How to build a data architecture to drive innovation—today and tomorrow – 19 – By space dreamer M.Temel Aygün

Door to science via art – Küçükköy- Ayvalık – Balıkesir – Turkey

How to build a data architecture  to drive innovation—today and tomorrow

Yesterday’s data architecture can’t meet today’s need for speed, flexibility, and innovation. The key to a successful upgrade—and significant potential rewards—is agility.

Over the past several years, organizations have had to move quickly to deploy new data technologies alongside legacy infrastructure to drive market-driven innovations such as personalized offers, real-time alerts, and predictive maintenance.

However, these technical additions—from data lakes to customer analytics platforms to stream processing—have increased the complexity of data architectures enormously, often significantly hampering an organization’s ongoing ability to deliver new capabilities, maintain existing infrastructures, and ensure the integrity of artificial intelligence (AI) models.

Current market dynamics don’t allow for such slowdowns. Leaders such as Amazon and Google have been making use of technological innovations in AI to upend traditional business models, requiring laggards to reimagine aspects of their own business to keep up. Cloud providers have launched cutting-edge offerings, such as serverless data platforms that can be deployed instantly, enabling adopters to enjoy a faster time to market and greater agility. Analytics users are demanding more seamless tools, such as automated model-deployment platforms, so they can more quickly make use of new models. Many organizations have adopted application programming interfaces (APIs) to expose data from disparate systems to their data lakes and rapidly integrate insights directly into front-end applications. Now, as companies navigate the unprecedented humanitarian crisis caused by the COVID-19 pandemic and prepare for the next normal, the need for flexibility and speed has only amplified.

For companies to build a competitive edge—or even to maintain parity, they will need a new approach to defining, implementing, and integrating their data stacks, leveraging both cloud (beyond infrastructure as a service) and new concepts and components.

Six shifts to create a game-changing data architecture

We have observed six foundational shifts companies are making to their data-architecture blueprints that enable more rapid delivery of new capabilities and vastly simplify existing architectural approaches. They touch nearly all data activities, including acquisition, processing, storage, analysis, and exposure. Even though organizations can implement some shifts while leaving their core technology stack intact, many require careful re-architecting of the existing data platform and infrastructure, including both legacy technologies and newer technologies previously bolted on.

Such efforts are not insignificant. Investments can often range in the tens of millions of dollars to build capabilities for basic use cases, such as automated reporting, to hundreds of millions of dollars for putting in place the architectural components for bleeding-edge capabilities, such as real-time services in order to compete with the most innovative disruptors. Therefore, it is critical for organizations to have a clear strategic plan, and data and technology leaders will need to make bold choices to prioritize those shifts that will most directly impact business goals and to invest in the right level of architecture sophistication. As a result, data-architecture blueprints often look very different from one company to another.

When done right, the return on investment can be significant (more than $500 million annually in the case of one US bank, and 12 to 15 percent profit-margin growth in the case of one oil and gas company). We find these types of benefits can come from any number of areas: IT cost savings, productivity improvements, reduced regulatory and operational risk, and the delivery of wholly new capabilities, services, and even entire businesses.

So what key changes do organizations need to consider?

1. From on-premise to cloud-based data platforms

Cloud is probably the most disruptive driver of a radically new data-architecture approach, as it offers companies a way to rapidly scale AI tools and capabilities for competitive advantage. Major global cloud providers such as Amazon (with Amazon Web Services), Google (with the Google Cloud Platform), and Microsoft (with Microsoft Azure) have revolutionized the way organizations of all sizes source, deploy, and run data infrastructure, platforms, and applications at scale.

One utility-services company, for example, combined a cloud-based data platform with container technology, which holds microservices such as searching billing data or adding new properties to the account, to modularize application capabilities. This enabled the company to deploy new self-service capabilities to approximately 100,000 business customers in days rather than months, deliver large amounts of real-time inventory and transaction data to end users for analytics, and reduce costs by “buffering” transactions in the cloud rather than on more expensive on-premise legacy systems.

Enabling concepts and components

 — Serverless data platforms, such as Amazon S3 and Google BigQuery, allow organizations to build and operate data-centric applications with infinite scale without the hassle of installing and configuring solutions or managing workloads. Such offerings can lower the expertise required, speed deployment from several weeks to as little as a few minutes, and require virtually no operational overhead.

 — Containerized data solutions using Kubernetes (which are available via cloud providers as well as open source and can be integrated and deployed quickly) enable companies to decouple and automate deployment of additional compute power and data-storage systems. This capability is particularly valuable in ensuring that data platforms with more complicated setups, such as those required to retain data from one application session to another and those with intricate backup and recovery requirements, can scale to meet demand.

2. From batch to real-time data processing

The costs of real-time data messaging and streaming capabilities have decreased significantly, paving the way for mainstream use. These technologies enable a host of new business applications: transportation companies, for instance, can inform customers as their taxi approaches with accurate-to-the-second arrival predictions; insurance companies can analyze real-time behavioral data from smart devices to individualize rates; and manufacturers can predict infrastructure issues based on real-time sensor data.

Real-time streaming functions, such as a subscription mechanism, allow data consumers, including data marts and data-driven employees, to subscribe to “topics” so they can obtain a constant feed of the transactions they need. A common data lake typically serves as the “brain” for such services, retaining all granular transactions.

Enabling concepts and components

 — Messaging platforms such as Apache Kafka provide fully scalable, durable, and fault-tolerant publish/subscribe services that can process and store millions of messages every second for immediate or later consumption. This allows for support of real-time use cases, bypassing existing batch-based solutions, and a much lighter footprint (and cost base) than traditional enterprise messaging queues.

 — Streaming processing and analytics solutions such as Apache Kafka Streaming, Apache Flume, Apache Storm, and Apache Spark Streaming allow for direct analysis of messages in real time. This analysis can be rule based or involve advanced analytics to extract events or signals from the data. Often, analysis integrates historic data to compare patterns, which is especially vital in recommendation and prediction engines.

 — Alerting platforms such as Graphite or Splunk can trigger business actions to users, such as notifying sales representatives if they’re not meeting their daily sales targets, or integrate these actions into existing processes that may run in enterprise resource planning (ERP) or customer relationship management (CRM) systems.

3. From pre-integrated commercial solutions to modular, best-of-breed platforms

To scale applications, companies often need to push well beyond the boundaries of legacy data ecosystems from large solution vendors.

Many are now moving toward a highly modular data architecture that uses best-of-breed and, frequently, open-source components that can be replaced with new technologies as needed without affecting other parts of the data architecture.

The utility-services company mentioned earlier is transitioning to this approach to rapidly deliver new, data-heavy digital services to millions of customers and to connect cloudbased applications at scale. For example, it offers accurate daily views on customer energy consumption and real-time analytics insights comparing individual consumption with peer groups. The company set up an independent data layer that includes both commercial databases and open-source components. Data is synced with back-end systems via a proprietary enterprise service bus, and microservices hosted in containers run business logic on the data.

Enabling concepts and components

 — Data pipeline and API-based interfaces simplify integration between disparate tools and platforms by shielding data teams from the complexity of the different layers, speeding time to market, and reducing the chance of causing new problems in existing applications. These interfaces also allow for easier replacement of individual components as requirements change.

 — Analytics workbenches such as Amazon Sagemaker and Kubeflow simplify building end-to-end solutions in a highly modular architecture. Such tools can connect with a large variety of underlying databases and services and allow highly modular design.

See you in next blog with the following topics :

  • From point-to-point to decoupled data access
  • From an enterprise warehouse to domain-based architecture
  • From rigid data models toward flexible, extensible data schemas
  • How to get started


Kadıköy, İstanbul – TURKEY

Author: M. Temel AYGÜN, Ph. D. in Aerospace Eng.

Copyright belongs to author.

Dijital Düşünme Raporu 2020 – 14 – by Space Dreamer Mr.M.Temel Aygün

“Oldies 1 ” by Reha Mustecaplioglu @rmustecap

Dijital Düşünme Raporu 2020

Tüm dünya büyük bir hızla dijitalleşmeye doğru evrilirken düşünce yapımızı ve düşünme şeklimizi değiştirmek aslında her birimiz için kaçınılmaz bir gerçek.

