Failure Mode and Effect Analysis (FMEA) Method


Failure Mode and Effect Analysis (FMEA) adalah pendekatan sistematik yang menerapkan suatu metode pentabelan untuk membantu proses pemikiran yang digunakan oleh engineers untuk mengidentifikasi mode kegagalan potensial dan efeknya. FMEA merupakan teknik evaluasi tingkat keandalan dari sebuah sistem untuk menentukan efek dari kegagalan dari sistem tersebut. Kegagalan digolongkan berdasarkan dampak yang diberikan terhadap kesuksesan suatu misi dari sebuah sistem.

Secara umum, FMEA (Failure Modes and Effect Analysis) didefinisikan sebagai sebuah teknik yang mengidentifikasi tiga hal, yaitu :

1. Penyebab kegagalan yang potensial dari sistem, desain produk, dan proses selama
siklus hidupnya,
2. Efek dari kegagalan tersebut,
3. Tingkat kekritisan efek kegagalan terhadap fungsi sistem, desain produk, dan

FMEA merupakan alat yang digunakan untuk menganalisa keandalan suatu sistem dan penyebab kegagalannya untuk mencapai persyaratan keandalan dan keamanan sistem, desain dan proses dengan memberikan informasi dasar mengenai prediksi keandalan sistem, desain, dan proses. Terdapat lima tipe FMEA yang bisa diterapkan dalam sebuah industri manufaktur, yaitu :

1. System, berfokus pada fungsi sistem secara global
2. Design, berfokus pada desain produk
3. Process, berfokus pada proses produksi, dan perakitan
4. Service, berfokus pada fungsi jasa
5. Software, berfokus pada fungsi software

Berikut ini adalah tujuan yang dapat dicapai oleh perusahaan dengan penerapan FMEA:

1. Untuk mengidentifikasi mode kegagalan dan tingkat keparahan efeknya
2. Untuk mengidentifikasi karakteristik kritis dan karakteristik signifikan
3. Untuk mengurutkan pesanan desain potensial dan defisiensi proses
4. Untuk membantu fokus engineer dalam mengurangi perhatian terhadap produk dan
proses, dan membentu mencegah timbulnya permasalahan.

Dari penerapan FMEA pada perusahaan, maka akan dapat diperoleh keuntungan – keuntungan yang sangat bermanfaat untuk perusahaan, (Ford Motor Company, 1992) antara lain:

1. Meningkatkan kualitas, keandalan, dan keamanan produk
2. Membantu meningkatkan kepuasan pelanggan
3. Meningkatkan citra baik dan daya saing perusahaan
4. Menurangi waktu dan biaya pengembangan produk
5. Memperkirakan tindakan dan dokumen yang dapat menguangi resiko

Sedangkan manfaat khusus dari Process FMEA bagi perusahaan adalah:

1. Membantu menganalisis proses manufaktur baru.
2. Meningkatkan pemahaman bahwa kegagalan potensial pada proses manufaktur harus
3. Mengidentifikasi defisiensi proses, sehingga para engineer dapat berfokus pada
pengendalian untuk mengurangi munculnya produksi yang menghasilkan produk yang
tidak sesuai dengan yang diinginkan atau pada metode untuk meningkatkan deteksi
pada produk yang tidak sesuai tersebut.
4. Menetapkan prioritas untuk tindakan perbaikan pada proses.
5. Menyediakan dokumen yang lengkap tentang perubahan proses untuk memandu
pengembangan proses manufaktur atau perakitan di masa datang.

Output dari Process FMEA adalah:

1. Daftar mode kegagalan yang potensial pada proses.
2. Daftar critical characteristic dan significant characteristic.
3. Daftar tindakan yang direkomendasikan untuk menghilangkan penyebab munculnya mode
kegagalan atau untuk mengurangi tingkat kejadiannya dan untuk meningkatkan
deteksi terhadap produk cacat bila kapabilitas proses tidak dapat ditingkatkan.

FMEA merupakan dokumen yang berkembang terus. Semua pembaharuan dan perubahan siklus pengembangan produk dibuat untuk produk atau proses. Perubahan ini dapat dan sering digunakan untuk mengenal mode kegagalan baru. Mengulas dan memperbaharui FMEA adalah penting terutama ketika:

1. Produk atau proses baru diperkenalkan.
2. Perubahan dibuat pada kondisi operasi produk atau proses diharapkan berfungsi.
3. Perubahan dibuat pada produk atau proses (dimana produk atau proses berhubungan).
Jika desain produk dirubah, maka proses terpengaruh begitu juga sebaliknya.
4. Konsumen memberikan indikasi masalah pada produk atau proses.

