Supercharge your Workforce Management solution with advanced predictive analytics and optimization

Watch WFM optimization beyond AI webinar

 

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Don't change your WFM solution. Enhance it.

 
Market is full of workforce scheduling software that do a good job in running the operational process.
 
However, even the world’s leading WFM software fall short when it comes to advanced forecasting or true optimization capabilities.
 
That is why we deliver a “layer of intelligence” so you can benefit from all available data and realize the full potential of your existing WFM solution. 
 
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Improve the accuracy of forecasting the drivers of workforce demand

Take advantage of not just the historical scheduling data, but also other variables such as detailed receipt data, marketing activities, weather conditions, annual special events and more.
 
Customized for your unique context to guarantee service availability, increase profitability and optimize personnel costs.

Enhanced scheduling optimization

In today's competitive environment, organizations are challenged to re-think how they should approach their labor and what "good" looks like for them. In many settings staffing goals should be optimized for increased sales and superior customer service rather than being considered simply as cost to be minimized.
 
 By deploying individualized staffing optimization tailored for your context, we help you to differentiate from your competitors. 

Insights for workforce planning and contracting

 

Develop insights for workforce planning and contracting by leveraging data from workforce demand drivers and locations, seasonal changes, employee sick-leaves and attrition data. 
 
Connect insights to your preferred BI-solution. 

 

Watch WFM optimization beyond AI webinar

Frequently Asked Questions

I already have a WFM solution – what does Houston’s service add to that?

Houston’s managed services for WFM will enhance the capabilities of your selected WFM solution in three critical areas: 

  1. Improve the forecasting accuracy of demand drivers and optimize their conversion into staffing needs
  2. Enhance the scheduling optimization logic with client specific optimization engines
  3. Generate insights for optimal labor plans and contract structures to best fit forecasted needs, seasonality changes and scheduling needs
Can we use the forecasts for other processes beyond WFM?

Yes.

Houston Analytics' Forecasting service applies machine learning and combines time series data with additional variables to generate purpose-built, ready-to-use forecasts for process optimization and decision support.

With machine learning, the forecast models are  trained to find unique contextual patters using your own historical data, and over time the model learns, adapts and improves. Houston Analytics is able to complement your own data sources with external weather, competition, geographical and demographics data available from various public and 3rd party data sources.

This allows you to optimize product and service availability across different categories and locations, decrease over/under stocking, and gain improved understanding of sales patterns and their drivers at a more detailed level.

Do I need to buy new technology or have in-house expertise?

No.

Houston Analytics' forecasting service is fully managed, meaning you don't need to buy any separate analytics solutions or add more resources to your payroll. Houston's experts will take care of the heavy lifting of data modelling, with optimization engines and state-of-the-art machine learning running in the background.

Forecast outputs are integrated into clients' preferred end-user systems and BI tools for simple-to-use consumption.

How can Houston’s managed WFM service improve my forecasting accuracy?

Most WFM solutions will provide you some baseline forecasts. These generic forecasts are typically based on historical series of data with the assumption that future can be determined by the past. These generic forecasts may struggle identifying irregularities, special events, changes in competitive landscape, etc.

Houston’s forecasts combine the historical time series data with additional variables, such as calendar data, marketing campaigns, weather forecasts, visitor counters and other data sets that may be relevant for your business. With advanced machine learning models, we’ll be able to generate hyperlocal forecasting processes capable of identifying complex relationships within the data.

What is Workforce demand optimization and how can we define the optimal staffing levels?

First, we need to have in place the relevant forecasts of various demand drivers impacting the workforce. In retail and customer service context for instance, we’ll want to forecasts the amount of visitors and gain an understanding what type of demand it generates. Similar visitor volumes may generate significantly varying demand for customer service personnel depending on the seasonality and time of the day. Respectively, typically after special events there’s a spike in product returns which will put a strain in service personnel handling such returns – these demand drivers must be taken into account separately.

Although the conversion of certain demand drivers may be rather straight forward – some may require a bit for advanced modelling to define the optimal levels. In the case for sales oriented customer service personnel for instance, the optimization logic should aim for maximizing overall profitability rather than simply seeking to minimize workforce costs. This can be done by modelling the impact of customer service personnel’s capabilities to convert sales potential into actual sales and with this information we can define the optimal staffing level that is most likely going to maximize overall profitability.

What is scheduling / rostering optimization – How does Houston’s custom approach differ from generic solutions?

Workforce sizing and scheduling follow are so called combinatorial problems – where we need to forecast various demand drivers, create shifts and pair individual employees to them. It’s a process that combines several interdependent decisions that must be made and requires obeying a set of rules / constraints to find a feasible solution.

We do not take the term optimization lightly. If the method can't prove a solution is optimal, then it is not optimization in mathematical sense. This is a key point in the approach we take at Houston Analytics. Most off-the-shelf software is built by coming up with answers for these points that would suit the widest types of clients possible. We reverse this logic, by relying on our technical capabilities to devise tailor-made models for a client specific needs using dedicated optimization engines.