While digitalization is now being used in many areas of company operations, work distribution methods are for the most part still being handled in the same way as they have been for a decade or more. In many companies, managers are continuing to use a manual method of assigning tasks to distribute the workload among their teams.
However, manual task dispatch is a time-consuming and cumbersome process. Furthermore, unless a manager has been with a team sometime, and/or has a relatively few number of employees to deal with, achieving an optimal distribution of tasks can be difficult. A digitized system can support managers in obtaining effective and efficient task distribution outcomes, as it is able to synchronize layers of information in order to give the best possible staff match for a task. Time is a company’s most precious resource, and managers should be incorporating digital tools to help them utilise their time more efficiently. Using a manual task allocation process can be a laborious process, and even if the manager knows the team well, there is still the possibility of scheduling conflicts or task/staff mismatch. Automated systems are able to analyze data to encompass various factors, including performance, location, and skill sets in order to optimize task allocation. It can be applied to the work distribution process in any sector ranging from healthcare to retail and engineering to sales.
There are four layers of automation that can be implemented to facilitate work distribution, and a company can elect to partially or fully automate their work distribution systems. Each of these layers has a specific and individual function, which can be used on a stand-alone basis or integrated into a four layer fully automated system, whereby each layer will interact and in this way is able to produce a fully synchronized overview of the staffing pool and their availability and suitability for a task.
This first layer of the automated dispatch system provides a basic staff availability analysis. Even at this most simple level, using a digitized system provides a fast and effective solution which will eliminate the issues for which manual systems are notorious, most notably double-bookings or staff not being allocated a sufficient number of jobs.
This layer utilizes a timeline function that works on the same premise as the much-loved, endlessly revised handwritten charts that decorate offices everywhere. The system analyses staff schedules across the pool to determine availability, much like a manager scans spreadsheets. It will filter personnel according to their existing bookings and the number of work hours remaining to fulfill. The system will automatically categorize the data and thus ensure an equitable workload allocation, based on previously determined parameters.
This second layer of the process focuses on logistical factors and screens the selections by analysis of an employees’ geographical proximity. Staff location is a critical factor in work distribution, but it’s sometimes overlooked during manual procedures for task dispatch.
This layer includes the data from layer 1 and then collates that with information held on file relating to geo-positioning. Embedded analytics will ensure each member of the team is only allocated tasks within their assigned region. It also considers the travel times required for an employee to move between task locations. It is possible to achieve a significant reduction in costs by taking into account logistical details such as these. Obviously, a reduction in travel times and distances between jobs, will result in lower operational costs.
Arguably, this third layer is one of the most important in ensuring that the most suitable staff are selected for a task. It requires an analysis of the professional profile for each employee which is then cross-referenced against the job criteria. This is difficult to perform using a manual approach, unless the manager knows his team well and/or has a relatively small number of staff.
Achieving an optimal skills/task match is essential to produce the best outcomes and improve customer satisfaction. This layer selects potential employees for the task based on their skill sets, qualifications and ratings. The system then filters the options, combining the results from the previous two layers, availability and location, to provide recommendations on which staff would be most suitable for the task.
The fourth and final layer combines the functions of the three preceding layers into a fully automated work allocation system. It synergizes all aspects of the selection process from receiving the job request to considering availability and location and then conducting a final analysis of the employee’s professional profile and performance parameters. Clearly, for a manager to attempt to provide a manual analysis of this number of diverse factors would be extremely laborious and time-consuming.
The results of this analysis can also be used to compare staff performance with the averages of the market/sector/company to support further management decisions, both relating to work distribution and other business areas.
Overall, using a fully automated system reduces the amount of time needed to perform routine administrative tasks and optimizes the use of the company’s resources. In this way digitalization takes care of all aspects of work allocation among the team members, using embedded logic to ensure best match selection and an equal distribution of the workload.
An automated dispatch control system will systematize and streamline the processes of task assignment. Trying to manually cross-reference spreadsheets and databases can be an incredibly laborious procedure, taking hours of time that could be used more profitably in other areas. Digitizing work allocation means that managers are freed up to deal with aspects more suited to their role, such as coordinating their teams and problem solving. Digitized work dispatch systems are an effective tool to help managers ensure that the workload is efficiently and effectively distributed. They take the subjectivity out of task allocation decisions by analyzing and categorizing staff data to provide the optimal task/employee match.
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