Parameter design for quality improvement using Taguchi method and application examples | newji
製造業の見積・発注クラウド

その単価は妥当か。
AI が根拠付きで分析。

相見積の比較も発注も進捗管理も、ひとつの画面に。

サービス資料をダウンロードPDF・無料/1分で受け取れます

投稿日:2025年7月27日

Parameter design for quality improvement using Taguchi method and application examples

Understanding the Taguchi Method

💡 こうした調達・受発注の属人化、Newji one なら「ひとつの画面」で解決。見積依頼から発注・進捗・承認までAIが下支えします。
サービス資料を見る(無料)→

The Taguchi Method is a statistical approach designed for improving the quality of manufactured goods.
It is named after Dr. Genichi Taguchi, a Japanese engineer who developed the method in the mid-20th century.
The primary goal of the Taguchi Method is to determine the best set of conditions for a product or process to ensure quality and minimize variability.
This is achieved through robust design, which focuses on making the product or process less sensitive to variations in external conditions.

Basics of Parameter Design

Parameter design is a crucial aspect of the Taguchi Method.
It involves identifying the key factors that impact the quality of a product and determining the optimal settings for these factors.
This requires systematic experimentation and the use of orthogonal arrays, which help in the efficient study of parameter effects on performance.
The key is to identify the control factors that can be manipulated to minimize the effect of noise factors, which are uncontrollable variations that can affect the outcome.

Steps Involved in Parameter Design

1. Define the Problem

The first step in parameter design is to clearly define the problem.
This includes understanding the product or process and identifying what aspect needs improvement.
In this step, the objective is to set clear goals for the quality improvement project.

2. Identify Control and Noise Factors

Next, identify the factors that can be controlled (control factors) and those that cannot (noise factors).
Control factors can be adjusted during the manufacturing process, while noise factors are typically environmental variables such as temperature or humidity.
Understanding these factors is crucial in determining how they affect quality.

3. Develop an Experimental Plan

After identifying the factors, develop an experimental plan that makes use of orthogonal arrays.
These arrays are structured to allow the study of multiple factors simultaneously with minimal experimental trials.
This is done to efficiently evaluate the impact of different settings for each factor.

4. Conduct the Experiments

With the experimental plan in place, conduct the experiments as outlined.
This involves varying the control factors according to the plan and observing the results.
Record the data meticulously for analysis.

5. Analyze the Results

Once the experiments are complete, analyze the results to identify which combination of factors results in the best product quality.
Taguchi’s method uses signal-to-noise (S/N) ratio as a measure of how well the product performs under varying conditions.
The higher the S/N ratio, the better the product’s performance.

6. Implement and Monitor

The final step is to implement the optimal factor settings in the production process.
It is important to continually monitor the results to ensure the quality improvement is maintained and to make further adjustments if necessary.

Benefits of Using the Taguchi Method

Cost Efficiency

One of the major benefits of the Taguchi Method is cost efficiency.
By using orthogonal arrays and reducing the number of experiments, companies can save both time and resources.
This method allows for effective quality improvement without the need for extensive trial and error.

Improved Quality

By focusing on robust design, the Taguchi Method ensures that products and processes are less affected by variations.
This leads to improved reliability and consistent quality, which increases customer satisfaction.

Ease of Application

The Taguchi Method is straightforward and can be applied to various industries.
It provides a clear framework for quality improvement, making it accessible even for those with minimal statistical background.

Real-World Applications

Automotive Industry

In the automotive industry, the Taguchi Method is used to optimize engine performance and fuel efficiency.
By identifying the key parameters that affect engine output, manufacturers can produce vehicles that are more reliable and eco-friendly.

Electronics Manufacturing

In electronics, the method is applied to improve the durability and performance of components such as semiconductors.
This ensures that electronic devices are more resistant to temperature fluctuations and handling stresses.

Food and Beverage Industry

Food and beverage producers leverage the Taguchi Method to enhance product taste and shelf-life.
By optimizing processing conditions, they can deliver consistently high-quality products to consumers.

Conclusion

The Taguchi Method is a powerful tool for quality improvement across various industries.
By focusing on parameter design, it enables businesses to produce better products with fewer resources.
Its structured approach to experimentation and analysis provides significant benefits, including cost savings, improved quality, and ease of application.
For companies aiming to maintain competitive advantage, the Taguchi Method offers a strategic pathway to success.

WHITE PAPER

この記事の理解を深める
無料ホワイトペーパーをプレゼント

製造業の現場で使える実務資料(PDF)を無料でお届けします。"こんな資料が届きます" ↓ 下のボタンからどうぞ。

FREE DOCUMENT — サービス資料(PDF・無料)

製造業の見積・受発注クラウド
「Newji one」とは

Newji one は、製造業の調達・受発注に特化したクラウド/AIエージェント。見積依頼・発注書作成・進捗管理・承認をひとつの画面に集約し、AIが比較と異常検知を担当。最後の「GO」だけ人が押す仕組みです。

  • 見積〜発注〜納期を一元管理。催促・転記のムダをゼロに
  • AIが相見積もり比較と異常検知。あなたは判断だけに集中
  • 取引先は「招待」で完全無料。自社コストだけで取引先ごとデジタル化

※ 取引先から招待された企業様は完全無料でご利用いただけます

NEWJI総研

購買・調達や設計・品質の実務を、
研修テキストと実務書式にまとめています。
無料サンプルで中身を確かめられます。

NEWJI総研の資料を見る

OEM/ODM 生産委託

アイデアはある。作れる工場が見つからない。
試作1個から量産まで、加工条件に合わせて最適提案します。
短納期・高精度案件もご相談ください。

加工可否を相談する

AI/DX支援

見積・発注、紙・FAX、品質記録など、
人に頼って回っている業務を、AIと仕組みで回る形に。
まずは無料でご相談ください。

AI/DX支援を見る

見積・発注クラウド Newji one

受発注が増えるほど、入力・確認・催促が重くなる。
受発注管理を“仕組み化“して、ミスと工数を削減しませんか。
見積・発注・納期まで一元管理できます。

機能を確認する

You cannot copy content of this page