Smoothing workload is a strategic necessity for on-site service delivery

Delivering goods and services to your customer’s door will always present challenges in terms of peaks and troughs, so smoothing workloads – that is, balancing the distribution of effort evenly over time as a function of production capacity in terms of available resources – is key to operating performance and profitability.

While managers and scheduling personnel juggle the many adjustments needed on a daily basis to enable them to satisfy a maximum number of customers, they also need to be able to take a medium-to-long term view if they are to take decisions in a timely manner – these might be decisions about administration, training, recruitment – and ensure an optimum match for the production capacity of their teams and the foreseeable workload.

Better understanding through analysis

There’s no way to predict the future with any degree of certainty, it’s true, but analysing the past is always a useful way to approach gaining insight into what makes the business ‘tick’, and so to programme realistic workload plans in the medium term. Before attempting any kind of workload smoothing, analysing your operating data history (for the year n-1, say) will allow you to identify the imbalances over time (peaks and troughs of activity, short-term or seasonal) and also any discrepancies between servicing capability on the part of your resources and workload (by agency, team, person, month, week…) and highlight over- or under-capacity.

Business data represented graphically, as generated by GEOCONCEPT with the  solution, help you identify imbalances and discrepancies, and identify the underlying causes. The user has immediate access to representations of sub-adjacent data according to different axes (temporal or organisational) and variables (year, quarter, month, week, agency, team, staff member) and can explore this information deeply and constructively.

Imbalances in workloading shown graphically, prior to smoothing

This exploratory stage is critical, since the deviations and discrepancies it reveals are often indicative of the real problems you need to address as a matter of urgency, and resolving them may involve decisions that are fundamental to your business and your overall strategy, for example:

• an uneven distribution of the customer portfolio, that calls for a redesign of the , or different siting of agencies;
• a need to upgrade competences/qualifications of certain resources, through training or redeployment of personnel;
• the need to correct the ratio of numbers of missions/human resources between agencies with a recruitment campaign or geographic redeployment of certain staff members.

Forecast and simulate for optimum smoothing: realistic and achievable

Using your operating data for the year N and/or N-1, your new scheduling tools will allow you to produce realistic forecasts for the year N+1/ and/or N+2 (or even more). Next, in applying your operating model to these forecasts, you can create simulations to test out different growth scenarios, or restructuring plans, including new staff members or reducing the headcount, depending on the results you obtain. These simulations should help you to see your way through the decision-making by giving answers to hard-headed questions:

• Can we withstand the load, in terms of capacity, if the number of missions increases by X%?
• What will happen if we transfer resources from one agency to another?
• Should we replace someone who is leaving with another person with the equivalent profile?
• How many people should I employ if demand suddenly rises?
• To underwrite a new type of business offering, should I retrain existing staff or rather, recruit new staff? What new kind of imbalances might this create?

When you configure your hypotheses in the solution, the business forecast it produces will take your strategic choices into account, but don’t forget: it is not yet optimized: it will highlight uneven distribution between resources or peaks of activity on some periods that you will need to correct. It is at this point that smoothing on workload comes into play. This operation needs , and there will be two ways to pursue the analysis:

• Automated smoothing, applied by an optimization engine, that has been programmed to apply your scheduling rules alongside a series of constraints defined for a group of resources for a set period;
• Manual smoothing, to handle exceptional events and unforeseen snags or difficulties, to refine the detail and arrive at the most balanced planning possible for the time period under review, taking into account your resources and your constraints.

Optimized workloads, after smoothing