Bu değişim ihtiyacı iş hayatında, müşterilerin güne uyum sağlayan ve neredeyse her gün değişen beklentilerini yönetmek için de karşımıza çıkıyor. Dijital düşünmeden uzaklaştığımız durumda müşterilerimizin beklentilerini karşılamaktan da uzaklaşıyoruz.

Peki, “Dijital Düşünme” ne demek? Bu sorunun cevabını verebilmek amacıyla, TING İstanbul olarak dijital düşünmeyi tanımladığımız, kriterlerini açıkladığımız, ülkemiz ve dünyada dijital düşünen şirketlerin ortak özelliklerini aktarmayı hedeflediğimiz “Dijital Düşünme Raporu”nu hazırladık.

Raporumuza katkı sağlaması amacıyla Türkiye’deki şirketlerin ve yöneticilerin ne kadar dijital düşündüklerini, dijital dönüşüme hangi mesafede durduklarını ortaya koyacak ‘Dijital Düşünme Araştırması’nı gerçekleştirdik. Online olarak düzenlediğimiz anketimize 11 farklı sektörden farklı büyüklüklerde şirketleri temsilen 102 üst ve orta düzey yönetici katıldı.

Araştırmanın ortaya koyduğu önemli bulgulardan biri, şirket yöneticilerinin en büyük sorumluluklarından ve aynı zamanda en çok mücadele ettiği zorluklardan biri dijital dönüşüm stratejilerini belirlemek. Araştırma katılımcılarının yüzde 70,3’ü şirketlerinin dijitali de kapsayan bir stratejisi olduğunu belirtirken, dijitalleşmenin ana stratejileri olduğunu belirtenlerin oranı ise yüzde 68,9 olarak karşımıza çıkıyor.

Elbette sadece strateji belirlemek yeterli değil, önemli olan bunu yaparken eş zamanlı olarak şirketin güne ayak uydurmasını sağlamak ve hatta rekabette geride kalmaması için hep birkaç adım önde gitmek…

Dijitalleşme, araştırmamız katılımcılarının yüzde 75’inin de belirttiği gibi, şirketler için çok önemli bir fırsat. Bu fırsatı değerlendirip şirketinin dijital dönüşüm sürecini başlatan şirketlerin oranı yüzde 82, ki bu Türkiye için umut verici bir gelişme.

Dijital dönüşüm, bir yönetim kararından çok daha fazlası. Belirlenen stratejilerin başarıya ulaşmasının ardında tüm şirketin aynı bakış açışına sahip olması ve inanması yatıyor. Araştırmaya katılanların yaklaşık yüzde 86’sı şirketlerinin üst yönetiminin ve yöneticilerinin dijital dönüşüme inandığını ve desteklediğini belirtirken, bu oranın çalışanlarda sadece yüzde 67 olması, şirketlerin önünde bu dönüşümün gerçekleşmesi için ele almaları gereken önemli bir gündem olduğunu ortaya koyuyor. Bunu aşabilmek için şirketlerin çalışanlarına yönelik eğitim programları düzenlemesi ve fikirlerini değiştirecek adımlar atması gerekiyor.

İnsan merakını gidermek, yenilik yaratmak ve ilerlemek için düşünüyor. Güne ayak uydurmak için de düşünme şeklini değiştirmesi gerekiyor. Acaba yeteri kadar dijital düşünebiliyor muyuz? Peki, ‘dijital düşünme’den ne anlıyoruz? Dijital dönüşümün gerekliliğine inanıyor muyuz? Dijitalleşmeden ne gibi beklentilerimiz var? Şirketimizin dijitalleşme stratejisi var mı? Varsa acaba bu strateji ne kadar uygulanıyor?

Türkiye’deki dijital düşünmenin resmini ortaya koyan, dünyadaki trendlere göre Türkiye’nin yerini görmemizi sağlayan ‘Dijital Düşünme Araştırması’na katılarak fikirlerini paylaşan tüm yöneticilere tekrar teşekkür ediyoruz. Raporun dijital dönüşüm yolculuğunda şirketlere ve siz değerli okuyuculara ışık tutmasını diliyoruz.

Düşünmenin Tarihi

Düşünmek insanı bilgiye ulaştırır

Dijital düşünmeye geçmeden önce kısaca ‘düşünmek’ nedir, tarihsel gelişimi nasıl olmuştur diye bakmakta fayda var.

Düşünmek kelimesinin sözlük anlamı: “Bir yargıya varmak ereğiyle bilgileri incelemek, karşılaştırmak ve aradaki bağlantılardan yararlanarak düşünce üretmek, zihinsel yetiler oluşturmak, muhakeme etmek”.

Düşünme kelimesinin fiil olarak ilk defa 12. yüzyılda kullanıldığı varsayılıyor.

Bu noktada bir kafa karışıklığını da ortadan kaldırmak gerekiyor. Yakın zamana kadar ‘düşünme’ ile ‘düşünce’ birbirlerinin yerine kullanılan kavramlardı. Oysa ‘düşünce’, insanın bilinçli veya tesadüfi olarak yaşadığı zihinsel bir süreç olan ‘düşünme’nin sonucudur. Yani aslolan düşünmedir.

Peki, düşünmek bize neler getiriyor? Öncelikle, insanı insan yapan düşünebilme yetisi. Bizler bu sayede geleceğimizi tasarlayabiliyor, problem çözüp karar alabiliyor ve sonuçta da bilimi oluşturabiliyoruz.

Düşünmenin kaynağı aslında meraka dayanır. İnsan hayatının her döneminde merak eder, araştırır, bulduğu sonuçlarla tatmin olup devam edebileceği gibi, kuşku duyup yeniden araştırmaya, bulduklarını doğrulatmaya ihtiyaç duyabilir. Bu da insanın sürekli bir arayış içinde olmasını ve dolayısıyla düşünmesini sağlar.

Analog Düşünme Dijital Düşünmeye Karşı

Dijital gerçekten ne demek?

Farklı bakış açılarına sahip yöneticiler bu soruya farklı cevaplar veriyor; kimine göre dijital sadece teknoloji ile ilgiliyken, bir diğerine göre değişen müşteri beklentilerini karşılamak ya da gelir maksimizasyonu anlamına geliyor. Elbette bu tanımların hiçbiri yanlış değil. Ancak unutmamak gerekir ki, farklı bakış açıları ya da farklı tanımlar, o konuda ortak hareket edilmesine hatta kimi zaman farklı departmanlarda aynı çalışmaların farklı yöntemlerle yapılarak gereksiz zaman ve para kayıplarına neden olur. Bu açıdan, her zaman şirket genelinde kabul görüp benimsenecek ortak vizyon ve tanımlar üzerinden hareket etmek hayatı kolaylaştıracak, tüm çalışan ve yöneticilerin konuyu net bir şekilde anlayıp, ona göre hareket etmesini sağlayacaktır.

Sonuç olarak, anlamlı ve sürdürülebilir olmak için, dijitalin sadece teknoloji değil, bir iş yapış biçimi olarak görülmesi gerektiğine inanıyoruz.

İnanılmaz bir hızla dijitalleşen bir dünyada yaşıyoruz ancak acaba bu dijital dünyada yeteri kadar dijital düşünebiliyor muyuz?

Değişimi anlamak, içinde yaşadığımız ortamda meydana geldiğinde zor olabilir. Bu da dijital teknolojinin hayatımızı nasıl şekillendirdiğini her zaman fark edip anlamamızı ve takdir etmemizi engelliyor. Analogdan dijitale geçiş, kullandığımız cihazlardan dünyayla etkileşim şeklimize kadar neredeyse her şeyi değiştirdi. Bilgi paylaşımını ya da günlük işlerimizi daha hızlı ve insan hatasını minimuma indirecek şekilde daha akıllı yollarla yapabilir hale geldik.

Elbette hatalar olacak ancak dijital teknolojiler sayesinde çevik düşünüp hareket edebilmemize bağlı olarak bakış açımız geliştikçe hızlı cevap verme, tepki gösterme yeteneklerimiz de her geçen gün gelişiyor.

