Lean manufacturing tends to reduce waste and make the production process more cost-effective and efficient as well. The primary focus is to streamline production operations while enhancing customer experience. To make this possible, lean manufacturing optimises production operations by emphasizing customer needs. Overall, lean manufacturing is about terminating things that do not add value, therefore adhering to delivering the product based on the needs of the customers and fulfilling the expectations, says Ritesh Sutaria, co-founder of Prompt Softech. Using a low-code platform with manufacturing can support manufacturers in solving several issues.

When dealing with code, we often talk about clean and reusable code. Python is an Object-Oriented Programming (OOP) language and it's great for reusing the code because, for example, we can create our own functions and invoke them. But can we follow a methodology to write clean and reusable code? In this article, we'll see the "5S" methodology which can be useful to create clean and reusable code, even in our Data Science projects. The "5S" methodology comes from the so-called "Lean Manufacturing" or "Toyota Production System" (TPS), as it's been developed at Toyota Motor.

Artificial intelligence is widely acknowledged as a crucial aspect of what is broadly referred to as Industry 4.0. While no one knows yet how artificial intelligence will be incorporated into the next phase of the Industrial Revolution, most agree that it will allow greater connectivity between people, machines, and information technology, allowing manufacturers to better optimize processes and predict problems. How are small and medium-sized manufacturers (SMMs), who typically do not have the time or capital it would take to test emerging technologies, supposed to evaluate how artificial intelligence could impact their organization -- and play a role in preparing them for Industry 4.0? Waiting for the manufacturing sector to decide, so to speak, is certainly not an option. A delay of one, two, or five years could cause a manufacturer to be left behind. The time to act is now, but the path forward isn't clear.

Headlines today are littered with references to'the fourth industrial revolution', 'artificial intelligence', 'machine learning' and'big data'. While this hype isn't unfounded, the practical ways of achieving value often remain unclear to the industry. The bottom line is a primary concern for many manufacturers. Not without reason; many associate the fourth industrial revolution with further expenditure - instalment of new sensors to collect data, investment in data storage or perhaps in 3D printing equipment – rather than cost savings. Fortunately, the reality doesn't always need not be this expensive.