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An Introduction to Data Mining


Data mining, the extraction of hidden predictive information from large databases, is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses. Data mining tools predict future trends and behaviors, allowing businesses to make proactive, knowledge-driven decisions. The automated, prospective analyses offered by data mining move beyond the analyses of past events provided by retrospective tools typical of decision support systems. Data mining tools can answer business questions that traditionally were too time consuming to resolve. They scour databases for hidden patterns, finding predictive information that experts may miss because it lies outside their expectations.

Most companies already collect and refine massive quantities of data. Data mining techniques can be implemented rapidly on existing software and hardware platforms to enhance the value of existing information resources, and can be integrated with new products and systems as they are brought on-line. When implemented on high performance client/server or parallel processing computers, data mining tools can analyze massive databases to deliver answers to questions such as, "Which clients are most likely to respond to my next promotional mailing, and why?"

This white paper provides an introduction to the basic technologies of data mining. Examples of profitable applications illustrate its relevance to today’s business environment as well as a basic description of how data warehouse architectures can evolve to deliver the value of data mining to end users.

The Foundations of Data Mining

Data mining techniques are the result of a long process of research and product development. This evolution began when business data was first stored on computers, continued with improvements in data access, and more recently, generated technologies that allow users to navigate through their data in real time. Data mining takes this evolutionary process beyond retrospective data access and navigation to prospective and proactive information delivery. Data mining is ready for application in the business community because it is supported by three technologies that are now sufficiently mature:

  • Massive data collection
  • Powerful multiprocessor computers
  • Data mining algorithms

Commercial databases are growing at unprecedented rates. A recent META Group survey of data warehouse projects found that 19% of respondents are beyond the 50 gigabyte level, while 59% expect to be there by second quarter of 1996.1 In some industries, such as retail, these numbers can be much larger. The accompanying need for improved computational engines can now be met in a cost-effective manner with parallel multiprocessor computer technology. Data mining algorithms embody techniques that have existed for at least 10 years, but have only recently been implemented as mature, reliable, understandable tools that consistently outperform older statistical methods.

In the evolution from business data to business information, each new step has built upon the previous one. For example, dynamic data access is critical for drill-through in data navigation applications, and the ability to store large databases is critical to data mining. From the user’s point of view, the four steps listed in Table 1 were revolutionary because they allowed new business questions to be answered accurately and quickly.

The Scope of Data Mining

Data mining derives its name from the similarities between searching for valuable business information in a large database — for example, finding linked products in gigabytes of store scanner data — and mining a mountain for a vein of valuable ore. Both processes require either sifting through an immense amount of material, or intelligently probing it to find exactly where the value resides. Given databases of sufficient size and quality, data mining technology can generate new business opportunities by providing these capabilities:

  • Automated prediction of trends and behaviors. Data mining automates the process of finding predictive information in large databases. Questions that traditionally required extensive hands-on analysis can now be answered directly from the data — quickly. A typical example of a predictive problem is targeted marketing. Data mining uses data on past promotional mailings to identify the targets most likely to maximize return on investment in future mailings. Other predictive problems include forecasting bankruptcy and other forms of default, and identifying segments of a population likely to respond similarly to given events.
  • Automated discovery of previously unknown patterns. Data mining tools sweep through databases and identify previously hidden patterns in one step. An example of pattern discovery is the analysis of retail sales data to identify seemingly unrelated products that are often purchased together. Other pattern discovery problems include detecting fraudulent credit card transactions and identifying anomalous data that could represent data entry keying errors.

Data mining techniques can yield the benefits of automation on existing software and hardware platforms, and can be implemented on new systems as existing platforms are upgraded and new products developed. When data mining tools are implemented on high performance parallel processing systems, they can analyze massive databases in minutes. Faster processing means that users can automatically experiment with more models to understand complex data. High speed makes it practical for users to analyze huge quantities of data. Larger databases, in turn, yield improved predictions.

Databases can be larger in both depth and breadth:

  • More columns. Analysts must often limit the number of variables they examine when doing hands-on analysis due to time constraints. Yet variables that are discarded because they seem unimportant may carry information about unknown patterns. High performance data mining allows users to explore the full depth of a database, without preselecting a subset of variables.
  • More rows. Larger samples yield lower estimation errors and variance, and allow users to make inferences about small but important segments of a population.

