Case Studies


Digital Transformation

Digital Transformation

Predictive Analytics for
national franchise organisation

The client was a large national franchising organisation with global partners, operating in a complex and dynamic market landscape. With a vast network of locations across the nation and affiliations around the globe, the company faced escalating challenges in sales forecasting, supply chain management, and coordination. Traditional approaches were falling short in delivering real-time insights and adaptability, resulting in operational inefficiencies and missed revenue opportunities. Recognising the need for a transformative solution, they engaged us to implement an advanced analytics platform designed to optimise sales predictions, streamline supply chain logistics, and improve global partnerships.

Workforce Optimisation and Resource Utilisation Platform

Our client faced substantial challenges in workforce management due to outdated, disjointed systems, leading to inefficiencies, poor operational planning, and missed strategic opportunities. The lack of integrated systems resulted in redundant handling of information, while insufficient transparency over workforce competencies hindered effective resource allocation. Siloed data limited the client's insight into market dynamics and resource availability, and reliance on legacy systems imposed laborious, manual processes. These compounded issues significantly undermined the client's operational efficiency and competitive positioning in the industry, highlighting a critical need for a comprehensive overhaul of their workforce management practices.

Optical character recognition on engineering drawings to achieve automation in Document Management.

The client had over 20 years of engineering drawings that required audit, digitisation,  and consolidation into a document management system. With limited resources and a condensed time period due to the sale of assets, using human resources presented a number of issues.


Planning Inefficiencies

Managers had outdated or imprecise information, affecting resource planning and workforce allocation.

Extensive Time

Manual tasks and cumbersome engagement processes resulted in extensive time taken to onboard and engage a worker.


The reliance on physical paperwork slowed down workflows and increased the risk of error.

Siloed Processes

The lack of integrated systems led to reduced cross-functional communication, causing inefficiencies.

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Operating in a fiercely competitive market, our client—a national franchise company with global partnerships—faced significant hurdles in streamlining sales predictions and supply chain logistics. Their challenges resembled those of real-world franchise organizations such as fast-food chains or retail stores that manage hundreds of locations with diverse local market conditions.

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In the transient and dynamic nature of the mining industry, workforce optimisation emerges as a critical aspect of sustainable and profitable operations. The unique challenges posed by remote locations, varied skill requirements, strict regulatory environments, and fluctuating market demands make it crucial for mining companies to have a streamlined, adaptable, and efficient workforce. Failing to do so not only impacts the bottom line but also compromises safety, compliance, and long-term viability. Hence, workforce optimisation in mining is not just a luxury; it's an operational necessity. The right mix of skills, aptly allocated, can mitigate risks and capture opportunities, thus making the organisation more agile and resilient in facing the unpredictabilities of the mining landscape.

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As organisations grow and evolve, the need for efficiently managing a plethora of documents becomes a critical challenge. Our client, a renowned engineering firm, was confronted with the mammoth task of auditing, digitising, and consolidating over 20 years' worth of engineering drawings. Further complicating the scenario was the sale of assets that demanded this effort be completed within a condensed time frame. Traditional methods, heavily reliant on manual human resources, presented logistical and quality challenges, not to mention the extended duration required for completion.


Platform Statistics










We proposed a Predictive Analytics Dashboard that uses machine learning algorithms and real-time data analytics to offer predictive insights into various operational aspects—pricing, inventory, and supply chain management.


Impact and Results

The approach to the selection and evaluation of suitable tech had a profound impact on the client's operations. Efficiencies in resource allocation enabled the company to better adapt to the shifting demands and conditions inherent to the industry. The streamlined onboarding process significantly accelerated the time it took to deploy new workers to the field, enhancing the company's agility and responsiveness. Overall risks were reduced and productivity increased by deploying sustainable tech solutions.  


Our strategy encompassed a phased approach that started with defining the current state, moved on to defining the target state, set out the functional requirements for going to market, and finally involved the selection, evaluation, and implementation of various technology providers. The project culminated with the establishment of a defined enterprise architecture backed by a business case and proof of value.









We decided to employ Optical Character Recognition (OCR) technology to automate the extraction of details from the engineering drawings. For the machine learning aspect, we utilised Keras and TensorFlow frameworks to train custom models tailored for the intricacies of engineering drawings.

Apps and Tech

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Our Process


Current State Gap Analysis

Conducted an exhaustive gap analysis to identify existing inefficiencies in workforce management, site access, rostering, and travel management.


Stakeholder Interviews

Engaged key stakeholders to capture their requirements and understand the challenges they faced with current systems.


Functional Requirements

Based on the gap analysis and stakeholder interviews, we defined a set of functional requirements aimed at addressing identified issues.


Business Objectives

Articulated the business objectives for the new system, including improved efficiency, reduced costs, and quicker decision-making.


Go To Market & RFP Process

Go To Market and preparation of a Request for Proposals (RFP) outlining the functional requirements and business objectives.


Vendor Evaluation

Conducted a thorough evaluation of potential technology providers based on their ability to meet functional requirements and contribute to achieving business objectives.


Proof of Concept

Vendors were shortlisted and evaluated based on key performance metrics that aligned with our functional requirements and business objectives.


Business Case and POV

With a future state architecture defined, we built a comprehensive business case, including a completed Proof of Value to validate the chosen solutions against set objectives and expected ROI.

The insights generated, enabled business leaders to make data driven decisions

The Predictive Analytics Dashboard became an essential tool in decision-making across various operational aspects of our clients business. By leveraging the power of predictive analytics and real-time data, the firm is now better positioned to compete in the market. Future endeavors will include refining the machine learning models and expanding the range of metrics to further improve operational efficiency.

Our strategic approach transformed workforce efficiency whilst elevating operational resilience

By strategically selecting and integrating applications for various workforce needs, we managed to break down the silos and inefficiencies that plagued our client's operations. The result was a more agile, efficient, and responsive workforce, allowing the mining company to focus on their core operations while achieving significant operational savings.


The project demonstrates the immense value of integrating advanced technologies like OCR and machine learning in overcoming operational challenges. By utilising a smart mix of Keras and TensorFlow for OCR, we successfully automated a substantial part of the document digitisation process for our client, significantly cutting down both time and human resource requirements. By focusing on continuous improvement and adaptation, we aim to make this solution even more robust and versatile for the client’s future needs.