Past & Current Projects

The Need: International Logistics Planning

The Department of National Defence (DND) of Canada sometimes outsources mission-support services to private contractors to help alleviate strain on the Canadian Forces (CF) in areas where military expertise is less crucial. The challenge for Canadian military planners is to decide upon those missions in which to leverage private contractors, to what extent and in which capacities.

The Syllogix Solution

Syllogix was contracted to build a simulation model to support the logistics planning effort of the Canadian Forces. Starting with the current state of CF resources and based on historical and live operational mission data, the model simulates how the current mission requirements might evolve over time and how this, combined with future mission requests, would impact the CF's need to use private contractors to support its desired international engagements.

Implemented in a commercial simulation package, and wrapped with attractive and easy-to-use input/output modalities in a spreadsheet format, the Syllogix solution comprises a complete decision-support tool that requires little-to-no modeling expertise to understand and interact with. This accessibility renders the tool even more beneficial to DND since planners at all levels of the organization can more easily appreciate the use of the model and exploit it to study important policy options with respect to mission support planning.

By running a number of scenarios with different starting assumptions and analyzing the results, CF logistics planners can now answer a number of questions with new-found analytical rigor. For instance, the planning model could be used to study how expanding the Forces over time, combined with accepting a growing mission load, might affect the use contractors over a 5 year planning horizon. Such wide-ranging and multi-faceted questions could only be guessed at without the help of analytical support tools.

Once fully validated and integrated to DND’s data systems, the Syllogix simulation model, will augment DND's international logistics planning process with objective insight, helping our military decision-makers develop policies that keep our forces strong and safe for years to come.

The Need: Long/Short Term Financial and Operational

Planning

Station Mont-Tremblant, a world-class ski resort and one of North-America’s premiere tourist destinations, needed to better predict the number of snow enthusiasts that will visit its slopes each winter in order to make planning decisions that have financial, operational and customer service related ramifications. Some of these decisions must be made months in advance, others on a much shorter time frame. This makes accurate planning a difficult ongoing problem for the resort’s management team.

The Syllogix Solution

Syllogix developed a dynamic and flexible forecasting model, powerful enough to incorporate long-term trends and last-minute effects due to weather, for an all-in-one unified solution.

The forecasting model constructed by our analysts revolved around well known regression-based techniques, using a large number of 0/1 variables to provide maximal flexibility in the definition of independent predictors.

A 7 year historical attendance record from the resort formed the dataset upon which the model was run. This time-horizon is long enough for the model to detect economic growth effects over-time, and the important cyclical variations in the data. Due to the fine granularity of the model structure, seasonal holidays (e.g. Christmas, March Break) can be specified by the user so that the model automatically adjusts the forecast appropriately during these peak periods. This functionality allows financial planners at Mont-Tremblant to make long term budgeting and investment decisions for upcoming seasons.

The model was augmented with the added capability of dynamic revision of the short-term forecast based on weather predictions. The model was made to incorporate daily meteorological data, in such a way that the forecasted attendance values would correctly reflect the influence of similar weather patterns in the past. This added functionality gives the model a great deal more power for performing operational decision-making at the resort (e.g. scheduling correct staff levels).

In a benchmark test using a year-long hold out sample, the ‘hands-free’ forecasting model was seen to perform at least as well as experienced human executives in the resort’s finance department who were impressed with the accuracy and flexibility the automated solution provided. Work is now underway to further refine the model to become fully integrated into Mont-Tremblant’s financial and operational planning process.

The Need: Surgical Resource Allocation

Over the past decade, health system administrators in Canada faced a great deal of pressure to do more with less. Governments slashed hospital budgets while the public continued to demand high-quality and timely access to care. Now that governments are beginning to re-establish funding levels, hospital decision-makers must make important choices in regards to how to best allocate resources amongst surgical programs, so as to most effectively treat patients and reduce overly-long waiting lists.

The Syllogix Solution

Syllogix developed for Walker Economics Inc., a respected consultancy in health policy, an embedded optimization model to constitute the core intelligence engine within a complete decision-support tool for health system decision-makers.

An integer programming model was formulated to select optimal weekly treatment slates, based on the needs of simulated elective surgery patients on waiting lists who must be treated before their Maximum Allowable Waiting Time (MAWT). Taking into account weekly resource allocations defined by the user, the model selects for treatment those patients that are most ‘in need’ of surgery, as calculated by the objective function - which may be arbitrarily customized to reflect operational priorities. Minimum treatment volumes may be defined, by the user, to enforce real-world targets for certain surgical groups. Further limitations may be placed on how specific ‘resource packages’ may be consumed, allowing health administrators study the effect of targeted campaigns or policies.

By creating different ‘resource schedules’ – defined over a certain number of weeks into the future – the decision-maker uses the software tool to watch how waiting lists can be expected to evolve over time, based on simulated patient arrival volumes. The output of the optimization module (patients to treat each week) is subsequently extracted by the software to compute expected resource utilizations, and waiting lists breakdowns by surgical service and time on the waiting list for each week of a particular ‘run’. Results are then presented in attractive three-dimensional graphs for ease of interpretation and study.

This patient selection optimization model, was coded into a callable library, and seamlessly integrated into Walker Economics’ enterprise decision-making software. This software, built using a modern web-services paradigm, is now being considered by many health jurisdictions across Canada for implementation.