A recent Gartner Group Advanced Technology Research Note listed data mining and artificial intelligence at the top of the five key technology areas that "will clearly have a major impact across a wide range of industries within the next 3 to 5 years."2 Gartner also listed parallel architectures and data mining as two of the top 10 new technologies in which companies will invest during the next 5 years. According to a recent Gartner HPC Research Note, "With the rapid advance in data capture, transmission and storage, large-systems users will increasingly need to implement new and innovative ways to mine the after-market value of their vast stores of detail data, employing MPP [massively parallel processing] systems to create new sources of business advantage (0.9 probability)."3

The most commonly used techniques in data mining are:

  • Artificial neural networks: Non-linear predictive models that learn through training and resemble biological neural networks in structure.
  • Decision trees: Tree-shaped structures that represent sets of decisions. These decisions generate rules for the classification of a dataset. Specific decision tree methods include Classification and Regression Trees (CART) and Chi Square Automatic Interaction Detection (CHAID) .
  • Genetic algorithms: Optimization techniques that use processes such as genetic combination, mutation, and natural selection in a design based on the concepts of evolution.
  • Nearest neighbor method: A technique that classifies each record in a dataset based on a combination of the classes of the k record(s) most similar to it in a historical dataset (where k ³ 1). Sometimes called the k-nearest neighbor technique.
  • Rule induction: The extraction of useful if-then rules from data based on statistical significance.

Many of these technologies have been in use for more than a decade in specialized analysis tools that work with relatively small volumes of data. These capabilities are now evolving to integrate directly with industry-standard data warehouse and OLAP platforms. The appendix to this white paper provides a glossary of data mining terms.

How Data Mining Works

How exactly is data mining able to tell you important things that you didn't know or what is going to happen next? The technique that is used to perform these feats in data mining is called modeling. Modeling is simply the act of building a model in one situation where you know the answer and then applying it to another situation that you don't. For instance, if you were looking for a sunken Spanish galleon on the high seas the first thing you might do is to research the times when Spanish treasure had been found by others in the past. You might note that these ships often tend to be found off the coast of Bermuda and that there are certain characteristics to the ocean currents, and certain routes that have likely been taken by the ship’s captains in that era. You note these similarities and build a model that includes the characteristics that are common to the locations of these sunken treasures. With these models in hand you sail off looking for treasure where your model indicates it most likely might be given a similar situation in the past. Hopefully, if you've got a good model, you find your treasure.

This act of model building is thus something that people have been doing for a long time, certainly before the advent of computers or data mining technology. What happens on computers, however, is not much different than the way people build models. Computers are loaded up with lots of information about a variety of situations where an answer is known and then the data mining software on the computer must run through that data and distill the characteristics of the data that should go into the model. Once the model is built it can then be used in similar situations where you don't know the answer. For example, say that you are the director of marketing for a telecommunications company and you'd like to acquire some new long distance phone customers. You could just randomly go out and mail coupons to the general population - just as you could randomly sail the seas looking for sunken treasure. In neither case would you achieve the results you desired and of course you have the opportunity to do much better than random - you could use your business experience stored in your database to build a model.

As the marketing director you have access to a lot of information about all of your customers: their age, sex, credit history and long distance calling usage. The good news is that you also have a lot of information about your prospective customers: their age, sex, credit history etc. Your problem is that you don't know the long distance calling usage of these prospects (since they are most likely now customers of your competition). You'd like to concentrate on those prospects who have large amounts of long distance usage. You can accomplish this by building a model.

The goal in prospecting is to make some calculated guesses about the information in the lower right hand quadrant based on the model that we build going from Customer General Information to Customer Proprietary Information. For instance, a simple model for a telecommunications company might be:

98% of my customers who make more than $60,000/year spend more than $80/month on long distance

This model could then be applied to the prospect data to try to tell something about the proprietary information that this telecommunications company does not currently have access to. With this model in hand new customers can be selectively targeted.

Test marketing is an excellent source of data for this kind of modeling. Mining the results of a test market representing a broad but relatively small sample of prospects can provide a foundation for identifying good prospects in the overall market.

If someone told you that he had a model that could predict customer usage how would you know if he really had a good model? The first thing you might try would be to ask him to apply his model to your customer base - where you already knew the answer. With data mining, the best way to accomplish this is by setting aside some of your data in a vault to isolate it from the mining process. Once the mining is complete, the results can be tested against the data held in the vault to confirm the model’s validity. If the model works, its observations should hold for the vaulted data.

