Lean Manufacturing

The manufacturing industry is as broad as it gets, it consists of 5 different types of processes, spans dozens of verticals, and involves various methods,  philosophies, and approaches.

But all manufacturers have one common challenge they need to overcome, which is the problem of waste.

The adoption of a waste minimization mind state is what’s called lean manufacturing, and is defined as a systematic method for waste minimization.

Lean manufacturing categorizes waste to seven different categories and was originally derived from the Toyota Production Systems (TPS) in 1990. What it means is that anything that doesn’t add value (while not sacrificing productivity), is considered waste.

The 7 wastes in lean manufacturing

While lean manufacturing categorizes 7 different types of waste, it seems there is one more type of waste, “the hidden waste”, which I will discuss later in this post.

But first, let’s understand what the 7 wastes are.

The 7 waste in lean manufacturing:

  1. Transporting – moving materials from one position to another. The transport adds zero value, and there’s definitely room to minimize the costs.
  2. Waiting – one of the more serious wastes in manufacturing, simply put, waiting for goods to move or be processed.
  3. Inappropriate Processing – often, organizations use high precision equipment, which can easily be replaced by much simpler tools. And by investing in smaller, more efficient tools, this waste can be eliminated.
  4. Defects – defects eventually affect quality, which leads to loss of money, either because a product is sold for less, or not sold at all. It’s not enough to detect these afterward as this will also result in extra time and money, but rather prevent it from happening in the first place.
  5. Overproduction – this relates to when there are more produced products than demand for them. Overproduction can also lead to other wastes, such as waiting, inventory, resources and more.
  6. Unnecessary inventory – unsold products lead to extra inventory that organizations are “stuck” with, which need to be stored and take up space and transportation.
  7. Over Processing – when inappropriate techniques are used, or the wrong equipment, processes that are not required are performed, which cost time and money.

The 8th ”hidden waste” in lean manufacturing

Though the list of opportunities and potential to minimize waste in manufacturing seems comprehensive  – there is one more type of waste we see many process manufacturers dealing with, with huge potential to minimize:

  1. Process Inefficiencies – process inefficiencies are different “disturbances” in the production line that can affect quality and yield.

For example, in the chemical manufacturing industry, such process inefficiencies can be:

  • Formation of undesired side products that affect the product purity- when 2 or more reactions occur simultaneously
  • Incomplete reactions – which damage yield and quality of the finished product
  • Losses during separation – of the desired product from the reaction mixture
  • Process instability – due to blocked assets, leakages, and other asset faults
  • Losses during purification – due to the transfer of material from reaction vessels
  • And more.

The bad news is that these process inefficiencies come on account of meeting production goals, such as increasing product purity, preventing asset failures, increasing throughput, and much much more, but most importantly – reducing waste.

But the good news is, there is a way to minimize them. With the rise of Industrial Artificial Intelligence (AI), more and more manufacturers are leveraging process-based machine learning solutions to predict and prevent process inefficiencies.

Predicting and preventing process inefficiencies with Industrial AI

When it comes to AI, it’s important to understand the difference between traditional AI vs. process-based AI.

While traditional AI looks at raw data from production lines (OT data) and applies machine learning to it (causing many false-positives), process-based AI contextualizes the data by adding business data from IT systems into datasets — together with the specific production process flow context — and builds a process-based data model.

It then applies process-based machine learning algorithms, which are able to clear the noise and pinpoint actionable insights.

What this means, is that by implementing process-based machine learning – we can now understand 3 important insights:

  1. Why process inefficiencies happen
  2. When they will happen, and
  3. How to avoid them from happening again

The end result? Improved yield and quality, which leads to reduced waste.

There’s no question regarding waste being a strategic issue for manufacturers and the importance of understanding how to combat waste in the most cost-effective way.

Manufacturers, and process manufacturers in particular need to address the 8th waste of Process Inefficiencies as a core cause of waste. This requires the implementation of machine learning with its predictive analytics capabilities.