must go beyond predictive analytics, which only tell them what will happen. Ensuring the frontline can implement and sustain identified opportunities is the way forward.

Many organisations 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, adoption at the frontline tends to stall and often does 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.

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 investments.

Proof of value informs a carefully designed, phased, and short‑term 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 the frontline change? What uplift is achievable if a Virtual Expert is implemented?

Unlocking the value of prescriptive analytics tells you when something happens, why, and what should be done 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 input from subject matter experts (SMEs), you have data with limited insights into how best to leverage it. The back‑and‑forth process between data scientists and SMEs 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 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, 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 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 improve by using it.

‘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 for data to flow continuously so the model can be updated while 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 operations is what contributes to a strong retention strategy. It requires upskilling and intellectually challenging process engineers to ensure they are retaining systematised knowledge about how best to run a plant given daily changing conditions.