An Architecture for Data Mining

To best apply these advanced techniques, they must be fully integrated with a data warehouse as well as flexible interactive business analysis tools. Many data mining tools currently operate outside of the warehouse, requiring extra steps for extracting, importing, and analyzing the data. Furthermore, when new insights require operational implementation, integration with the warehouse simplifies the application of results from data mining. The resulting analytic data warehouse can be applied to improve business processes throughout the organization, in areas such as promotional campaign management, fraud detection, new product rollout, and so on.

Profitable Applications

A wide range of companies have deployed successful applications of data mining. While early adopters of this technology have tended to be in information-intensive industries such as financial services and direct mail marketing, the technology is applicable to any company looking to leverage a large data warehouse to better manage their customer relationships. Two critical factors for success with data mining are: a large, well-integrated data warehouse and a well-defined understanding of the business process within which data mining is to be applied (such as customer prospecting, retention, campaign management, and so on).

Some successful application areas include:

  • A pharmaceutical company can analyze its recent sales force activity and their results to improve targeting of high-value physicians and determine which marketing activities will have the greatest impact in the next few months. The data needs to include competitor market activity as well as information about the local health care systems. The results can be distributed to the sales force via a wide-area network that enables the representatives to review the recommendations from the perspective of the key attributes in the decision process. The ongoing, dynamic analysis of the data warehouse allows best practices from throughout the organization to be applied in specific sales situations.
  • A credit card company can leverage its vast warehouse of customer transaction data to identify customers most likely to be interested in a new credit product. Using a small test mailing, the attributes of customers with an affinity for the product can be identified. Recent projects have indicated more than a 20-fold decrease in costs for targeted mailing campaigns over conventional approaches.
  • A diversified transportation company with a large direct sales force can apply data mining to identify the best prospects for its services. Using data mining to analyze its own customer experience, this company can build a unique segmentation identifying the attributes of high-value prospects. Applying this segmentation to a general business database such as those provided by Dun & Bradstreet can yield a prioritized list of prospects by region.
  • A large consumer package goods company can apply data mining to improve its sales process to retailers. Data from consumer panels, shipments, and competitor activity can be applied to understand the reasons for brand and store switching. Through this analysis, the manufacturer can select promotional strategies that best reach their target customer segments.

Each of these examples have a clear common ground. They leverage the knowledge about customers implicit in a data warehouse to reduce costs and improve the value of customer relationships. These organizations can now focus their efforts on the most important (profitable) customers and prospects, and design targeted marketing strategies to best reach them.

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Para pekerja merasa tidak puas dan bosan terhadap pekerjaan, demikian juga para manajer dan professional merasa pencapaian sasaran pekerjaan belum cukup terpenuhi. Keinginan dan kebutuhan individu tersebut akan memberi bentuk pada perancangan tugas yang merupakan suatu peleburan kepentingan individu ke dalam suatu kegiatan perusahaan.
Rancangan tugas harus dapat menjelaskan tentang sasaran tugas, spesifikasi, deskripsi dan pembagian tugas.
Sebelum rancangan tugas disusun secara umum telah ditetapkan jenis produk yang akan dihasilkan. Sedangkan penetapan teknologi proses harus dilakukan bersamaan dengan penyusunan rancangan tugas yaitu sebagai penunjang fleksibilitas produksi.
Rancangan tugas merupakan suatu yang rumit karena harus memenuhi criteria teknis dan criteria social. Pendekatan untuk pemenuhan criteria seperti ini dinamakan pendekatan sociotechnical.

Ada bermacam-macam pilihan teknologi yang dapat digunakan untuk mencapai tujuan yang ekonomis dan juga memenuhi criteria social. Oleh karena itu, diperlukan suatu pengembangan teori sociotechnical sebagai dasar rancangan suatu tugas.

Titik pertemuan akan membawa semua kelompok tugas yang layak dan yang akan memuaskan baik kebutuhan social maupun kebutuhan teknis.
Pendekatan sociotechnical tidak hanya untuk merancang tugas tetapi juga untuk merancang penyelenggaraan suatu organisasi secara keseluruhan.
Apabila produksi ataupun kualitas dari suatu pekerjaan menurun maka perbaikan harus dilakukan dengan cara antara lain sebagai berikut:
1. Mengganti supervise
2. Memilih pekerja
3. Menjalankan system penghargaan

Semua ini termasuk di dalam rancangan tugas yang merupakan salah satu dari mekanisme yang dapat digunakan untuk mencapai tujuan perusahaan. sehingga dengan demikian maka pendekatan sociotechnical juga akan merupakan kerangka untuk merancang penyelenggara organisasi.

