EOQ Versus JIT Explained Through Coffee Beans and Raspberries

The “Plan for Every Food” in my household involves different policies for buying coffee beans and fresh raspberries. These simple examples show that  thinking in terms of Economic Order Quantity (EOQ) isn’t always wrong, and Just-In-Time (JIT) isn’t always right.  You need to set appropriate policies for screws, steel bars, engines, microchips, and all other items you may need, and review these policies periodically as circumstances change.

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Change your production leveling strategy to achieve flow | Ian Glenday | Planet Lean

“…What I came to call Repetitive and Flexible Supply (RFS) is based on the idea of manufacturing the largest products in the same sequence at the same time every week. To many people, this sounds ridiculous and stupid at first.

My analysis consistently showed that, typically, 6% of a company’s products represent 50% of the volume it produces.

I started to see this happen in every factory, hospital, or office I went to. And that’s when it hit me – why not simply focus on stabilizing the plan for that 6% of the products?…”

Source: planet-lean.com

Michel Baudin‘s comments:

Ian Glenday’s idea of RFS is fine, but not quite as original as presented in the article. Making it easy to do what you do the most often is the motivation behind the Product-Quantity (P-Q) analysis I learned in Japan in the 1980s.

To use the terminology introduced  in the UK by Lucas Industries about that time, it breaks the product mix into Runners, Repeaters, and Strangers. You make each Runner is an dedicated production line, because it has a volume that justifies it.

Then you group Repeaters in families and make them in flexible lines, and you keep a residual job-shop to make the Strangers — the long tail of your demand — products in large numbers but with low and sporadic demand.

This method is described, as prior work, in Lean Assembly as a foundation for assembly line design, and in Lean Logistics for warehouse/supermarket design and for production scheduling, in particular heijunka.

See on Scoop.itlean manufacturing

Just-in-time and disasters

 

See on Scoop.itlean manufacturing

Every time a natural or human-made disaster occurs, there are journalists and bloggers to see in the resulting supply chain disruption evidence that just-in-time (JIT) is wrong and should be abandoned as an objective.

This is based primarily on the perception that JIT means zero inventories. Since zero inventories means zero production, it is obvious that not all inventory is waste. What is waste is unnecessary inventory, which is a bit more subtle because it requires you to tell what is necessary from what is not. There are telltale signs, like thickness of dust or the inability of anyone to tell you what materials are for, but that is the easy part. Beyond that, you have to figure out experimentally what you really need.

What JIT really is about is protecting yourself against shortages by vigilance rather than inventory. This means keeping accurate inventory data, monitoring the in- and out-flows, monitoring the disruptions that can be anticipated, and responding quickly to events. The reason to pursue this strategy is that , while protecting yourself against shortages by inventories works with crude oil, it does not when you are dealing with thousands of items. If you try, you end up with full warehouses that happen not to contain the item you need today.

When a disaster hits your supply chain, the quick response cannot be yours alone. You need your suppliers’ help, and that is why you cannot be in adversarial relationships with them. Long-term, single-source agreements, the regular exchange of business and technical information, and collaborative problem-solving are all necessary to cement the relationships that make a joint emergency response possible.

See on blog.kinaxis.com

Steven Spear on Problem-Solving with JIT: Not Bad for an Academic Paper

Steven Spear’s The Essence of Just-in-Time:Imbedding diagnostic tests in work-systems to achieve operational excellence  is a working paper from Harvard Business School in 2002 focused on the interaction between JIT and problem-solving. It is an important topic, only briefly alluded to in Lean Logistics and covered in more detail in When to Use Statistics, One-Piece Flow, or Mistake-Proofing to Improve Quality, but there are many other improvement opportunities besides product quality, and shining a light on their relationship with JIT is useful.

Spear’s paper is worth reading because he did his homework: it is based on research that involved immersion in a Toyota supplier support team, visits to seven Toyota plants and 12 suppliers in Japan and the US, and working as an assembler in a non-Toyota plant for comparison. I recommend in particular sections 4 to 7. Section 4 is a case study of mattress manufacturing at Aisin Seiki, from which the following sections draw general conclusions.

You have to look past the other sections, which mainly reflect Spear’s membership in the academia tribe. His research is described as an “ethnographic study,” which conjures up the image of an American or European spending 15 years among the Guaranis of Paraguay recording what they are willing to share of their language and culture. That this vocabulary should be used in a study of Lean reflects how alien the world of manufacturing is to academia.

As an academic, Spear is obligated to reference other academics, but not non-members of the tribe, no matter how major their contributions. For example, the only Japanese author in the bibliography is Takahiro Fujimoto, from the  University of Tokyo, but neither  Taiichi Ohno nor Shigeo Shingo appear. Section 3, on Methods, opens with “Many scholars argue…” With all due respect, the arguments of scholars don’t amount to a hill of beans in Manufacturing, because, unlike Computer Science or Biology, it is not a field to which they have contributed much. From Taylor and Gastev to Ohno and Shingo, the key innovators in Manufacturing have almost all been self-taught, Lillian Gilbreth being the exception with a PhD. Why was Spear’s research not done in an Industrial Engineering department, where its content would normally place it? As I found in my own ethnographic studies of academia, the need for grants pushes researchers in other directions, like genetic algorithms.