In the era of the Internet of Things (IoT) and Industry 4.0, productivity and operational efficiency are conditioned on technology. Their goal involves, beyond survival, innovative processes that ensure optimal system performance and more accurate decision-making, through the use and advanced analysis of data from the shop floor to the administrative sector of the business.
So-called deep learning amplifies the concept of Big Data combined with machine learning. It provides advanced analysis tools through the use of algorithms, neural networks, applications, and platforms that will completely revolutionize production, monitoring, and quality maintenance processes. All of this to transform the business models we know today.
Want to discover the paths to productivity and operational efficiency? Keep reading and check them out!
1. Theory of Constraints (TOC)
The Theory of Constraints (TOC) is a practical application of organizational decision-making that involves some type of constraint among its objectives. A constraint limits the company’s movement toward its goals, which are commonly results and sustainable growth.
Ideally, these constraints should be combated or minimized in order to achieve productivity and operational efficiency. Constraints can be:
- Physical: idle rate in machines, equipment, vehicles, facilities, systems, intellectual capital, etc.;
- Non-physical: demand unpredictability, lack of alignment with market trends, inefficiency in meeting consumer demands, etc.
They are measured through performance indicators that evaluate business profitability, operating expenses, and inventory maintenance investment. Examples include: contribution margin, Return on Investment (ROI), and productivity index.
2. Lean Manufacturing and its variants
With well-structured and managed production processes, it is possible to resolve bottlenecks in lead time, transportation, and handling, as well as reduce inventory volume and defective items. Thus, total quality and zero waste (TPM — Total Productive Maintenance) are enabled.
This structuring occurs through lean manufacturing. It guides flexible pull-type processes, where items are pulled rather than pushed at the end of the production chain.
By combining lean manufacturing with IoT technology, with significant participation from the workforce and machine learning, the deployment of the Lean Six Sigma method becomes capable of producing certain advantages, such as those we will see below.
It is worth noting that “lean” refers to lean production, while “six sigma” refers to the variability rate (standard deviation) of a process.
Demand predictability
According to McKinsey research, machine learning can reduce supply chain forecasting errors by up to 50%.
This effectiveness is driven by deep learning, where technological resources and Artificial Intelligence (AI), supported by Big Data, test mathematical models to increase the accuracy of supply and production requirements. This occurs due to both consumption profile variability and seasonality.
Automation of material procurement
This improvement process is the result of demand precision combined with automation and the recording of process consumption history for supply chain replenishment.
It is facilitated by 3D printing, which creates materials in real time and according to the demands of the production process.
Predictive maintenance
This concerns the improvement of metrics such as OEE (Overall Equipment Effectiveness), where the availability and performance of machines used in the production process are enhanced through the integration of neural networks, deep learning, and predictive maintenance.
Equipment conditions are evaluated automatically, with strictly necessary human intervention, which minimizes idle rates and maintenance and inspection costs.
3. Continuous and cyclical process monitoring for productivity and operational efficiency
The process of controlling and monitoring the application of methodologies must be cyclical, with the aid of tools such as the PDCA Cycle (Plan, Do, Check, and Act). In addition, it should aim at matching world-class levels — WCM (World Class Manufacturing).
The IoT concept, supported by deep learning, is the path to total quality. According to PwC data, machine learning and integration between APIs, Big Data, and Analytics will contribute to a 31% growth in digitized manufacturing.
There are many advantages in Industry 4.0, as technology helps companies build a connected network of devices that operate exclusively to improve work processes. From quality control to asset management, supply chain solutions, and lean manufacturing, it is possible to increase productivity and operational efficiency.
Want to better understand how productivity and operational efficiency methodologies can amplify your business results? Then subscribe to our newsletter!