Manajemen ilmiah atau scientific management merupakan metode tertua untuk merancang suatu tugas tetapi masih tetap digunakan sampai saat ini.
Apabila seseorang baru bekerja maka perkerja lain akan menjelaskan ke pekerja baru ini norma-norma kebersamaan dalam menentukan kondisi dari suatu pekerjaan.
Apabila ada pekerja yang bertindak menyimpang dari norma yang tidak tertulis ini, seringkali akan menerima hukuman dari kelompok pekerja dan harus segera mengubah sikap untuk tidak menyimpang lagi dari norma ini.
Norma kebersamaan ini dapat berupa kesepakatan para pekerja untuk membatasi jumlah produksi dengan dua alas an, yaitu:
1. Pekerja takut kehilangan pekerjaan bila produksi telah mencapai tujuan perusahaan.
2. Manajemen tidak ingin menambah pembayaran untuk setiap peningkatan produksi.

Sementara ini telah terjadi kesalahan pengertian tentang arti scientific management, di mana beberapa orang berpendapat bahwa scientific management merupakan suatu system percepatan, time study, dan penggunaan keahlian untuk meningkatkan efisiensi.
Scientific management mendapat banyak tanggapan, yaitu tentang kelemahan dalam metode atau dalam pelaksanaan, antara lain menciptakan kebosanan karena pekerjaan yang berulang-ulang, terlalu spesialis dan menganggap manusia sebagai mesin.
Di samping itu, ada anggapan bahwa Scientific management mengakibatkan orang bekerja karena mengharapkan imbalan upah.
Kritik dari para pekerja bahwa Scientific management adalah kampanye untuk bekerja lebih cepat.
Sesungguhnya Scientific management adalah suatu teori perubahan yang nyata dari sikap mental para pekerja dan manajemen. Para pekerja dan manajemen harus bekerja sama untuk menghilangkan keborosan dalam rangka meningkatkan produksi.
Peningkatan produksi akan menghasilkan peningkatan keuntungan dan demikian juga peningkatan upah yang hanya dapat diperoleh dari perbaikan metode dan tidak dengan bekerja lebih keras.
Dengan demikian maka tujuan dari Scientific management untuk meningkatkan efisiensi ekonomi pada produksi dengan jalan kerja sama antara manajemen dan pekerja.
Ada empat dasar pelaksanaan Scientific management yaitu:

1. Pemahaman tugas dengan baik terutama tentang latar belakang ilmu yang digunakan, usulan dari para pekerja harus diteliti berdasarkan pengkajian ilmu pengetahuan serta keterkaitannya dengan kebijaksaaan dari manajemen.
2. Pemilihan dan pelatihan pekerja harus didasarkan pada metode terbaru. Tanggapan pekerja terhadap tugas akan berbeda-beda satu dengan lainnya. Dengan pemilihan dan pelatihan yang tepat, diharapkan kesesuaian antara tugas dengan masing-masing pekerja.
3. Untuk lebih mendayagunakan sebuah metode maka diperlukan kerja sama para pekerja dan pihak manajemen dalam menetapkan tata cara pelaksanaan.
4. Kerjasama antara pekerja dan manajemen akan membentuk satu kelompok kerja yang akan melakukan koordinasi dalam rangka peningkatan produksi.

Saat ini ada perubahan pada penerapan Scientific management di mana tidak ada lagi tugas semata-mata sebagai hasil pertimbangan teknologi.
Tugas dibagi menjadi unsure-unsur dasar yang masing-masing unsure harus dipahami dengan seksama untuk menghindari gerakan-gerakan yang tidak berguna.
Kemudian tugas dapat disusun dari penggabungan unsure-unsur dasar ini dengan pertimbangan ekonomi, social dan teknologi.
Seorang pekerja yang mempunyai bermacam-macam keahlian dapat mengerjakan lebih banyak tugas. Sedangkan perluasan tugas dalam rangka peningkatan efisiensi total dapat dilakukan dengan metode JIT.
Suatu tugas mengandung arti penting yang meliputi antara lain: pencapaian keberhasilan, lingkup wewenang dan tanggung jawab, yang merupakan factor internal potensi kepuasan kerja. Sedangkan factor eksternal antara lain seperti: supervise, upah, dan kondisi lingkungan pekerjaan, adalah yang merupakan potensi ketidakpuasan kerja.