Son yıllara kadar yani dijitalleşme bu kadar kapımıza dayanmadan önce hepimiz, alıştığımız şekilde analog düşünmeye devam ediyorduk. Mesela, bir markanın falanca ürünü iyiyse mutlaka başka bir ürünü de iyidir diye düşünmek ya da grip olmadan önce hapşırmak normal ise siz de her hapşırdığınızda grip oluyorsunuzdur mantığını yürütmek aslında tipik analog düşünme şekilleri. Bizlere öğretilen, kabul görmüş doğrulara bağlı olarak, fazlaca sorgulamadan, bize verileni olduğu gibi kabul etmek…

Oysa artık analog düşünmenin yeterli olmadığı, ezici bir ağırlığa sahip dijital bir dünyada yaşıyoruz. Bizi sayısız cihaz ile bağlantılı hale getiren ve her zaman her yerde var olan internet, gücünü dijital düşünceden alıyor. Artık iş dünyasının geleceğinin dijital dönüşümden geçtiğini hepimiz biliyoruz.

İş dünyasında müşteri beklentileri de hızla değişiyor. Salesforce’un ‘2019 State of the Connected Customer’ raporuna göre müşterilerin yüzde 73’ü şirketlerden kendi ihtiyaç ve beklentilerini anlamasını bekliyor.

Artık eskisi gibi bir siparişin eline ulaşmasını haftalarca hatta belki günlerce beklemeye hiçbir müşterinin tahammülü yok. Çünkü online sipariş verdiğimizde kesin teslimat tarihi ve hatta saati istiyoruz, siparişimizi takip edebilmeyi, dağıtım aracının hangi sokakta olduğunu bile bilmek istiyoruz. Bu tamamen dijital teknolojinin bir sonucu olarak şirketlerin standartlarını ve dolayısıyla müşteri beklentilerini yükseltmesinden kaynaklı. Bu örnekten de yola çıkarak, analog dünyada sıkışıp kalmış şirketlerin dijital dünyanın beklentilerini karşılamakta zorlanacağı ve zamanla kendini yenileme yoluna gitmezse yok olacağını söyleyebiliriz.

See you in next blog with the following topics :

  • Dijital Dünyada Analog Düşünmenin Yarattığı Tehlike
  • Konu Dijital Stratejiye Sahip Olmak Değil, İş Stratejinizi Dijitalleştirmek


Kadıköy, İstanbul – TURKEY

Yazar/ Author : M. Temel AYGÜN, Ph. D. in Aerospace Eng.

Copyright belongs to Author.


How Change Management helps you to hold course

“We live in times in which digitalization is radically changing the business landscape across industries. To remain competitive, businesses around the world  are increasingly investing in digital transformation.”

The failure rates of digital transformations remain high. These failures largely stem from the unique challenges associated with digital transformation, including shifting from the current culture to a more digital, entrepreneurial one, dealing with a lack of digital talent, working in cross- functional teams where silos have been the norm, meeting accelerated timelines due to higher customer expectations, and accepting evolving target states instead of fixed goals. What most business leaders underestimate is that digital transformation is not just about technology: Above all, it is about people. That makes it complex – thus requiring a  new approach for managing the change.

From our experience with digitalization topics, we have  identified three key imperatives in successful digital transformations. These imperatives have shaped our Integrated Change Management methodology:

  1. Instituting an integrated approach across facts – i.e., tangible elements such as technology, processes and social interactions – i.e., intangible elements such as culture and teams throughout  the transformation journey and across the entire company
  2. Living agile by proactively updating change measures to meet both current needs and  overall objectives effectively
  3. Adapting “classic” Change Management levers such as communication, leadership, team setup, training, etc. for digitalization needs

This paper analyzes real cases to provide a deeper, more practical understanding of how digital transformation  can be a success story with the help of Integrated Change  Management. At the end of the paper, you will find insights as well as key recommendations to start your own digital transformation journey with confidence.

“Change Management  is a top 3 concern among executives when entering digital transformation  projects.”

“Digital transformation?  It’s all about the people!”


We live in times in which digitalization is radically changing the business landscape across industries. Companies worldwide are facing the challenge of managing the fast and repetitive adaptation of their organizations to suit the volatile circumstances of the digital age. Sooner or later, your business may also be faced with a disruption.

The worldwide digital transformation market is estimated to grow by 20% annually to USD 2 trillion by 2022, from the current size of USD 1.2 trillion, driven by the prospect of significant benefits in customer experience, time-to-market, product quality and operational reliability. However, the failure rates of digital transformation initiatives lie in the range of 60% to 85%. It is perfectly understandable that business leaders are quite uncertain when it comes to digital transformation in general, and about Change Management in particular.

The top challenges we often hear from  business leaders about digital transformations are

  1. CULTURE : There is a lack of openness to digitalization, sometimes even pushback from traditional entities. It takes time for both leadership and employees to adopt the necessary information-sharing mentality and cope with continuously changing conditions.
  2. DIGITAL TALENT : It is difficult to attract the right talent to execute the transformation or fill in new roles. New and existing employees need to grow together and work toward the same goals across the entire organization.
  3. SPEED : There is a need to progress quickly in an environment that lacks complete clarity. Encouraging people to speed up and make their own decisions can subject them to stress.
  4. EVOLVING TARGET STATE : Unlike traditional transformations, the target state continuously evolves due to changes in technologies, processes and roles.  The flexibility required for this approach with its “fail fast, fail often” mentality contradicts the common 100% quality approach of many traditional organizations.
  5. CROSS-FUNCTIONALITY : The impact of digitalization across interconnected business processes is not fully understood. Breaking down silos and linking formerly independent functions often leads to turmoil.

Does this sound familiar to you? A lot of business leaders we spoke with see the need for launching a digital transformation soon in order to prevent being outperformed by their competitors. However, they do not know how to prepare their organizations and employees for what is to come. Therefore, it is not surprising that 46% of interviewed C-level leaders report that Change Management is among their top three concerns when initiating digital transformation projects. They acknowledge that the only way to remain competitive is to create a wholehearted acceptance of digitalization within an organization. Because ultimately, people build up your business, not machines.


“Starting a digital transformation journey without a dedicated focus on Change Management is like  leaving the safe harbor with no knowledge of how  to sail through turbulent waters. Only when a mast cracks and the waves get rough do you notice that you forgot to manage the complex interplays.

To reach your desired destination with a sailboat, you need the basic equipment as visualized on the left. Facts such as technology and processes as well as social interactions influenced for example by culture and teams serve as the sails. Transformation management acts as the boat’s hull to provide the platform and a reliable structure for sailing the transformation in the right direction.

However, to master the challenges of a tough environment with troubled waters, skilled skippers adhere to three imperatives: Align both sails to benefit from the joint forces that quickly drive a boat forward, use a robust, yet flexible boat hull to hold course, and adapt the tools available on board to react to changing conditions.

Keeping the essence of these analogies and our experience with digitalization projects in mind, we created our Integrated Change Management approach to help you navigate your digital transformation journey:


From our experience in digitalization topics, we have identified the three following key imperatives for Change Management in  successful digital transformations:

  1. INSTITUTE AN INTEGRATED APPROACH TO CHANGE MANAGEMENT by incorporating tangible facts with the intangible social interactions throughout the digital trans- formation journey – from conception to implementation.
  2. LIVE AN AGILE CHANGE MANAGEMENT APPROACH given the context of technology  and people challenges that lead to evolving  target states. Humans often behave and react in unpredictable ways. Hence, Change Management must anticipate and adapt to changing situations to remain effective. Agile means being flexible  at all times to address current needs without compromising the vision.
  3. ADAPT CLASSIC CHANGE MANAGEMENT LEVERS TO THE CONTEXT OF DIGITALIZATION. The set  of levers for Change Management – comprised  of leadership, teams, culture, change story, communication, training, role transitioning  and learning organization – remain the same  but should be tuned to the requirements of digitalization.

See you in next blog with the following topics :

  • Institute an integrated approach to change management
  • Live an agile change management approach
  • Adapt classic change managment levers to the context of digitalization


Kadıköy, İstanbul – TURKEY

Author : M. Temel AYGÜN, Ph. D. in Aerospace Eng.

Copyright belongs to Author

Determining the target condition – Value Stream Design 4.0 by Space Dreamer (Author :M.Temel AYGUN)

VSD 4.0 serves the designing of the target condition for the future order-processing process including the associated information flows. In the first step, the approach comprises the traditional VSD, which aims to bring products into flow in order to achieve short throughput times. In the second step, there is a check of which stations can be further stabilized and designed to contain less waste through digitalization in order to improve or expand the product flow.

