Home BUSINESS Kanban System Management And Setup

Kanban System Management And Setup

Kanban In Just In Time Production

In practice, the Just in Time acronym can be translated as producing the necessary piece in quantity and at the necessary time. The very ambitious goals are:

  • The almost total cancellation of stocks between each working phase,
  • Improvement of product quality,
  • The recovery of flexibility about the demand trend.

The just-in-time production system provides that only the last production stage knows the customer’s requests. All the upstream phases are then activated from it, which always and only produces specific requests from the downstream phase. Each phase must follow the requests of its “customer,” i.e., the next phase. The tool that governs this production system is Kanban. Kanban means “visual signal” and is a visual management tool that governs the exchange of information between processes, which controls sequences and quantities to be produced in each work phase. Kanban can be used to manage different types of processes:

  • Production: production Kanbans are used, which allow the upstream process to produce 1 (single Kanban) or more (batch Kanban) containers of the item managed with this methodology.
  • Handling: picking Kanbans are used, which govern the movement of material toward a production process.
  • Purchase: purchase Kanbans are used, which represent the purchase order of a container of the code indicated on it.

The Kanban is typically a tag that accompanies the containers with the products and reports a series of information that cannot be missing:

  • The product name
  • Its identification code
  • Its storage location
  • The capacity of the container
  • The description of the upstream operation
  • The description of the downstream operation

Also Read: The 5 Main Goals Of Data Lifecycle Management

Description Of A Two Kanban Just-In-Time Production System

Suppose we consider a processing department “N” with small stocks of raw materials and finished products, respectively upstream and downstream, contained in containers, each with a specific type of kanban present. (withdrawal kanban for upstream containers, production kanban for downstream containers). Department “N” receives an order to deliver material from the next station. He must therefore deliver part of the material he has processed, stored in the containers of the already processed products (containers P). The production kanban is detached from the container and placed on the department bulletin board.

It represents the production order. The department operators need raw materials to be processed and therefore take the raw materials from the small upstream inter-operational warehouse (containers C). The withdrawn products, in the pre-established quantity, are processed. At this point, the withdrawal kanban on container C assumes the meaning of the order to withdraw finished products to the department “N-1”. In contrast, the production kanban on the notice board is inserted into the container of finished processing products. The level of inventories downstream of the process has returned to equilibrium.

The withdrawal kanban is entered on the container taken from the previous department, and together with the container, it is stored in the area immediately upstream of processing. Here too, the balance has been restored. Now in a cascade, the more upstream departments are not in equilibrium. The process described above is iterated similarly up to the boundaries of the company’s internal processes and from there to the suppliers with the supplier kanban, always following the same logic.

A crucial issue when designing a just-in-time production system concerns the kanbans and their number in particular. It is a question of correctly sizing their number to minimize the trade-off between costs related to storage and possible production failures. The model now presented carries out the sizing of the number of kanbans according to the average consumption of the pieces to be machined, calculated on the history, the coverage time, i.e., the time in which the machine remains fed with only the pieces present in the containers, and the addition of safety stock, essential to cover any demand increases. Data necessary for calculating the number of kanbans relating to the article,

  • M = average daily consumption of pieces (pieces/time)
  • T = coverage time (time)
  • SS = safety stock, expressed in percentage terms
  • Q = number of pieces present in a container (pieces/container). Formula: N= [ M x T x (1 + SS) ] Q

NB: M and T must have the same unit of time measurement (hours, shift, day, week). The brackets used require the result obtained to be rounded up. Let’s see an example. Let’s assume that the average consumption of the pieces produced by a work center is 124 pieces/day. Let us also assume that the cover time equals one hour and a working day comprises 8 hours. A value of 50% is set as the security level. Each container can fit five pieces/containers. Calculate the number of kanbans needed. First, it is necessary to standardize the time measurement unit between average consumption and coverage time: 1 hour of coverage time is equivalent to 0.125 days (divided by 8, i.e., by the length of the working day). Formula for following: [124 x 0.125 x (1 + 0.5)] = 5. Therefore, five kanban containers are needed.

Also Read: Management Platform: Understand The Main Impacts On Your Business

Tech Cults
Tech Cults is a global technology news platform that provides the trending updates related to the upcoming technology trends, latest business strategies, trending gadgets in the market, latest marketing strategies, telecom sectors, and many other categories.


Resource Management: 7 Best Practices For Your Project

Resource management is strategic not only for the success of projects but also for the health and well-being of team members. Wait For Resources To...

The Benefits of Keeping Your Old Phone

When your two year mobile phone contract comes to an end, you might find yourself considering an upgrade to the latest model. However, there...

Cultivating Leadership Excellence in the Corporate World

In an era where business dynamics shift with dizzying speed, the difference between success and faltering often hinges on leadership. Good leaders possess an...

API Monitoring to Improve ML Models

Introduction Generative AI and Machine Learning models have exploded in recent times, and organizations and businesses have become part of the new AI race. The...

Data Analytics: Six Trends That Will Shape The Future

Quick advances in information science are opening up additional opportunities for organizations. They can extend their insight into their market, their clients and their...

Buying Instagram Likes: Strategies, Upsides, and More…

Hey everyone! People who have used Instagram for a while know how important it is to get likes. They're "thumbs up" that lets you...