Workforce optimization encompasses all the activities needed to maintain a productive workforce. Falling under the general human resource management umbrella, workforce optimization is a highly specialized function requiring expert knowledge of interdependencies including employee performance, employee wellbeing, staff turnover and absenteeism. Workforce optimization supports the business with key insights into how its workforce is performing. The emphasis is on improving operational efficiency and managing the workforce effectively while keeping overall costs at a minimum.
To achieve these goals, workforce optimization employs analytics to tie workforce management to key business concepts such as growth, profit and the customer experience. Workforce optimization is concerned with enabling businesses to take control of all aspects of their staffing, and helps staff understand how they affect the business’s performance, with an emphasis on improving this incrementally over time. Workforce optimization solutions tie together vertical workforce management systems and provide bridges between the organization’s main stakeholders.
The approach is business-driven and involves automating entire processes, making key data more visible at more levels in order to support better decision-making, ensuring compliance with a wide range of relevant legislation, and solving business problems related to staff. Workforce optimization is best viewed as the next logical step in the move to optimise the performance of staff and to understand and manage the overarching impacts of staff on both operational efficiency and the customer experience.
Our client, one of South Africa’s leading mobile telephone networks, experiences significant increases in customer volumes during the year-end holiday season. Depending on the particular year, between one-third and one-half of annual customer service requests are experienced in the last two months of the year.
Together with the client, we calculated that the cost of additional staff required to serve year-end customer volumes was approximately R143.0m (including salaries, training, support services, technology and systems, rental, and other associated costs), which was prohibitive. In addition to the cost implications, the logistics associated with introducing more than 2,000 comparatively inexperienced customer service staff into the front-line environment for the first time – all of whom needed to be recruited, trained, and deployed over a three-month period – posed significant risks of operational disruption during the client’s busiest period of the year.
We developed an alternative solution for the client whereby we aimed to increase the productivity of existing employees rather than deploy additional resources.
During the course of the project, a substantial increase in productivity was achieved. Before implementation, the cost per unit of customer service was R24.86. During the course of the project, the cost per unit of customer service fell to R2.09 per unit, a 92% drop.
Specific improvements were as follows:
- staff costs remained essentially static (from R56.0m to R55.0m per month during the project) but employee performance increased significantly:
- the number of customers handled per month increased from 18.6m (August) to 26.0m (December);
- staff compliance with turnaround-time and quality-assurance standards increased from 28% (August) to 44% (December);
- customer service levels increased from 49% (August) to 75% (December);
- staff turnover declined significantly, from 42% to just 3%, over the period;
- two years after the project, all the benefits that occurred had been sustained or improved: service levels were 83% on average, staff turnover had remained extremely low, staff compliance had increased to 65%, and the cost per unit of customer service had declined further to just R1.86.
During the course of the project, we effectively saved our client R143.0m, being the cost that the client would have incurred to field the increased customer volumes at the higher quality and service level at pre-intervention levels of productivity.