Top features to improve employee retention in your workforce management software
In business, there is always a point where you have to ask yourself, how do we improve employee retention?
This workforce optimization glossary provides an overview of commonly used terms and abbreviations related to workforce optimisation and the use of artificial intelligence and machine learning in scheduling tools. It explains the meaning of specific jargon, acronyms and technical terms.
A/B testing consists of carrying out a randomized experiment that typically involves two versions, though it can also extend to multiple variants of the same factor.
A concept in Computer Science is to develop intelligent machines that can match human reasoning and decision-making.
The amount of labour hours an employer is willing to schedule is determined by external factors such as wage rate and customer demand. Displayed as a graph often in one-hour segments.
Estimate or forecast (some future event or condition) based on an analysis of existing relevant data.
Is the estimated amount of hours required for each role in each department/store over a given period of time?
Timing of a worker’s completion of a particular task or activity.
AI subset where machines learn from data patterns without being explicitly programmed to do so.
Maximizing the potential of a situation or resource.
An AI-driven system can utilize trial and error, obtaining feedback from its actions to gain knowledge. Utilizing a scoring system to “reward” it for producing desirable results is often employed
Maximising available staff to meet the desired number of shifts is the aim of optimising schedules. This involves taking into account a variety of factors such as demand, budget, compliance, staff preferences, fairness and more. Creating the ideal schedule is a complex process, so modern systems now lean on Artificial Intelligence (AI) or machine learning to create the optimal schedule.
Data is stored in a table, with labels for each column and row.
A subcategory of machine learning in which an algorithm must be trained to classify data or predict outcomes accurately before it can be used.
A collection of diverse, natively-stored data, such as video, image, and audio files, which often lack metadata or have insufficient metadata to enable AI training. This unstructured data must be labelled before it can be utilized by a data scientist.
A Machine Learning Algorithm that discovers patterns from unlabeled data. This algorithm generates outputs such as predictions or plans from the acquired model of the data.
A variable factor with a high correlation to staffing levels, such as number of items sold.
Stands for Workforce Optimisation
The process of optimising the number of workers per hour, so that customer demand is met.
