Moxy has experience working across many industries including the service orientated ones.
Their Commercial Challenge
Service business face particular challenges in the delivery to customers of consistent and reliable service.
The first challenge stems from the uneven arrival of new jobs and that variation if not well forecast tends to drive inefficiencies into the organisation.
The good news is that the variation in job demand tends to be quite predictable as it is driven by a range of factors: geographic, environmental, social and temporal.
For example in an industry like automotive roadside assistance, jobs tend to be denser in the city versus the country. They tend to occur more frequently during the week, on the way to work. They tend to occur more frequently after a long weekend particularly when the weather is cold. Understanding the importance of these factors is the first step towards forecasting demand.
The second key challenge flows from the variation between jobs. Not all jobs are the same:
- They are not all equally important to either the consumer or the organisation.
- Some jobs are harder, take longer, require more specialised skills and equipment.
- Not all resources can service every job.
- Physical restrictions such as travel time, traffic, access and inventory cannot be ignored.
Ultimately, the service levels that the customer receive are a function of how well the demand was forecast, and how accurately the organisation was able to match the supply of labour, skills and equipment to meet that demand. An oversupply results in low utilisation of resources. Under supply results in lost work, long wait times and unhappy customers.
Moxy takes a bottom-up, fundamentals approach to matching supply and demand.
We start by analysing historical performance in terms of types of job, job arrival rates, resourcing levels and particularly the resulting level of service delivered.
From that, we build a model (often using discrete-event simulation) where take a days worth of jobs and simulate what should have happened given the resourcing levels we actually had. We calibrate our models comparing the actual versus predicted performance until our model can reliably reproduce a days trading.
With a well-calibrated model, we can then simulate the impact of different resourcing schedules, skill mixes, dispatch algorithms or inventory replenishment schemes to come up with optimum solutions to improve service quality and reduce cost.
We can answer such questions as:
- How likely are we to hit our service levels on a given day?
- What if we changed our skill mix of service personnel?
- When should we use overtime? When should we simply increase the number of people rostered on?
- Where should we base our resources?
- What is the best use of idle resources?
Such questions are fundamental to service organisations. On one such client, the difference between the current implementation and optimum solutions was a saving of $50m per annum and an improvement in service levels. The cost-saving came from a combination of:
- Reduction in unplanned and planned overtime
- Better matching resource schedules to demands peeks
- Higher levels of automation of dispatch and reduced travel times
- Higher utilization of capital equipment
As a result of the analysis, the business could confidently undertake initiatives such as:
- extending training so that resources were more flexible and so could take on a wider range of jobs
- optimisation of inventory, to ensure that each serviceman had the right tools and equipment to perform the job
- clarity around the optimal skill mix for the team to inform hiring
- a clear roadmap for the setup of new depots based on changing demographics
“The interaction of jobs, skills, rosters and vehicles is complex. Moxy helped us to focus on the important variables that were inside of our control.”
Executive Director Roadside Operations