C-OPT brings many years of knowledge and experiences to the table when it comes to solving local to global business problems. They have saved corporations like SAIA and ExxonMobil millions of dollars - what can they do for you?
Below you will find a number of recent case studies where C-OPT played a role in designing unique solutions to unique business problems. You can find a more in-depth PDF document of each case study after their summaries.
Improved Load Planning for SAIA
Unique Algorithm-Based Technology
SAIA (a successful freight carrier company in the US) approached C-OPT while in the process of creating a more cost-effective load plan. SAIA's current load plans were ineffective as they were created manually by humans. These plans can take days to create but SAIA's tight schedules required them to be completed in only a few hours.
The solution provided by C-OPT was to construct an algorithm that quickly created load plans in a fraction of the time of human methods. This algorithm is also the only technology of its kind that is able to handle daily freight-volume fluctuations.
Download this case study (PDF).
Markdown Management at a Major Department Store
Improved Decision Making through Maths
A major department store chain in the US was having trouble comprehending the full impact of their decisions concerning markdown policies and contracted C-OPT to assist them in making better decisions to improve future revenue.
C-OPT analysed the historical data from all stores in the chain, and interviewed many store managers to get the full picture. Eventually, researchers at the Centre discovered that there were two factors that had a large influence on the success of markdown policies. With the aid of C-OPT, the department store was able to greatly increase their revenue by exploiting the full potential of controlling these factors.
Maritime Inventory Routing
Balancing Multiple Factors Through Algorithms
ExxonMobil needed to find an economical way of transporting vacuum gas oil from Europe to refineries in the USA. This particular challenge involved several complex factors that could have affected the cost-effectiveness of the final result, and therefore required an high level of care in its handling.
C-OPT developed an algorithm which not only balanced these multiple factors efficiently, but which also produced high quality solutions to other, more complicated transportation problems.
Prioritising Healthcare Systems Improvement
Bayesian Hierarchical Models for Better Estimates
The Australian Council on Healthcare Standards need to find a better way to measure the performance of healthcare organisations.