Finally, in a third step, the product and information flows are integrated and synchronized. The basic rule is that initially it should be striven for a robust flow based on process stability instead of digitalizing complex and inherently instable processes.

Execution of traditional VSD

Through VSD, a value stream vision is developed that preferably satisfies the previously formulated targets regarding throughput time, quality, productivity, etc. The approach was established following Rother [6], who describes value stream guidelines. The use of these guidelines results in workstations and processes being capable to fulfill a given work content within the scope of customer takt time allowance. Subsequently, adjoining processes can  be linked with one another to create the largest flow areas possible. Within those “islands of flow” a product or order can be further processed without waiting times. If processes cannot be directly linked (e.g. due to setup times  or different cycle times), they need to be decoupled through pull systems (FIFO systems or supermarkets). The authorization of new orders preferably takes place in one place, the so-called pacemaker process.

Digitally improving product flow

If a value stream vision has been developed in this way, implementation projects are defined that develop the current value stream step-bystep towards this vision. Typically, these projects first address traditional wastes. Fundamental projects are the introduction of standard work and the stabilization of quality. Subsequently, projects follow that bring about the improvement of the material flow, e.g. the line balancing of flow lines, the development of flow layouts, or the organization of a pull-material supply.

Subsequently, digital improvement opportunities through the following questions can be checked systematically:

  • Which traditional wastes can be better eliminated through digital measures? Example: The use of flexible pick-by technologies if material trays are too inflexible for zero-defect commissioning.
  • Which wastes in information logistics can be eliminated by a better organization? Example: Figures and their recording are unified for all machines in a group and used in the morning meeting for the target/actual comparison.
  • Which wastes in information logistics should be eliminated through digital measures? Example: The data for machine availability is recorded directly to the machine control instead of a manual transmission to MES.

Finally, there should be a check of which performance characteristics of the formulated busines model can be supported by the digitalization of the order processing.  Example questions could be:

  • How can the flow be further improved by automating manual planning steps that are repeated for every order?
  • At which point do configurators help in automatically translating customer requests into process parameters?
  • How can the product automatically parameterize work stations in order to further decrease setup times and support standard work?
  • Where does it make sense to assign process data to the product and make them available to the customer?

Integrating the product and process information flow

In the last step of VSD 4.0 the information defined, which is needed (product, process, and resource information) at the stations of the new value stream to implement the vision of order processing without waiting times. To start processes at a station without delay, all required information must be available at the beginning of the order. With this goal, the information needs of all processes are assessed and recorded as ”activities” in the process boxes. The same applies to the support processes like work preparation, intralogistics and maintenance.

Linking of information sources  and storage media

Based on the definition of future informational needs, suitable storage media are defined in cooperation with production-oriented IT and inscribed in the value stream map with the respective horizontal lines. Through vertical lines and the placement of points, a clear and standardized assignment of information sources to storage media takes place. For example, in this step it can be determined that, in the future, all quantity reports are automatically collected in MES. To show this, a vertical line from the data point “quantity” is drawn to the line of the storage medium MES and connected with a point.

In this final step, all activities that use available information are attached by dashed lines. For example, it is determined that the output quantity of every station available in MES is discussed daily in the course of shop floor management in order to recognize deviations and initiate improvements. From data point “quantity”, a dashed line is therefore drawn to the horizontal line of shop floor management and also connected with a point.     

Example application of VSD 4.0

For the already exemplarily observed value stream of the special machine manufacturer, the objective of value stream design is to significantly reduce the cycle-time whilst offering simultaneously high flexibility in the configuration for sales and customers. This is essentially achieved through

  • consistent digitalization of information sharing from the customer all the way to the machines
  • a drastic reduction in manual process steps and the associated processing time (from 6.5 h to 15 min)
  • a reduction of storage media (from 12 to 7) and media disruptions

The new process in detail

On the customer side, an online configurator was implemented that depicts the solution spaces possible in production in the dimensions categories, materials, and measurements. In this way, it is possible for the customer to configure and order the product without further communication with sales or development. The product data generated through the configurator is automatically transmitted to a parameterizable CNC code generator. The new CNC program arrives directly at the machine through the Distributed Numerical Control (DNC).

Until now, the order authorization took place through a push principle. The foreman planed the sequence of the orders according to demand and his own judgement (Go-and-see planning). In the course of VSD 4.0, strict FIFO pull processing now takes place (Fig. 20).

In order to stabilize and further improve the new processes, the figures delivery performance and capacity utilization are discussed in future daily shop floor meetings. In the event of deviations the PDCA cycle is started.


Kadıköy, İstanbul – TURKEY

M. Temel AYGÜN, Ph. D. in Aerospace Eng.

Copyright belongs to author.

Korona sonrası neler olabilir – 3 – by Futurist Numan Bayrak

Ülkemiz adına sevindirci gelişmelere şahit oluyoruz ve olacağız. Bir müsibet bin nasihatten iyidir ata sözünün gerçekleşmesini görmek hem üzüntü verici hemde sevindirici.

” İkra” kutsal kitabımız Kuran-ı Kerimin ilk sözlerinden dir ve bunun gündeme gelmesi için Korona gibi bir belanın olmasına gerek yoktu aslında.

“İlim Çin’de bile olsa gidiniz.” diye ne güzel söylemiş peygamberimiz Hazreti Muhammed.Evet Korona belası bizide ilme olan ihtiyacı farketmemiz için vesile oldu, oluyor ve olacak.

16 günde solunum cihazı yapabilen bir ülkemiz ve insan potansiyelimiz olan bir cennete sahip iken, ithalat cenneti olmayı seçmek ve hatta bunu devlet politikası haline getirmek zaten sürdürülebilir değildi.

Şimdi sıra hedeflenen 1 milyon kodlama yazan genci yetiştirip, onları teknoloji kümelerinde toplamak ve onların çarpan etkisiyle 5 milyon yabancı yazılım uzmanını Türkiye’ye çekecek Teknoloji Cennetleri yaratmak olmalı hedefimiz.

Sosyal izolasyon ile gelişecek yeni servis modelleri ve iş olanakları için hazır olmalı gençlerimiz.

Drone Cleaner yani Dron Temizlikçi dönemi hızla gelecek. Her belediye birer DKM ( Dron Kontrol Merkezi ) kuracak. Bu DKM lerde Dron sürücüleri (DS ) konumlanacak ve mahalle mahalle sokak sokak 7/24 temizlik yapacak dronları sürecekler, yönetecekler.

Dron Sürücü Kursları (DSK) yönetmeliği hızla hazırlanacak ve kamu ile özel Dron Sürücüleri yetiştirilecek ve Dron Sürücü Belgeleri verilecek.

Tabiiki bu arada var olan Dron üreten ve AR_GE çalışmaları yapan firmalarımız çeşitli boyut ve işlevde dronları geliştirme ve üretme konusunda hızla yol alacaklar. Zaten başlamış olmalılar diye düşünsek yanlış olmaz sanırım.

Dron ilaç uygalamaları için Eczacılar Birliği toplanmış ve evlere dron ile ilaç dağıtımı konusunu gündemine almış olmaları , kimseyi şaşırtmayacak. Zaten telefon ile başlayan aile hekimlerinin ilaç yazması uygulaması ile raporlu olan ilaçların otomatik eczaneler tarafından verilmesi uygulaması başladı. O zaman o ilaçların eczanelerden alınması yerine dronlarla hastanın evine teslim edilmesi de normal bir uygulama olacaktır.

Aile hekimi demişken , aile hekimine gidip rutin ilaç yazdırma işlemi yerine, doktorun internetten e-posta veya zoom/skype ortamında hastasıyla konuşması veya haberleşmesi de yeni normallerden olacaktır. O arada hastaya Tansiyonunu ölçüp e-posta yazması veya göstermesi, ateşini ölçüp bilgilendirmesinide isteyebilecektir Aile Hekimi.

Evlerimizde aile hekiminin bilgisayarıyla konuşabilen tansiyon ölçer, ateş ölçer, EKG çeken, kan şekeri ölçen, kan ve idrar tahlili yapan apartlar zaten her evin standart alet edevatından olacak. tabiiki benzer bilgiler o kişinin izin verdiği çocuklarının cep telefonlarına otomatik olarak bildirilecek ve bakıma muhtaç veya kontrol edilmesi istenen kişiler online yani anlık canlı olarak sağlık bilgilerini isterlerse paylaşabilecekler.

