Optiveat – Benoni
The BENONI program allows you to size the workstation optimal number to open in order to minimize your hourly costs and to maximize customer satisfaction by finding the « right length » for your queues.
- The administrations
- Call centers & after sales hotline
The problem of queuing: between customer dissatisfaction and oversupply
How do you determine the optimal number of workstation to open during a day? Between 12pm and 2 pm? After 6pm? The arrival times of your customers are random.
The same applies to the time it takes to process a customer (larger or smaller basket, longer or shorter file).
In retail, it is known that a customer may become discouraged and leave a store without buying if he thinks he will wait too long at a checkout. Out of fear of missing sales or frustrating a customer who stays in line too long, a manager may tend to over-staff, making the department less efficient.
In any case, a long queue creates dissatisfaction for customers and stress for employees.
Benfit of the program: anticipation rather than reaction
Our BENONI program allows you to forecast the number of checkouts, counters or telephone lines to be opened so that you can reach your objectives while minimizing your costs. This forecasting phase makes the good management of the planning of your teams easier and allows to optimize your performance. It allows you to anticipate traffic peaks and limit the surprise effect linked to them.
How does it work?
The OPTIVEAT-BENONI program helps you to find the right number of posts to open in order to cover your needs in an optimal way, i.e. by checking whether the conditions for receiving your customers are in line with the minimum service objectives you have defined. When it is run, the program will therefore return a number of reliable statistical indicators that you can use in your situation analysis.
To do this, it will first divide the studied period of time into several intervals of a length you have chosen, then it will determine the distribution of customers over each of these time intervals. It will run several hundred simulations with this distribution. This step makes it possible to keep the distributions observed in your data but to change the order of arrival of the clients and the order of the processing time values each time: in this way, the randomness that can be observed in the arrival times of the clients and in the processing time of the files is integrated. Finally, the program will calculate the average of each indicator for all simulations. The number of simulations was chosen to be sufficiently high for the results to converge: the randomness observed was smoothed out. This approach makes it possible to reliably obtain, among other things:
- the average inactivity time of your stations ;
- the average length of your queues
- the average waiting time of a customer at a checkout.
The results are presented as follows:
-Microsoft Visual C++ 2005 and later
-Microsoft Excel 2007 and later
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