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Workforce optimisation is an integral part of creating operational excellence and overall efficiency. Using various automation processes, intelligent workflow restructuring, and redesigning of work environment, Ecolibrium assisted the client team in creating a more efficient workforce and a better working environment that boosted their productivity, ultimately leading to lower operational costs through efficient reassigning of skilled labour.
Data-driven optimisation is everywhere now, finding applications from industrial to medical fields.1 It enables organisations to gain deep insights into workforce behaviour, their interactions with the environment and then create changes in the associated variables like task automation, asset staging, predictive demand-based supply, energy utilisation, lighting, temperature, airflow, etc., to create a better, more productive workforce and workplace.
However, most processes in most organisations are not automated and decision making is usually not data-driven. This is why there is a huge opportunity for optimisation in most facilities.
The Micro and Macro
Optimising any workforce with a target of increasing operational efficiency and reducing operational costs is a challenge. It can be eased if we look at it from an experience optimisation point of view. 2 This is because the organisation is not a simple formula with a single input and output set. The variables mentioned earlier are just a sliver of the many factors that can enhance workforce productivity, thereby increasing the organisation’s overall profitability.
In industry 4.0, we are now gifted with the possibility of collecting and analysing all relevant data points using smart meters and sensors, leveraging the AI/ML algorithms like the ones that make up Ecolibrium’s flagship AI/ML platform SmartSense, and speed up the process of discovery and insight.
Data is already being used to optimise human resources, and it is fast becoming a brand new tool for taking informed decisions through ongoing research and implementation. 3
Data-Driven Decision Making for Higher Profitability
Ecolibrium was trusted with a similar opportunity where a global innovator of IT and business services across 50+ countries its facility management company for workforce optimization. The client company wanted to innovate and leverage predictive analytics and energy accounting.
Ecolibrium deployed SmartSense with the goal of workforce optimisation. Using its versatile applicability for asset-level insights and accurate analysis, the data-science team, which is also a part of the SmartSense platform implementation, came up with actionable recommendations.
The results speak for themselves:
• 42% reduction in manual processes of the engineering workforce, freeing up skilled resources by automating tasks.
• 21% operational cost savings.
• Additional benefit of a threefold increase in the project’s gross margin for the facility management company.
If you would like to know exactly how the Ecolibrium team achieved this, click on the link here for the detailed case study.
If you are interested in discovering how SmartSense can redefine your organisation's maintenance strategies, reach out to our team to schedule a personalised demo and consultation.
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Bibliography
1. Kaderka, R., Hild, S. J., Bry, V. N., Cornell, M., Ray, X. J., Murphy, J. D., ... & Moore, K. L. (2021). Wide-scale clinical implementation of knowledge-based planning: an investigation of workforce efficiency, need for post-automation refinement, and data-driven model maintenance. International Journal of Radiation Oncology* Biology* Physics, 111(3), 705-715.
https://doi.org/10.1016/j.ijrobp.2021.06.028
https://www.sciencedirect.com/science/article/pii/S036030162100746X
2. Glasscock, B. (2015). A data-driven, experience-based approach to workforce optimization. Hydrocarbon Processing.
3. Arena, D., Tsolakis, A. C., Zikos, S., Krinidis, S., Ziogou, C., Ioannidis, D., ... & Kiritsis, D. (2018). Human resource optimisation through semantically enriched data. International Journal of Production Research, 56(8), 2855-2877.