Özellikle en önemli konu olan hasta olan veya düzenli ilaç kullanmaları gereken kişilerin, almaları gereken ilaçları almamaları önemli bir sorun biliyorsunuz. Ama yakında böyle bir sorun olmayacak çünkü ; ilaçların her birine konacak nano sensörler sayesinde her bir ilaç izlenebilir olacak, o insan ilacı almadığında uyarı gönderecek ve kullanıldığında midede çözülünce de ayrı bir sinyal gönderecek. Proje takibi gibi güncel ve anlık olarak ilaç tüketimi takibi yapılabilecek.

Bu yukarıda saydıklarımızın bir çoğu üretiliyor veya arge çalışmaları bitmiş durumda.

İşte Koronanın yıkıcı yapıcı etkisi ( Disruptive constructive affect ) dedikleri bu olacak.

Anlık internet üzerinden başlayan psikolojik danışmanlıklar yaygınlaşacak ve bunu normal doktor muayeneleri takip edecek. İsmine dron doktor mu diyeceğiz, sanal doktor mu diyeceğiz bilmiyorum ama sosyal mesafeyi koruyarak maksimum hizmeti veren sağlıkçılar , sağlık kuruluşları ve ülkeler bir adım öne geçecekler. Çünkü bu sağlığa ulaşımı da demokratikleştirecek.

İşte burada Alan Musk’ın Starlink projesinin önemi bir kat daha artmış oluyor. Bilmem takip ediyor musunuz ama Starlink projesi ile her ay 60 adet uydu fırlatılıyor ve yakında 15 günde bir 60 uydu atılacak. Peki kaç uydu atılması planlanıyor? 1950 den bugüne 9500 civarında uydu atıldığını da not ederek düşünün bakalım?* (Cevabı yazımın en altına ekledim.).

Neden önemli çünkü 2030 yılına gelmeden internet bütün dünyada bedava olmasını hedefliyor Alan Musk. Üstelik var olan internet hızının onlarca kat hızlı ve onlarca kat az yatırım maliyetli bir çözümle. Evet çok önemli olacak bu hız ve bedava internet yukarıda konuştuğumuz nesnelerin interneti yani neslerin birbirleriyle konuşması için altyapıyı dahada güçlü bir şekilde hazırlamış olacak.

Tek yapmamız gerek hayal etmek, hayallerimize doğru yürümek değil koşmak.

Bunun için tembelliği bırakmamız ve Alan Musk gibi günde 16-18 saat çalışmalıyız.

Peygamberimiz Hazreti Muhammed’in sözüyle bugünkü yazımızı sonlandırıyorum;

“İlmin yarısı soru sormaktır.”

  • Sadece Alan Musk’ın 2030 a kadar yani 10 yıl içinde atacağı uydu sayısı 12 000 adet ( On iki bin adet ) !!!!

Altınova, Balıkesir, Türkiye


Yazar/ Author : Numan Bayrak

Copyrigths belongs to Author

Execution of the Value Stream Analysis 4 by Space Dreamer -10-

Author : M.Temel AYGUN

Starting the project & defining added value

Before the start of the project, the product or product family is determined, which is going to be analyzed. A product family is a group of products that occupies the same or similar resources in production and order processing. The VSA 4.0 is carried out for the entire order processing process. Therefore, the project team must also be assembled cross-departmentally. Employees from marketing, sales and adjustment development should especially be integrated.

At the beginning it should be clarified which product characteristics are especially important for customers and how they are created today. This helps refine one’s awareness for non-value-adding activities in the VSA. At the same time it is to define, what the value stream has to accomplish in the future in order to establish a striving competitive advantage (e.g. “…we deliver faster than…”, “…free product configuration…”) and to realize the planned business model. Then a clear target should be set by management which KPIs should be improved for the selected product group (e.g. reduce order throughput time to X days, reduce First Time Failure Rate to Y ppm, etc.) to achieve the desired competitive advantage. This makes it easier for the project team to prioritize improvement opportunities. In this way, improvement ideas can already be thought up by the team during the analysis phase to shape the future state vision.

Analyze the current state –  Value Stream Analysis 4.0

The traditional VSA initially creates an overarching understanding of the value stream for all involved. The result is a value stream representation with visualized areas of potential, the  Kaizen flashes. The familiar process boxes from the VSA are first extended upon in the VSA 4.0 in such a way that the collected information sources can be represented in extended notation. The type of data collection is characterized by the collection interval and its type of recording. At the same time, the respective current value is determined and inscribed in the process box. This notation should be used as uniformly as possible across all processes.

Understanding and incorporating storage media for information

To make the handling of data and information transparent, horizontal lines for each used storage medium are now delineated on the value stream map below the process boxes. Examples of storage media are paper, ERP systems, MES or MS Excel®, as well as the employees themselves. Next step is the analysis and representation of the information flows from the sources to the storage media. Therefore, information sources are affiliated with the associated storage media through vertical lines and nodal points.   

Analyzing the use of information

Subsequently, there is a review of which applications the collected information is used for, e.g. in quality management, for order control, or for shop floor management. For every type of usage, just like for storage media, horizontal lines are inscribed. Information sources, in turn, are subsequently affiliated with the applications through vertical (in this case, dashed) lines and points. Here it already becomes evident as to which collected information will not be used or will be used differently than intended.    

Recording wastes in information logistics In this step, the already introduced wastes in information logistics are recorded for all processes and inscribed as Kaizen flashes. Furthermore, the observed level of waste in dealing with information can be quantified by means of figures. As an example, here three figures are specified:

  • Data availability: It answers the question what percentage of necessary information/figures is actually being recorded.
  • Data usage: It shows what percentage of the recorded information sources is actually subsequently used.
  • Digitalization rate: It discloses what percentage of the recorded information sources is digitally recorded.

These figures can be calculated for a single workstation, a line or the entire order throughput.

Example application of VSA 4.0

The example shows a portion of the value stream of a special machine manufacturer, which extends from customer contact all the way to production (Fig. 17). Though products are individually adjusted in size and material depending on the customer’s application the order-specific information processing (customer clarification, adaptation of drawing, CNC programming) is basically the same for every customer project. The programming time for an order amounts to approx. 30 minutes and ultimately represents an implementation of the customer’s desired product parameters in a CNC code.

The following wastes arise from the traditional value stream analysis:

  • Frequent questions from construction to sales
  • Machine downtime during programming
  • Rejects due to programming errors

The application of VSA 4.0 provides further insights:

  • To exchange data and information, twelve different storage media are necessary (number of horizontal lines).
  • The high number of nodal points on the vertical lines of the data exchange indicates that process steps use several storage media for the same information.

Additional wastes arise from this, e.g.:

  • Employees must transmit information from different systems and with different formats by hand.
  • Media disruptions hinder the smooth flow of information and extend the processing time.

The figures confirm the findings and demonstrate additional potential for improvement: 

  • The data availability of the key performance indicators desired by management, such as processing time, quantity, etc. is 0 % for all processes.
  • None of the recorded information is being used in order to push forward an improvement of the value stream (undermost horizontal line). The data usage figure is therefore 0 %.
  • The digitalization rate in the value stream is  0 %, since paper is the storage medium used in different forms for every exchange of information.

Quick order processing through the synchronization of information flows

The traditional VSD aims to reduce the throughput time of a product by eliminating non-valueadding activities. Information is considered mainly from the process control perspective. This doesn’t sufficiently take into account the comprehensive, new opportunities for the use of information through digitalization and networking. Companies in mechanical engineering must consider further information flows beside the information for process control in order to be able to supply customers quickly and flexibly, improve processes, and increase the customer value through information-based services. Four information flows can be recognized that must mesh together in synchronization:

The product flow represents the physical flow of material. In production, this coincides with the product information flow (see below), partly from the flow of suppliers.

The utilities flow controls the provision and transport of necessary operating and auxiliary materials for the execution of an order.

The process information flow comprises information about the condition of production and all supporting processes (like processing time, force, temperature, pressure, etc.).

The product information flow comprises all information about the product. It begins with the customer but leads through development (e.g. drafting of drawing) and work preparation (e.g. programs, work plans) all the way to logistics, production and to the customer.

