Data Analytics techniques are based on algorithms, mathematical models, and artificial intelligence methods. These tools permit to automatically collect data, explore the associated information, and get insights that could be unnoticeable but useful for maximizing the efficiency of systems and businesses. The results typically bring to fixed and variable costs reduction thanks to the wider visibility on operations in progress, the greater control on processes, and the improved organization of activities.
Overall, there are different types of Data Analytics:
- descriptive analytics is limited to identifying and characterizing events that occurred in the past;
- diagnostic analytics focuses on examining the causes and motivations of happened facts;
- predictive analytics has the objective to predict future circumstances;
- prescriptive analytics suggests actions to be taken in the short, medium, or long term.
Real-time and historical data from sensors, machinery, robots, and automatic devices are of enormous value if used in a clever way.
Predictive maintenance methods, thanks to the application of advanced artificial intelligence techniques, allow to deeply explore data and to discover anomalous patterns revealing the possibility of future failures and criticalities.
Anticipating problems before they happen helps to reduce the costs of excessively rigorous preventive maintenance interventions and at the same time to increase the system reliability, avoiding breakages and unplanned downtimes.
Having a look at what is going to happen is essential in any business.
When historical information is available, it becomes possible to investigate data to identify trends, cyclical fluctuations, and characterizing patterns
Predictive Analysis methods such as time-series analysis and machine learning algorithms enable leveraging data to forecast the future demand or to determine the expected load of systems. This improves the capability to plan ahead, make informed decisions, and reduce operational costs, allowing an efficient employment of resources under variability conditions.
The combination of historical inputs with hints about incoming events or probable facts makes the forecasting engines even more powerful, giving the algorithms the ability to accurately predict peaks of sales, loads, people flows, etc…
Planning & Scheduling
Every production system from chemical to food & beverage, from automotive to electronic, from textile to furniture, faces three important interrelated and recurring questions: How much to produce? When? With what resources?
In the easiest cases, the answers can be found with simple reasonings on the available capacities. However, there are many production environments that are complex by nature, involving different production areas, several critical resources (also human resources, which require specific considerations), tight time constraints, high productivity targets, etc…
When the number of possible choices grows, decision-making becomes increasingly difficult and far from performance maximization. By adopting optimization algorithms based on the operation research or relying on comprehensive simulation models it is possible to translate reality into mathematical terms, include custom requirements and goals, and obtain planning & scheduling results that guarantee efficiency, optimal resource utilization, and resilience to unexpected events.
Dynamic Quality Inspection
Quality control is expensive. Some of the cost items to typically take into account are manpower, technical equipment, wastes due to destructive testing, etc...
■ Efficient Statistical Quality Control helps to reduce the number of samples to check and the examination frequency, without losing conformance to specifications.
■ . Artificial Intelligence -based Inspection & Defects Detection allows to identify small imperfections and to automate the quality control process, making it cheaper, faster, and reliable at the same time.
■ . Artificial Intelligence -based Visual Classification enables machines to autonomously distinguish between different items, characteristics, and quality levels. This permits the automatic sorting of goods towards diverse processing steps.
Whether they are pallets, packages, or pieces, whether they are raw materials, semi-finished, or final products, the handling of goods within distribution or production contexts may require the adoption of advanced logics capable of controlling the movements, coordinating parallel processes, and synchronizing different flows.
Optimizing logistics & intralogistics operations constitutes a big opportunity for supply chain players, from producers to retailers and e-commerce companies.
Mathematical modeling of processes such as picking, sorting, and mission dispatching for automatic guided vehicles (AGVs) enables superior management of good flows and significant costs reduction.
There are many questions that may arise during the design of a system, first of all those related to throughput and performance. When it comes to complex automated or partially-automated environments, it becomes even more important to find the right answers due to the high investment costs and the originated lock-in effects.
Having the certainty that the purchased technology guarantees the right productivity even in case of high flows variability and the correct flexibility to respond to evolving conditions is crucial.
Data-driven methods ranging from operation research to simulation sono gli strumenti più adatti per provare matematicamente l’adeguatezza di un sistema. Infatti, permettono il confronto tra possibili alternative, l’analisi in caso di carichi di picco e la valutazione delle prestazioni in condizioni di incertezza o rischio.
Simulation-based design strongly supports decision makers, providing richer insights and enabling a better understanding of critical issues to solve. It helps to devise new solutions and to flexibly test modifications to layouts, dimensionings, processes, control logics, etc...