If you want to improve performance, improve efficiency and reduce costs, ORITAMES is for you. If you want to migrate from messy spreadsheets, or are stuck with hard to implement complex programs, ORITAMES is for you.
ORITAMES APS Scheduler is a powerful Advanced Planning and Scheduling system including a versatile scheduling optimizer. ORITAMES is simple to use but can handle large and complex problems in areas such as multiple projects planning, manufacturing, event organization, routing, maintenance, staff assignment, software development, ...
ORITAMES is innovative, using the most recent advances in the science of operations research to tackle challenging real-life domain-specific cases and generic problems.
When selecting an APS system for implementation you need to pay attention to several factors that influence the time, efforts, costs and, of course, the benefits that you get from such implementation. The 4 main factors are: data quality and availability, IT systems in place, type and complexity of production and maturity of the organization.
Data quality and availability are important. An APS system needs to have an accurate and up-to- date vision of what is going on in the enterprise to avoid the "garbage in, garbage out" problem. You need to model your projects or orders, your workflows and your resources. You also have to track the status of the activities and resources to create correct timely schedules. The nice thing is that you do not need to have perfect and complete data at the very start. Often you already have more or less complete data and more or less accurate data. But further collection, sanitation and validation will be part of the APS implementation roadmap.
In the same way, having an ERP and/or MES system before you start implementing APS could be nice to have ... but it is not an absolute must. If data is already available in the ERP or MES system, and integration and data extraction can easily be automated, then that's an advantage. But the alternative to model the data in the APS itself during the implementation project is also feasible.
Type and complexity of production is a key issue. Many APS systems are case-specific and cannot handle large and complex data sets. Or their schedules are very poor and useless because the scheduling problem they try to solve is too complex for them. This is the main reason why the choice of APS is so important as not all APS systems are equal. In most cases flexibility, scalability and genericity should be a major requirement. The other major requirement is the ability of the APS to produce optimized results and handle realistic complex data sets. APS systems having advanced heuristics and/or artificial intelligence methods are the only way to achieve large benefits.
Finally, one should always take care of the maturity of an organization. Implementing an APS is a long time-consuming effort. There is no silver bullet, and even with easy GUIs, not everything will be "easy": you need to manage change and you need skilled people to implement and use your APS. But the APS implementation process is also the best way to improve efficiency and competitiveness!
ORITAMES APS Scheduler goes far beyond the leading State of the Art schedulers on the market. Those systems are based on "dispatching and sequencing algorithms" or "heuristic rules". Some of them even provide ways for users to create custom rules. They work by simulating operations and choosing activities to be performed from "available work queues". In the best cases, those systems can perform forward and backward sequencing.
But sequencing-based schedulers are myopic. Even though they can deliver better results than just plain static ERP forecasts, their optimization heuristics do not have any real intelligence to explore the huge space of possible schedules, and will never find anything close to the real optimum. They are far from being able to solve any NP-hard scheduling benchmark problems beyond very small cases. Basically those systems are based on techniques and concepts of the 1970's and 1980's.
ORITAMES is a new kind of APS based on R&D of the 2010’s. ORITAMES combines several machine learning meta-heuristics such as Genetic Algorithms (GA), Taboo Search (TS), Simulated Annealing (SA), Swarm Intelligence (SI) and others to create a unique Artificial Intelligence APS system. The unique breakthrough achieved by our R&D was to be able to use the modern AI methods and apply them to large real-life problems taking into account the multiple and complex constraints of real cases.
You do not have to believe us! We can demonstrate our claims using benchmark datasets, and we'll be more than willing to prove the advantages of ORITAMES using your own data.
ORITAMES is a multi-resource scheduler. Most schedulers can only handle one type of resource, or only a single resource per activity. ORITAMES can handle multiple instances of equipment, people, consumables, tools, locations ... and can also combine several resources together. Any activity can have multiple modes to closely model reality.
ORITAMES is also a multi-objective and multi-criteria scheduler. It does not just optimize one single Key Performance Indicator (KPI) at a time, e.g. minimizing makespan but with many setups or bad resource utilization, but can combine several KPIs together, produce Pareto–optimal schedules and analyze objective trade-offs. Many objectives can be selected together such as makespan, deadline satisfaction, Just In Time, resource utilization, consumables usage, activity switches, cycle time, ... ORITAMES can also help you analyze « what if » scenarios without effort.
ORITAMES differs from traditionnal single project planning software in than in can handle multiple and complex projects at the same time, automatically sharing the available resources between the projects. In this context, projects can be anything from production orders, events to organize, construction works, ... Projects also do not need to be sequential : graphs of dependencies or time constraints are easily modeled.
ORITAMES does not use a single “one size fits all” solver method, such as simulation, simple dispatch rules or a search heuristic. ORITAMES includes many solvers and heuristics and, depending on an analysis of the case at hand, it will try and apply many methods to find the one that produces the best results. ORITAMES includes sequencing, heuristic search, genetic algorithms, machine learning, ...
ORITAMES can be used offline, online and even in real-time. It can handle large cases, with dozens of resources, and hundreds or even thousands of activities. In order to create close-to-optimal solutions very fast, ORITAMES can also used parallel multi- threaded solvers, and thus benefit from modern multi-core computing hardware.
ORITAMES is an open system that stores input and output data in easy to understand XML formats. It can also load raw data in usual OR formats. ORITAMES uses a friendly Graphical User Interface (GUI) for case modeling, scheduling optimization, results analysis and interactive reporting. Typical reports include project schedule tables and lists, Gantt charts, resource schedules, resource utilization charts, ... ORITAMES provides a callable API for easy third party integration and automation.
ORITAMES can be used as a standalone application or it can be integrated with other IT tools such as ERP, APS or MES. It is easy to deploy, to extend and to integrate.
ORITAMES is based on the high performance UBABU software application building blocks from MangoGem. It is 100% written in Java and C++ and platform-agnostic. ORITAMES runs on MS Windows, Mac OS X and a variety of UNIX and Linux platforms.