If one of the four information flows comes to a halt or is not synchronized with the other flows, delays can result due to waiting times. In order to avoid this, a synchronization of these information flows should be ensured. This is especially demanding in production, as all four flows encounter one another here. At a workstation, the work and testing instructions must be available at the same time as the physical product, the tools, fixtures and measurement devices, and the necessary process parameters configured. In addition, the customer is to be linked to the in-house information flows in order to accelerate order clarification, adaptation development and work preparation, but also to receive product information from the usage phase.  

See you in next blog with the following topics :

  • Determining the target condition –  Value Stream Design 4.0
  • Digitally improving product flow
  • The new process in detail


Kadıköy, İstanbul – TURKEY

M. Temel AYGÜN, Ph. D. in Aerospace Eng.

Copyright belongs to Author.

The path to a lean, digital value stream by Space Dreamer – 9 –

The path to a lean, digital value stream

Author : Mehmet Temel Aygün

The customer-individualized project business is formative for many companies in mechanical engineering. An exemplary analysis of the throughput times of orders of a manufacturer of customer-individualized machine components shows that the largest proportion of time goes towards development and parts procurement, followed by customer contact during project clarification and in the context of delivery and commissioning. The product only spends a small portion of time (4% in the example) in production. It can be assumed that a similar time distribution can be observed in many companies in the industry.

Today, the Value Stream Method is the standard in many companies that want to improve their product flow, reduce inventory, and decrease throughput times. The method’s focus is mostly on the parts and product flow from supplier to  customer. Information flows are essentially regarded from the perspective of production control and its improvement.

The extended Value Stream Method

If the objective, however, is to satisfy individualized customer requests quickly, flexibly, and,  at the same time, efficiently, only considering production, material flows, and the associated control information falls short. For this reason, in the following, the focus of the traditional Value Stream Method will be extended to all areas involved in order processing, including the customer. Furthermore, the information within the scope of this method will be considered from three new perspectives:

  • Waste in handling information
  • Use of information for process improvement
  • Use of information to increase customer value

A look at waste in handling information

Lean activities typically aim for eliminating transport, inventory, movement, waiting time, overproduction, over-fulfilling processes, and defects. These traditional types of waste provide support in the analysis of material flows and production itself, but they cannot be transferred directly to information flows. In order to holistically recognize wastes and potential in handling information, a new perspective is necessary. Following material logistics, the term information logistics is therefore introduced. For this, the goal is formulated to provide information at the right time, at the right place, in the right amount, and of the proper quality and ultimately to be used in a target-oriented manner. This should take place with as little waste as possible.

Within the scope of a Value Stream Analysis 4.0, eight types of waste in information logistics are introduced that emerge along the lifecycle of information and can be assigned to defined phases. A cycle consists of three phases:  

  • Data generation and transmission
  • Data processing and storage
  • Data usage

The individual types of waste in information logistics are clarified in the following by guiding questions. 

Phase of data generation  and data transmission

The goal during data generation and transmission is to make the desired data available in the proper quality. Wastes can occur in the:

Data selection

  • Has a purpose been designated?
  • Is clearly defined what the data will be used for?

Data quality

  • Do the frequency and level of detail of the collection fit with the intended use?
  • Has the data been collected and transmitted in a standardized manner?  

Data collection

  • Is the collection of data appropriate with regard to the costs and benefit?
  • Is the regular collection of data automated?

Data transmission

  • Does an interface-free communication of data take place?
  • Is the data stored centrally?

Phase of data processing and storage

Data and the resulting information should be processed continuously and without waiting time in order to be available for decisions or activities. Wastes can be:

Waiting times and inventory

  • Can an order not be processed because information is missing?
  • Is data and information available at exactly the right time?

Transport, movement and searching

  • Can employees find the required information without searching effort?
  • Is the presentation medium suitable?

Data usage

The data compacted into information is to be used purposefully, either for order processing, for the improvement of processes, or to increase to product’s value. product value The following wastes can arise:

Data analysis

  • Is the recorded data analyzed with appropriate methods?
  • Are these analyses used?

Decision-making support

  • Is the data verifiably used for decisions or improvement activities?
  • Is the information processed in accordance with its use? 

Value Stream Method 4.0 

Through the Value Stream Method 4.0, all product and information flows of a value stream are analyzed and designed.  It comprises the Value Stream Analysis 4.0 (VSA 4.0) and the alue Stream Design 4.0 (VSD 4.0). The approach extends across departments from the first customer contact all the way to the shipment of the product. The goal of the method is to develop all processes of a value stream in such a way that customer requests can be satisfied quickly, flexibly and thereby efficiently. The emphasis here is on the simultaneous consideration and synchronization of product and information flows.

Approach in three steps – an overview

Step A – Define added value

The starting point of the Value Stream Method 4.0 lies in obtaining a basic understanding of what generates customer value. This refines one’s awareness during the search for wastes

Step B – Analyze the current state

Within the scope of the traditional VSA, process data, inventory, and control information are recorded and so-called “Kaizen flashes” (improvement opportunities) are delineated. The scope of the VSA 4.0 is extended to the entire order-processing, begins with the first customer contact, and goes all the way until product usage. A detailed observation of the information flow follows during the order cycle. It targets wastes that emerge during the handling, transport and usage of data and information (so-called wastes in information logistics).   

Step C – Determine the future state

Only a fundamentally stable and, with regard to material flow, lean value stream should be digitally supported or digitally valorized. Therefore, the traditional VSD, with its design rules, continues to remain the first step to the digital target condition. The resulting value stream vision is improved through the targeted use of digitalization solutions in order to stabilize or expand the flow, or to eliminate process steps. The VSD 4.0 focuses on the integration of product flows and the necessary information flows as well as on elaborating a consistent implementation in  IT systems.

See you in next blog with the following topics :

  • Execution of the Value Stream Analysis 4.0


Kadıköy, İstanbul – TURKEY

M. Temel AYGÜN, Ph. D. in Aerospace Eng.

Copyright is Author’s.

Lean & Industry 4.0 part 4 by Space Dreamer – 8

Author : M.Temel AYGUN

Takt, Flow, Pull

The customer takt is the average time that passes between the shipment of individual products of a product group. The closer and more stable process steps can follow the customer takt, the more closely they can be connected, the less unproductive waiting times occur and the better the material flows.

“Pull” means that material movements or orders are only authorized or started by a demand of internal or external customers. Within the scope of flow and pull, the customer takt synchronizes the activities of all parties involved in the value stream so that they intertwine with as little waste as possible.

Limits of Takt, Flow, Pull: If products are very different with regards to their work content and bills of material and if their demand fluctuates strongly, then determining a customer takt is very demanding or even impossible. The stronger the fluctuation of the work content within a product group, the more demanding it will be to economically organize flow lines with takt. Also, big machines and systems can only be moved between stations with great effort, which is why they are often constructed at the location. At the level of the production material, pull control according to the supermarket principle can only be economically implemented for materials that have a regular consumption and a not too high value.

Opportunities through  digitalization and Industrie 4.0

  • Opportunity 1:  Reducing cycle times through an improved flow of information: In project business, a value stream analysis should take place from the first contact with the customer all the way to the maintenance of a product. Thereby, special focus should be on idle times and waiting times due to missing information (authorizations, documents, programs, etc.). Here, the following rule applies: No order may wait due to missing information. The necessary extension of the value stream focus to production-related areas (work preparation, order planning, logistics) provides valuable new insights. Subsequently, unused information is to be eliminated and the measurement, transfer, and provision of necessary information is to be improved through digitalization.
  • Opportunity 2:  Aligning material logistics with demand: Inventory at workstations and lines can be reduced if transport orders for material are only triggered by a pull signal from the line itself (e.g. via MES). Here, the release of productions orders is to be treated separately from the authorization of the transport orders. For large assembly works the request for retrieval of the material can take place in this way in accordance with the assembly progress through appropriate terminals at the assembly site. A better levelling of the workload in logistical areas is the result.

Logistical elements like supermarkets or FIFO (First-In-First-Out) lanes can be made more flexible through digital support.For example, by dynamically adapting inventory to demand and supply patterns. eKanban helps reduce inventory by a faster transmission of information. Milk run systems (e.g. routes) can be dynamically adapted to the current demand. The drivers of a milk run train are to be shown all necessary information in order to guarantee the shortest routes and to avoid mistakes. Automated guided vehicle systems also find application here.

