OK, I'll explain in simplest way possible...

Author : Azrul Faizal b. Mohamad Dahalal

AIAG dan VDA dah berdamai

(Bridging the Gap Between AIAG and VDA: A Truly Unified SPC Standard)

Kita yang kerja kilang especially gang-gang yg handle SPC ni, ada satu problem macam bawah ni kan...?

Kita tak betul-betul faham SPC ni sebenarnya, AIAG mentioned macam-macam dalam blue book tu yg kita pun tak faham, German-based customer yang guna VDA as reference pulak ada approach style lain.

Korang yang kerja kat kilang ni pulak kena ikut dua-dua.

QA Engineer lah yang kena hadap problem ni biasanya...diorang lah yang kena explain dekat customer, auditor, management, dan kadang-kadang dekat diri sendiri sekali sebab diorang sendiri pun bukan nya paham. :-)

To be fair lah, AIAG dan VDA sebenarnya nak benda yang sama je, which are better quality, more stable processes, dan fewer defects. Cuma cara cakap, terminology, methodology, dan expectation tu kadang-kadang macam ntah apa-apa.

So, SPC Volume yang baru ni eliminate problem-problem macam ni lah. Actually bagus lah jugak untuk korang.

This new manual is jointly developed by AIAG and VDA, introduces a harmonized SPC framework that both sides can agree on. Supplier sekarang boleh rujuk kepada satu common framework, instead of trying to become fluent in two different SPC “dialects.”

The expectation with this new SPC would be:-

• Less confusion.

• Less debating during audits.

• Less explaining to customers why the numbers look different.

• Less headache for quality engineers.

Most importantly, companies can spend less time interpreting requirements and more time improving processes which, let’s be honest, was the whole point of SPC from the beginning.

Jangan syok sendiri assume semua data normal

(Improved Treatment of Non-Normal Process Data)

Let’s be honest lah, not all manufacturing data behaves nicely and follows a perfect normal distribution.

But for many years, bila nampak data, terus assume normal distribution.

• Coating thickness? Normal!

• Torque readings? pun Normal!!

• Surface roughness? Normal jugak!!!

• Chemical concentration? ini pun Normal jugak!!!!

Kadang-kadang rasa macam semua benda dalam kilang ni normal je.

The reality is that many manufacturing processes naturally produce skewed or non-normal data. Yet, these datasets were often analyzed using normal-distribution assumptions, which sometimes led to conclusions that were, let’s just say… a bit optimistic.

Pernah kan kita tengok report capability cantik-cantik, Cpk tinggi, semua orang senyum. Lepas tu next week dapat customer complaint. Ambikkk…

The updated SPC Manual puts much greater emphasis on understanding the actual distribution of the data before jumping into capability analysis.

In other words, sebelum terus calculate Cpk dan celebrate macam champion, cuba tengok data tu betul-betul. Data tu normal ke tidak? Ada skew ke? Ada pattern pelik ke?

Sebab kadang-kadang masalahnya bukan pada proses.

Masalahnya kita syok sendiri assume data itu normal. :-)

So, in this new SPC, they have key guidance that includes:

• Evaluating the shape and characteristics of process distributions

• Applying appropriate transformation methods where necessary

• Selecting suitable capability assessment techniques for non-normal data

  
  

By promoting a more accurate statistical approach, organizations can better identify true process risks, avoid incorrect capability conclusions, and improve confidence in customer reporting and audit outcomes.

Topik SPC yang buat meeting dragged lagi 30 Minit

(Capability vs. Performance: Bringing Greater Precision to SPC Analysis)

One of the most common SPC topics that can make a meeting drag for another 30 minutes is this question:

“Cp/Cpk ke? Atau Pp/Ppk?”

Tanya quality engineer, dapat satu explanation.

Tanya customer... lagi laaa, baik tak payah tanya.

and auditor will definitely act as they are the most ever knowing creatures.

Sometimes everybody is looking at the exact same data, but somehow each person sees something different and is convinced they’re right. Then we'll here something like below:-

“Customer saya dulu suruh guna Ppk.”

“Eh, auditor saya selalu nak tengok Cpk.”

“Dari dulu kita buat macam ni je lah.”

“Trainer kitorang yang before ni cakap...”

The 2026 AIAG & VDA SPC Manual attempts to put an end to this long-running debate by introducing a more structured approach to evaluating process performance. The manual acknowledges that not every process is in the same situation, so the analysis should depend on the actual stage and condition of the process.

In other words, tak boleh lagi main hentam guna capability index yang sama untuk semua keadaan dan harap auditor tak tanya banyak soalan.

The new guidance clearly separates the analysis into three distinct categories:

• Machine Performance – focused on short-term equipment variation under controlled conditions.

• Preliminary Process Performance – used during early production when process stability has not yet been fully demonstrated.

• Long-Term Process Capability – intended for mature and statistically stable production processes.

  
  

For each situation, the manual identifies the appropriate metrics, including Cp/Cpk, Pp/Ppk, and Pm/Pmk, together with guidance on when each should be applied. This structured approach reduces ambiguity, improves consistency in customer reporting, and supports more meaningful improvement objectives. For quality practitioners, the result is a much clearer roadmap for process evaluation.

Korang buat X-Bar & R macam "Cheese Leleh"

(A Broader Range of Control Charts for Modern Manufacturing.)

Kat Malaysia ni, semua benda kalau boleh nak bubuh cheese, nak nampak lagi mengiurkan kita letak sampai meleleh-leleh. Pisang goreng cheese, popia cheese, kopok lekor cheese... dalam SPC pun sama, semua benda nak bedal guna X̄-R Chart. Sekarang dah TAK BOLEHHHH.

