Ignoring resource constraints, this Master Schedule had a makespan of 264 days. Using Microsoft Project’s Resource Levelling function, a makespan of 506 days was achieved. This same Master Schedule optimized using ORITAMES produced a makespan of 396 days!
The main objective of Aslepiedra was to eliminate late deliveries, while reducing overall project cycle times. Elimination of idle times and unnecessary material transport was set as a nice-to-have side effect
The main objectives of Fabrica was to optimally load the batch furnaces, reduce the setups and eliminate late deliveries, while reducing overall project cycle times. Before using the APS, they needed a very high Work-In-Process (WIP) to keep the lines going, which meant a very high capital cost.
The current COVID crisis is nightmare that caught the world unprepared!
For industrial enterprises, it just intensified a key element of the reality of today's manufacturing world: the ability to cope with Volatility, Uncertainty, Complexity and Ambiguity (VUCA). Any organization needs to adjust to never seen variances and new production constraints impacting efficiency. Everyone has to become more agile and more reactive in order to cope with changing demands both when a downturn happens, as well as when a fast recovery requires a steep ramp up.
Anyone claiming that the problem of finite capacity planning & scheduling for manufacturing enterprises is fully solved is clearly missing the point. In any but very simple cases, the problem is still open both at the scientific as well as at the industrial level.
MangoGem launches ORITAMES APS Scheduler v2.5 (Update 4) adding extended AI capabilities for improved planning and scheduling optimization.
ORITAMES APS Scheduler v2.5 will be exhibited at Hannover Messe 2019 (Hall 5, Stand F33/1) from April 1-5, 2019.
ORITAMES is ideally suited to a wide variety of industries including discrete manufacturing, construction and infrastructure projects, logistics and transportation, equipment and property maintenance, engineering projects and service organizations.
Artificial Intelligence (AI) has recently become a trendy topic. For most people, this is mainly related to autonomous systems, robotic assistants or Big Data for Sales & Marketing. But there are also great expectations regarding applications of AI in the workplace and even in the work floor. McKinsey estimates the economic impact of AI in Supply Chain at around 1.4 trillion USD. This White Paper discusses how the ORITAMES APS Scheduler uses AI to go beyond the SOTA of legacy APS systems.
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.
Most APS systems developed in the 1980’s and 1990’s are based on dispatching or sequencing rules and simulations. The most sophisticated packages offer the possibility to use dynamic rules or even user defined configurable rules. Their popularity was not a surprise considering the limited computing resources of their time. But such rule based systems also have serious drawbacks and many implementations have led to disappointing results.