Kepuasan kerja dan ketidakpuasan kerja bukan merupakan dua hal yang berlawanan tetapi merupakan kondisi yang mempunyai ukuran tersendiri.
Oleh karena itu, perbaikan pada factor luar misalnya upah mungkin saja akan mengurangi ketidakpuasan kerja tetapi belum tentu meningkatkan kepuasan seorang pekerja. Kepuasan pekerja akan dapat diperoleh dengan memperbaiki factor internal seperti peningkatan motivasi, yang dapat dilakukan dengan jalan pendekatan perluasan tugas atau pendekatan job enrichment.

Job enrichment adalah memperluas rancangan tugas untuk memberi arti lebih dan memberikan kepuasan kerja dengan cara melibatkan pekerja dengan pekerjaan perencanaan, penyelenggaraan organisasi dan pengawasan pekerjaan sehingga job enrichment bertujua untuk menambah tanggung jawab dalam pengambilan keputusan, menambah hak otonomi dan wewenang merancang pekerjaan dan memperluas wawasan kerja.
Selain istilah job enrichment juga ada istilah lain yaitu job enlargement yang merupakan peragaman rancangan tugas yaitu dengan memberikan peluang kepada pekerja untuk menambah keahlian dan keterampilan dan melakukan bermacam-macam jenis pekerjaan, menghindari kebosanan dan meningkatkan gairah kerja.

Di mulai dari kolom kanan ke kiri, sebagai akibat dari job enrichment adalah output personel dan pekerjaan yang meliputi motivasi, kualitas kinerja, kepuasan kerja, dan tingkat kehadiran kerja yang tinggi.
Hal ini disebabkan karena ada kondisi psikologi yang kritis yaitu tentang:

1. Para pekerja menerima dan menyadari bahwa pekerjaan merupakan hal penting dan bernilai dari sebuah system
2. Tanggung jawab pekerja akan memberikan hasil pekerjaan yang baik
3. Para pekerja harus dapat mengetahui apakah hasil pekerjaannya sudah memuaskan

Ketiga kondisi psikologis yang kritis tersebut di atas disebabkan karena lima dimensi tugas utama yang tercakup dalam arti penting sebuah pekerjaan antara lain:
1. Perluasan keahlian untuk peragaman tugas, yang akan mengurangi kebosanan dan memberikan peluang untuk rotasi tugas atau skill variety
2. Pemahaman tugas akan memberikan efisiensi dan kualitas kerja atau task identity
3. Arti penting tugas bahwa pekerja mengetahui hasil kerja yang diinginkan oleh pelanggan atau task significance
4. Otonomi merupakan pelimpahan tanggung jawab kepada pekerja untuk memutuskan sesuatu yang menyagkut penjadwalan kerj dan melakukan penyesuaian apabila ada penyimpangan.
5. Umpan balik atau feed back memberikan informasi kepada para pekerja tentang hasil pekerjaan sehingga para pekerja dapat segera memperbaiki kualitas dan kinerja pekerjaan.

Kelima dimensi tugas utama dapat digabungkan menjadi suatu potensi yang dapat diukur melalui nilai potensi motivasi atau motivating potential score (MPS)
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Just In Time


JIT is a collection of concepts and techniques for improving productivity. JIT is a process aimed at increasing value-added and eliminating waste by providing the environment to perfect and simplify the processes.
Just-in-time manufacturing means producing the necessary items in necessary quantities at the necessary time. It is a philosophy of continuous improvement in which non-value-adding activities (or wastes) are identified and removed.

Putting this concept into practice means a reversal of the traditional thinking process. In conventional production processes, units are transported to the next production stage as soon as they are ready. In JIT, each stage is required to go back to the previous stage to pick up the exact number of units needed.


Reduced operating costs

Greater performance and throughput

Higher quality

Improved delivery

Increased flexibility and innovativeness

JIT Components:

Production Leveling

Pull System

Kamban (label or signboard) system

Good Housekeeping

Small Lot Production

Setup Time Reduction

Total Preventive Maintenance (TPM)

Total Quality Control (TQC)

JIT Purchasing

Line Balancing

Flexible Manufacturing

Small-group Activities (SGA)

Taken from:
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Kaizen Strategy, and Implementation in Toyota


Kaizen means "improvement". Kaizen strategy calls for never-ending efforts for improvement involving everyone in the organization – managers and workers alike. As we know that Management has two major components:

1. maintenance, and

2. improvement.