  • Opportunity 3:  Utilizing assembly lines in a better way: By adapting the workstations, a larger spectrum of different products can be economically assembled at the same stations. Here, the ability of products to identify themselves at workstations (so-called active traceability) proves helpful. It is conceivable that a product configures its own work instructions, triggers the picking of its individual materials, or ensures that the workstation is digitally supplied (the product controls the process) with the suitable process data (e.g. torque, NC program codes, test programs, etc.).
  • Opportunity 4:  Recognizing bottlenecks early on: Through the networked representation of the information from critical reporting points and a tracing of materials along a supply chain, bottlenecks can be recognized early on and countermeasures can be taken before serious disturbances occur.

Autonomation / Jidoka 

Autonomation / Jidoka pursues the goal of developing processes that allow only to produce good parts. This should be achieved through mistake-proof devices (Japanese: Poka Yoke) as well as through a workstation design that guarantees zero defects (so called “built-in quality”). In case of problems occurring they should be reliably recognized by machines or employees, which usually triggers a defined escalation process that can lead all the way to the stop page of production (reactive improvement cycle). It is important to ensure a short feedback loop to the location of an error, so that containment can start quickly and a problem analysis can be carried out with fresh and reliable information. This is the prerequisite for short-term protection of the customer and a sustainable problem solution.

Limits of Autonomation / Jidoka: If small quantities of different products are produced at the same workstation, often a 100% avoidance of error through devices cannot be achieved with acceptable effort. Without a clear specification of process parameters, work steps and expected work progress, the recognition of deviations is hindered. Escalation cascades (who reacts until when?) do not work safely, if they are defined at all. If a problem is nevertheless identified, measures are first taken against the effect of a problem. A systematic, causal problem solution is often omitted, so that a problem can reoccur. However, if a systematic approach is taken, problem analysis is often sloppy because associated process information is missing or can only be obtained with a great deal of effort. This leads to inadequate measures or prolongs the problem-solving process.

Opportunities through  digitalization and Industrie 4.0

  • Opportunity 1: Increasing safeguard against improper mishandling (production): Where hardware solutions are too inflexible to handle variety, improper handling and mistakes during execution can be prevented through software-based solutions. Products that identify themselves when registering for a process (e.g. at a screw or test station) can initiate the configuration of devices, tools, and work instructions specific to them. Digital worker assistance systems show work documents and steps via a monitor or data glasses. The movements of a person can be followed through ultrasound or camera systems and compared with the expected procedure in order to intervene in the event of deviations. In this way, mistake-proofing processes can finally be achieved by a softwaresupported, adaptive Poka Yoke.
  • Opportunity 2: Avoiding improper handling in information flow: In areas that are upstream or downstream of production (e.g. development, work preparation, or shipping), IT system discontinuities are to be avoided that can lead to the error-prone transmission of data and waiting times. Generally, in these areas all activities with mainly repetitive character are to be critically scrutinized.    
  • Opportunity 3: Solving problems more effectively: Through component identification and backtracking, product and process information can be comprehensively interwoven. Defective products can be better narrowed down. Also, the location of the emergence of an error can be found faster and problems can be described more fully.    
  • Opportunity 4: From reacting to preventing defects: The linking of process data with deviations allows for the training of systems. In this way, conditions in the future can be forecasted based on current process data. The residual lifespan of tools or components for instance,can be determined. In several cases, problems can be proactively recognized and solved without defects emerging. This results in a reduced rate of rejects and rework.

Continuous Improvement Process (CIP)

The reduction of non-value-adding activities forms the core of a lean system. Improvement activities are either reactively initiated through deviations from target conditions or proactively created through the provision of newer, more demanding goals. The underlying approach of improvement follows the PDCA cycle. Deviations from standards or gaps to targets initiate the PDCA cycle anew every day. Employees solve the underlying problems and ideally thereby improve both their processes and their own problem solving skills.

Limits of Continuous Improvement Process: Occurring problems may have already been solved elsewhere in the same or a similar form. Often the team lacks knowledge of these solutions which could shorten the problem solving process. If the problem complexity is underestimated and an appropriate problem analysis remains undone, then the cause-effect-chain will not be traced back to the root cause. In consequence the defined measures very likely won’t address the root cause. Such a process can only effectively solve “simple” problems. The more complex problems are, the more spread out the activities of the PDCA cycle across multiple employees and departments. This complicates the pursuit of deadlines and results on the action plan and the probability increases that the PDCA cycle is only undergone incompletely. Typically, a measure is only partially implemented (Plan & Do), the necessary success monitoring on-site remains undone (Check), and the improvement approach “peters out” with time.

Opportunities through  digitalization and Industrie 4.0

  • Opportunity 1: Transparency in the tracking of improvement measures: Software-based action plans help to more easily track the progress of individual measures and to increase transparency through the allocation of tasks amongst employees and between different departments. At the same time, they help to ensure the complete execution of a PDCA cycle.
  • Opportunity 2: Improving knowledge management: The digital documentation of successful problem solutions and their implementation can take place through databases (e.g. in the form of a Wiki system). The opportunity for a networked search for these activities can prevent the same problem or similar problems from being solved twice. 
  • Opportunity 3: Better recognizing complex connections: The process data that belongs to a deviation or a defect can be automatically integrated into a systematic problem analysis and clearly depicted. In this way, the team receives a better foundation for the subsequent search for the cause. 

The stated opportunities can, for example,  be realized through digital Shop Floor Management.

See you in next blog with the following topics :

  • The path to a lean, digital value stream
  • A look at waste in handling information
  • Value Stream Method 4.0 


Kadıköy, İstanbul – TURKEY

M. Temel AYGÜN, Ph. D. in Aerospace Eng.

1961 İzmit İstiklal Gazetesi – Babamın Yazıları – 12 (1 Haziran 1961) – KAZIK



Author/Yazan: Numan Bayraktaroğlu

Bir arkadaşım anlatıyordu:

Şu İstanbul İzmit asfaltı var ya, bu yol üzerinde ve İstanbul’dan İzmite’e doğru karısı ve bir yaşında çocuğu ile otobüsle seyahat ediyormuş. Otobüsleri, Gebze’nin altındaki çınarlı benzin istasyonunda benzin ikmali yaparken, bizimkilerde hemen benzinciye bitişik ve görünüşü çok temiz olan bir lokantaya girip çocuklarına yiyecek sorarlar. Lokanta sadece içki için kurulu olduğundan çorba V.S.nin olmadığı bildirilir. Çocuk için bir şey bulamayınca, bari derler, biz bir parça bir şeyler yiyelim diye ve hakikaten oturup dört tabak yemek yerler. Hesap 7.75 lira tutmuştur.

Şimdi biz, bunların yediklerine bir göz atalım: Birer tabağı zeytinyağlı dolma. Her tabakta, kuş yumurtası kadar ve dört tane dolma var. İnsan dördünü çatalla bir lokmada yutabilir. İkinci yemekleri, birer ızgara ciğer. Bu da on porsiyon yense doyulmayacak kadar az.

Peki….Bu lokantanın diğerlerinden farkı nedir ki; başka yerde dört liraya yenecek yemekleri burada sekiz liraya yiyelim. Herhangi bir eğlencesi yok, müzik yok ve diğerlerinden farklı hiçbir şeyi yok.

Ben, bu lokantayı bilirim. Oradan her geçişimde bomboş durur ve içinde müşteriyi pek nadir anlarda görürsünüz. Hani bir fıkra vardır: Bir eşek, hızla bir lokantaya girer, ve bir sürü yemek ısmarlıyarak karnını doyurur. Garsondan hesabı ister ve öder. Ancak kendine merakla bakan garsona “Ne bakıyorsun,, deyince garson “vallahi,, der “Ben şimdiye kadar, bizim lokantaya bir eşeğin gelip yemek yediğini hiç görmedimde ondan şaşırdım.,, Bunun üzerine eşek, güler ve “Vallahi siz bu tarifeyle ikinci bir eşeğin daha burada yemek yediğini artık göremezsiniz,, der.

Bizim bahsettiğimiz gibi lokantanın durumu da aynıdır. Ancak işin asıl garibi, bunlara, bu tarifeyi verenlerde veyahut tasdik edenlerdeki zihniyettir.