OK, yang ini korang kena baca dengan tenang, jangan gelabah tak pasal-pasal.

X̄-R Chart dan Individuals-Moving Range (I-MR) Chart masih lagi antara “otai” dalam dunia SPC ni. Dari dulu sampai sekarang, chart-chart ni banyak berjasa.

Tapi macam telefon Nokia dulu lah.

Masih boleh guna? Boleh.

Masih reliable? Memang.

Tapi kalau nak compare dengan smartphone zaman sekarang, ada beberapa benda yang Nokia memang tak boleh buat.

Sama juga dengan SPC.

Dalam manufacturing environment hari ini, proses semakin kompleks, tolerance semakin ketat, dan customer expectation pula semakin tinggi. Kadang-kadang kalau nak harap control charts yang sedia ada tak cukup sensitif untuk detect small changes before it gets worst.

Bukan sebab chart lama tak bagus.

Cuma sekarang ada situasi tertentu yang memerlukan tools yang lebih specialized dan lebih “pandai membaca” perangai proses kita.

So by recognizing this need, the new SPC manual significantly expands the range of recommended control chart methodologies. Additional tools now include:

• CUSUM (Cumulative Sum) Charts

• EWMA (Exponentially Weighted Moving Average) Charts

• Control Charts for Non-Normal Data

• Tolerance-Based Control Charts

  
  

More importantly, the manual doesn’t just tell you, “Go use a control chart lah.” It actually provides practical guidance on how to choose the right chart based on the process, the data behaviour, and what exactly you’re trying to monitor.

Satu benda yang korang kena admit, not every problem can be solved by simply throwing an X-bar & R chart at it and hoping for the best.

The focus now shifts from “Asalkan ada SPC chart... jalan” to “Eh, are we using the correct SPC chart or not?”

Different processes behave differently, so naturally different situations may require different monitoring approaches.

For organizations operating high-precision or highly regulated processes, early detection of subtle process shifts can prevent defects, reduce waste, and avoid costly customer complaints. Having access to a broader SPC toolkit strengthens the ability to identify problems before they affect product quality.

Jangan "Pulau" kan SPC lagi

(SPC as Part of an Integrated Quality System)

One thing that many people will probably appreciate in the new manual is how SPC is now much better connected with the other AIAG Core Tools.

Sebab kalau nak cerita jujur lah, dalam banyak organization, SPC, FMEA, Control Plan, dan MSA kadang-kadang macam jiran sebelah rumah.

Duduk sebelah-sebelah, nampak muka hari-hari. Tapi tak pernah bertegur pun.

• FMEA sibuk cerita pasal risk.

• Control Plan sibuk cerita apa yang nak dikawal.

• MSA pulak terlampau technical yang sampai ke sudah takde siapa paham.

• SPC tak habis-habis plot chart sorang-sorang kat hujung prod floor. Kita tengok pun kadang-kadang rasa kesian.

When the above happened, the outcomes do not reflect the actual process behaviour. Therefore, this new SPC manual tries to bring everybody back to the same table.

Data daripada SPC sepatutnya support apa yang kikta dah identify dalam FMEA, pastu kena allign dengan apa yang ditetapkan dalam Control Plan, dan mesti datang daripada measurement system yang telah dibuktikan boleh dipercayai.

Kalau tidak, FMEA cakap kiri, Control Plan cakap kanan, SPC tunjuk atas, MSA tunjuk bawah. Last-last quality engineer jugak yang berkerut-kerut kening kena explain dalam audit.

Basically, the new manual reinforces the relationship between SPC and:-

• Control Plans – ensuring monitoring activities support documented process controls.

• FMEA – linking process risks and special characteristics to ongoing statistical monitoring.

• Measurement System Analysis (MSA) – confirming that measurement data used for SPC is capable and reliable.

• Special Characteristics Management – providing clearer guidance on monitoring critical process and product parameters.

This integrated approach positions SPC as more than a quality reporting tool. Instead, it becomes an active component of risk management, process control, and continuous improvement across the manufacturing system.

Mamak pun dah pakai robot hantar makanan korang tau!!!

(Supporting the Digital Manufacturing Environment)

The 2026 edition is also the first SPC manual to formally acknowledge the growing role of digital technologies in quality management.

As manufacturers increasingly adopt Industry 4.0 practices, SPC systems are becoming more automated, interconnected, and data-driven. To support this evolution, the manual introduces guidance on:

• Validation of SPC software applications

• Automated data collection and acquisition systems

• Electronic record management and traceability

• Real-time monitoring, notifications, and response mechanisms

Sekarang banyak kilang dah moves towards automated data collection, real-time monitoring, dashboard, cloud system, dan macam-macam lagi yang bunyi tersangat lah Industry 4.0.

The updated manual acknowledges this reality and provides practical guidance on how SPC can operate effectively in a more connected and automated environment.

Tak nak dah guna SPC yang hidup dalam Excel.

Sekarang SPC dah kena berkawan dengan MES, ERP, database, software dashboard, dan macam-macam sistem lain.

For companies pursuing smart manufacturing initiatives, the updated manual offers a valuable framework for integrating SPC into automated and connected production systems.

Pendek kata, SPC pun dah masuk era digital.

Takkan telefon orang lain dah AI-powered, korang still nak guna Nokia, so SPC korang pun takkan still kena tunggu orang print chart pastu staple dekat notice board...kan?