The objective of the maintenance function is to maintain current technological, managerial, and operating standards. The improvement function is aimed at improving current standards.

Under the maintenance function, the management must first establish policies, rules, directives and standard operating procedures (SOPs) and then work towards ensuring that everybody follows SOP. The latter is achieved through a combination of discipline and human resource development measures.

Under the improvement function, management works continuously towards revising the current standards, once they have been mastered, and establishing higher ones. Improvement can be broken down between innovation and Kaizen. Innovation involves a drastic improvement in the existing process and requires large investments. Kaizen signifies small improvements as a result of coordinated continuous efforts by all employees.
Implementation of Kaizen Strategy: 7 Conditions

One of the most difficult aspects of introducing and implementing Kaizen strategy is assuring its continuity.

When a company introduces something new, such as quality circles, or total quality management (TQM), it experiences some initial success, but soon such success disappear like fireworks on summer night and after a while nothing is left, and management keeps looking for a new flavor of the month.

This if because the company lacks the first three most important conditions for the successful introduction and implementation of Kaizen strategy... More

Process-Oriented Thinking vs. Result-Oriented Thinking

Kaizen concentrates at improving the process rather than at achieving certain results. Such managerial attitudes and process thinking make a major difference in how an organization masters change and achieves improvements.

Quick and Easy Kaizen

Quick and Easy Kaizen (or Mini-Kaizen) is aimed at increasing productivity, quality, and worker satisfaction, all from a very grassroots level. Every company employee is encouraged to come up with ideas – however small – that could improve his/her particular job activity, job environment or any company process for that matter. The employees are also encouraged to implement their ideas as small changes can be done by the worker him or herself with very little investment of time.

Quick and easy Kaizen helps eliminate or reduce wastes, promotes personal growth of employees and the company, provides guidance for employees, and serves as a barometer of leadership. Each kaizen may be small, but the cumulative effect is tremendous.
I would like to preview the application of Kaizen in Toyota. this is the 7 principle in their production system. they are:

1. Reduced Setup Times:
All setup practices are wasteful because they add no value and they tie up labor and equipment. By organizing procedures, using carts, and training workers to do their own setups, Toyota managed to slash setup times from months to hours and sometimes even minutes.

2. Small-Lot Production: Producing things in large batches results in huge setup costs, high capital cost of high-speed dedicated machinery, larger inventories, extended lead times, and larger defect costs. Because Toyota has found the way to make setups short and inexpensive, it became possible for them to economically produce a variety of things in small quantities.

3. Employee Involvement and Empowerment:
Toyota organized their workers by forming teams and gave them the responsibility and training to do many specialized tasks. Teams are also given responsibility for housekeeping and minor equipment repair. Each team has a leader who also works as one of them on the line.

4. Quality at the Source: To eliminate product defects, they must be discovered and corrected as soon as possible. Since workers are at the best position to discover a defect and to immediately fix it, they are assigned this responsibility. If a defect cannot be readily fixed, any worker can halt the entire line by pulling a cord (called Jidoka).

5. Equipment Maintenance: Toyota operators are assigned primary responsibility for basic maintenance since they are in the best position to defect signs of malfunctions. Maintenance specialists diagnose and fix only complex problems, improve the performance of equipment, and train workers in maintenance.

6. Pull Production:
To reduce inventory holding costs and lead times, Toyota developed the pull production method wherein the quantity of work performed at each stage of the process is dictated solely by demand for materials from the immediate next stage. The Kamban scheme coordinates the flow of small containers of materials between stages. This is where the term Just-in-Time (JIT) originated.

7. Supplier Involvement: Toyota treats its suppliers as partners, as integral elements of Toyota Production System (TPS). Suppliers are trained in ways to reduce setup times, inventories, defects, machine breakdowns etc., and take responsibility to deliver their best possible parts.
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Masalah yang menonjol di dalam layout fungsipnal adalah bagaimana mengangkut barang-barang di dalam proses produksi dari sati bagian ke bagian yang lain sehingga proses produksi tidak terganggu akibat terlambatnya barang-barang atau bahan-bahan yang akan diproses di suatu bagian tertentu. Masalah ini merupakan masalah material handling atau pengendalian material. Material handling adalah suatu seni dan ilmu untuk memindahkan, membungkus, dan menyimpan bahan-bahan dalam segala bentuk (B.K. Hedge, 1972)

Tujuan dari pemindahan bahan adalah mencapai pemndahan bahan-bahan yang tertib teratur dengan memenuhi syarat-syarat yang telah disebut di muka, dan yang lebih penting lagi adalah bahwa tujuan tersebut dapat dicapai dengan biaya yang rendah.