1 Haziran 1961

Darıca, Kocaeli, Türkiye

Yazan : Numan Bayraktaroğlu

Digital development paths using the Toyota House as an example by Space Dreamer – 7

Author: M.Temel AYGUN

Lean production often appears as an approach that comes from the large-scale production of automobiles and from time to time reaches its limits in the project business of machine and system engineering. A high percentage of customer individuality, a low repetition frequency of activities, and extensive work contents impede the standardization of procedures and thereby prevent the basis for a sustainable introduction of lean. Especially the aspect of overcoming these limits through digital opportunities will play a role in the following chapter. For each element of the TPS the underlying mindset will be briefly introduced and where the practical implementation often reaches its limits will be shown. Subsequently, the opportunities for overcoming these limits within the context of digitalization and Industrie 4.0 will be presented.

Stable and Standardized  Processes

The stabilization and subsequent standardization of processes is the foundation for continuous improvement processes. The recognition of deviations is only possible through standards with a continuous target/actual comparison. Stability and standardization are furthermore the prerequisite for running further TPS elements, such as flow production or the triggering of PDCA cycles. Therefore, stability and standardization build the foundation of the TPS.

Limits of Stable and  Standardized Processes: Customer-specific individual products cause a high processing effort from sales to adjustment development, purchasing, and work preparation all the way to production and service. The creation of standard work instructions for individualized products and their components is time-consuming and error-prone. The processing and cycle times vary both in production and in all other areas. Frequently changing processing tasks mean that it is almost impossible to set times for workplaces and systems. But without standard specifications, deviations and underlying problems can hardly be recognized.

Opportunities through digitalization and Industry 4.0 :

  • Opportunity 1:  Extend standard work in  customer-individualized production: Analogous to the structure of the product program, related work and process standards can be modularized and digitalized. Subsequently, the work documents are adaptively configured for each of the products to be produced and digitally made available to the operator on-site. In this way, quantity and time per unit can also be specified for non-series production. These adaptive work standards, in turn, are the basis for managing deviations as a foundation of continuous improvement processes.
  • Opportunity 2:  Reducing effort in production preparation, avoiding mistakes during execution: The modularization and digitalization of work instructions means increased initial effort, but subsequently reduces work preparation effort for individual orders. Digital worker assistance systems provide the current information and thereby avoid improper processing caused by outdated paper documents. This way, paper will continue to be increasingly replaced in production by networked, electronic media. At the same time, assistance systems (e.g. pick-by technologies in commissioning) can avoid or recognize wrongful acting. Thereby, quality can be improved, order throughput time be reduced and flexibility be increased.
  • Opportunity 3:  Increasing the reliability of machines: Many companies measure the efficiency of their machinery and equipment and record the reasons for loss. Regardless, unplanned breakdowns still occur. The targeted equipping of existing systems with networked sensors can help to detect critical conditions (contamination, filling levels, wear and tear) at an early stage and to plan the maintenance or replacement of components in good time (e.g. during the next planned maintenance shift). The next predictive level of maintenance can be reached with machine learning. Through the linking of a machine’s condition data with labeled event data (dimensions, surface areas, tool breakage, etc.), connections can be found and future events predicted.

Visual Management

The visual representation of process conditions and performance as well as standard procedures and work documents provides employees and management with the opportunity to recognize deviations immediately and without additional aids and activities. A good visualization makes standards simple and easy to understand through optical elements without additional tools so that no deviation keeps consealed. The identification of deviations is in turn a trigger for continuous improvement processes.

Limits of Visual Management: Manual recording, continuous updating and subsequent visualization of key figures is time consuming and error-prone. The performance of a process is often only recorded at the end of a shift or a week. This wastes valuable response time for managers and employees.

Opportunities through digitalization and Industry 4.0

  • Opportunity 1:  Early detection of deviations –  Employee perspective: Important information about one’s own process can currently be made available to the employee directly at his or her workstation or anywhere else. In the simplest case, this is the current target/actual performance comparison (e.g. for number of units/productive time and scrap/rework time). This way, the employee can react or escalate in a timely manner. By expanding information to a simple trend analysis all the way to the prediction of deviation (realized through machine learning), the employee can engage proactively before negative effects on the process occur. Already today, for example, the failure of tools can be predicted. Depiction can take place, depending on the user, on-site via monitor, smartphone, or directly on a smart wearable (watch, glasses, etc.). This way, information can be made available directly to the respective employee.
  • Opportunity 2:  Early detection of deviations –  Management perspective: Through the digital and direct provision of actual values, trends, and prognoses, management is provided with the opportunity to intervene more quickly. The daily meetings on the shop floor can be supported by the current values of important workstations, machines and systems, which are sent via radio technology directly to a device. Escalation cascades are pushed beyond the limits of manufacturing execution systems (MES) or enterprise resource planning systems (ERP) and management can provide support early on, reallocate resources, or situationally find solutions.
  • Opportunity 3:  Solving problems more quickly with digital shop floor management: In daily shop floor meetings, above all, it is about recognizing target/actual deviations and describing these for the subsequent problem solving as comprehensively as possible. Figures, measurement results, and process data provide help and enrich the structured problem definition. Their digital measurement can bring about a temporal relief. The far greater benefit of digitalization lies in the possi bility of detecting relations between deviations (e.g. product defects) and the associated process data (such as temperatures, power consumption, feed forces, etc.). In this way, the point at which the error occurs can be narrowed down more quickly and more reliably. Utilizing documented digital PDCA cyclesenables  a networked search for similar events and thereby successful solutions.

Levelled Production

The direct transfer of fluctuatingdemand to production leads also to a fluctuating capacity utilization. This results in provision of additional capacity, unused capacity, as well as continuous plan changes, also in supply. The stronger the fluctuation of capacity, the harder it is to adhere given standards and processes, with the corresponding consequences for quality  as well as managing deviations. Therefore, through levelling and smoothing, the planning of production is decoupled from the market demand to a certain extent. This takes place, on the one hand, with a view to the overall capacity of production, in which orders are planned only until the attainment of maximum capacity. On the other hand, orders are scheduled with a smoothing pattern (“mix”) so that individual stations are not overloaded while others are simultaneously underloaded.

Limits of Levelled Production: Levelling and smoothing fluctuating demand for customer-individualized products is a challenging task. This is mainly due to the very different character of products and the associated fluctuating load of individual workstations or production areas. In this environment, master data, order data, resource data or project data are barely maintained or not available. Furthermore, available resources are unknown when decisions on the quantities and deadlines are made. Cooperation between sales, customization development and work scheduling is not synchronized, which is why customers are promised orders that cannot be realized within the specified time frame.

Opportunities through  digitalization and Industrie 4.0

  • Opportunity 1:  Making better decisions in sales: For the even planning of orders, it is already necessary in sales to estimate a new project’s capacity requirements in individual areas or at different locations and to synchronize it with the available capacity. The basis for this is on the one hand the division of the available capacity into so-called capacity buckets On the other hand, maintaining bills of material and standard times in the ERP system is needed. While this is a standard function of current ERP  systems, the great opportunity lies in making the capacity available for sales via a mobile frontend. Within a time frame (e.g. one week), only a certain amount of orders is confirmed until the associated capacity bucket has been exhausted. This way, customers can be promised more realistic delivery dates and throughput times decrease, as the inventory of already begun orders is avoided by less rescheduling.
  • Opportunity 2:  Better utilization with simpler planning: For certain product properties, a solution space can be defined that can be mapped by production without major planning and set-up effort. Configurators enable the sales department or even the end customer to find their individual solution based on rules. If the affected production system is prepared for all parameter combinations without major set-up effort, customers can book the next free deadline for their orders themselves. There is no need for the creation of engineering drawings, the writing of offers, as well as activities in work preparation.   
  • Opportunity 3:  Self-organized pull planning: In defined segments of production (e.g. final assembly), free workstations can pull the next suitable order from a reserve of released and prepared orders. This is supported by automated guided vehicle systems (AGV) which transport the preliminary product and the required material to the next free station. The goal is an efficient and even use of available capacities.

See you in next blog with the following topics :

  • Takt, Flow, Pull
  • Autonomation / Jidoka 
  • Continuous Improvement Process (CIP)


Kadıköy, İstanbul – TURKEY

M. Temel AYGÜN, Ph. D. in Aerospace Eng.

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