Penurunan biaya material handling dapat diusahakan dengan cara:

1. Pengurangan jumlah dan jarak pengangkutan. Hal ini dapat ditempuh dengan mengadakan perubahan terhadap layout.

2. Pengurangan waktu yang dibuthkan di dalam pengangkutan bahan. Hal ini dapat dicapai dengan mengurangi atau menghilangkan sama sekali waktu-waktu menunggu (waiting time). Dengan melakukan penghematan terhadapwaktu maka akan terdapat penghematan berbagai macam biaya disampung itu jadwak waktupun dapat dipercepat. Penghematan waktu berarti pula pemanfaatan alat-alat material handling secara lebih efektif.

3. Pemilihan alat pengangkutan bahan yang tepat Alat-alat pengangkutan bahan harus dipilih agar biaya operasional dan biaya modalnya minimum, terdapat keluwesan yang tinggi dalam pengangkutan bahan-bahan memiliki tingkat keselamatan yang tinggi, dan sebagainya.

Alat-alat material handling ada beberapa macam antara lain:

1. Bagi pabrik yang masih memiliki ruangan –ruangan yang cukup lebar maka dapat dipergunakan:

a) Prahoto

b) Traktor

c) Lori-lori kecil

d) Truk pengangkut (fork lift truck)

2. Bagi pabrik yang memiliki ruangan-ruangan yang terbatas dapat menggunakan:

a) Ban berjalan (conveyors)

b) Elevator (lift)

c) Derek (cranes)

Pada banyak perusahaan biasanya para pekerja sendiri yang mengangkut bahan-bahan yang diprosesnya dari satu tempat ke tempat lain sehingga hal ini mempengaruhi produktivitas kerja mereka, waktu untuk melayani mesin berkurang, mengakibatkan kelelahan dan sebagainya. Oleh karena itu seyogyanya material handling dalam pabrik perlu dipikirkan lebih lanjut, misalnya dengan penyediaan peralatan materials handling secukupnya sehingga tidak mengganggu kelancaran proses produksi.


Di dalam perencanaan materials handling beberapa unsure perlu diperhatikan:

Produk, macam/jenisnya: berat, ringan, cair, padat, kecil, dan seterusnya. Ini menentukan sekaligus pemilihan alatalat material handling.

Dari mana ke mana bahan dipindah-pindahkan: relative dekat, atau jauh.

Keadaan ruangan: cukup luas/sempit: atap: tinggi/rendah.

Bentuk gedung: datar, bertingkat.

Dana yang tersedia untuk pembelian/penyewaan alat-alat material handling. Perlu pengambilan keputusan ekonomis investasi pada aktiva tetap: kegunaannya, penghematan jangka pangjang yang diakibatkan oleh penggunaan fasilitas tersebut. Perlu dipertimbangkan pula kemungkinan-kemungkinan perkembangan baru penggunaan alat-alat material handling, putusan penggantian, dan lain-lain.


Urusan pengendalian bahan seyogyanya dispesialisasikan; paling tidak di bawah bagian produksi, atau teknik atau kogistik. Seperti halnya di PT. Semen Gresik, alat-alat besar ada di bawah bagian teknik, diproyek Karangkates ada di bawah bagian logistic. Seksi material handling merupakan seksi yang melayani kebutuhan bagian lainnya, terutama bagian produksi sehingga dapat meminimumkan biaya. Ini harus mendasarkan aktivitasnya pada apa yang disebut dasar UNIT LOADS, yaitu makin banyak satuan barang/beratnya barang yang dipindahkan dalam suatu kegiatan pengendalian bahan, makin rendahlah biaya tiap satuan/tiaap satuan berat dan makin pendeklah waktu yang diperlukan untuk memindahkan suatu volume tertentu.


Berbagai alat pengendalian bahan berbeda dalam harga, keuntungan dan kelemahan. Pimpinan tinggal memilih alat mana yang akan dipakai dalam perusahaannya dan ini merupakan putusan mengadakan investasi pada aktiva tetap. Criteria mengadakan investasi (dengan metode nilai sekarang dan hasil kembali investasi) dalam hal ini perlu diterapkan. Bagaimanapun, investasi pada alat pengendali bahan relative mahal. Perlu pertimbangan masak-masak demi efisiensi sekarang dan masa depan.

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