System Integration

Executive focus on daily production targets, quality requirements and legal issues sometimes cause important parameters to escape attention.

For instance, an equipment or process line purchased with a significant financial undertaking may not receive the deserved analyses by responsible personnel regarding efficiency, capacity utilization, costs-benefits and ROI. When the data sets that have to be collected from the field and KPI’s to make comparisons upon are not identified correctly, plants run the risk of efficiency loss and operational cost increases.

Technical specs and theoretical operating limits of operational assets are the most significant of these KPI’s. Additionally, analyzing the competition can give other benchmark values such as production count, overall equipment effectiveness, cycle times, new product development time, traceability and production flexibility.

Gathering the right data, pooling these into a single source and reshaping them into “Useful Information” through “Big Data Analytics” can shed light on dark spots previously unseen by line managers.

All the parameters that need to be kept in check can be compared with past process data by Statistical Process Control (SPC) methodologies, root-cause analyses can be carried out when deviations occur, therefore valuable insights for development projects can be attained in light of “Useful Information”.

All gathered data may be utilized to make trend and statistical analyses compliant to managament systems of choice like 6 Sigma, Lean Manufacturing, Just in Time (JIT), Total Productive Maintenance (TPM), Total Quality Management (TQM), etc. Imagine performance reports are issued instantly, media-independent on cell phones-tablets-desktops, in a personalized and automatic way… This is Manufacturing Intelligence.

A facility that has manufacturing intelligence can adapt to fast changing markets and meet customer demands, put in place  flexible production structures and ideally use human-material-time resources.

Faster, higher quality, more flexible and more efficient production systems are made possible by Industry 4.0 Manufacturing Intelligence Solutions.

Machines and equipment are the main operational assets, as well as the most significant investment items. Every asset purchased to improve processes, use resources more efficiently and cut costs are investments made to the future.

Is Asset Performance Measured Correctly?

Following up on returns on investment (ROI) is the key approach behind management by objectives (MBO). Even in modern facilities with sophisticated manufacturing infrastructures, it is possible to see serious gaps in asset management.

  • Beklenmedik duruşlarının kök nedenleri analiz edilerek, Kaizen felsefesiyle sürekli geliştirme faaliyetleri yapılabilir. Gerçek veriler ile yatırım ve fizibilite analizleri kıyaslanarak riskler ve fırsatlar doğru olarak tespit edilir ve gereksiz masraftan kaçınılır,
  • Kestirimci Bakım yönetimi hayata geçirilerek ekipmanlar arıza yapmadan önce bakım planlaması yapılır, işçilik-parça-zaman kaynakları etkin bir şekilde kullanılır,
  • Makina ve ekipmanın performans, test ve devreye alınma verileri ile teknik spesifikasyonlar tek bir veritabanında saklanarak ana performans kriterleri (KPI) olarak belirlenir, proseslerde bu değerlerden sapmalar zamanında anlık olarak raporlanarak düzeltici ve önleyici faaliyetler koordine edilir,

  • Operatör ve teknisyenlerin bazen «meslek sırrı» olarak sakladıkları tecrübe ve bilgi birikimine muhtaç kalmak yerine, işletmenin hafızasını oluşturan «En İyi Pratikler» temel alınır,
  • Yedek parça ihtiyaçları stoklarla karşılaştırılarak yöneticilere zamanında uyarılar gönderilir, böylece beklenmedik durumlarda her zaman gerekli parçanın stokta olması sağlanır. Satınalma ve lojistik ile eşzamanlı planlama yapılarak dorğu bir finans ve zaman yönetimi yapılır,
  • İş Sağlığı ve Güvenliği sürecine girdi teşkil edecek değerli veriler sahadan anlık ve tam olarak toplanır,
  • Amortisman, garanti, kiralama süreleri takip edilerek finansal planlama etkin bir şekilde yapılır.

Manual data entry into simple forms and spreadsheets – if that much is already done – is not enough for effective asset management. Thousands of bits of data generated instantly in processes by machines and equipment can only be analyzed correctly and in a meaningful way by software dedicated to Asset Management, belonging under a comprehensive IT infrastructure. Asset management is especially vital to Asset-intensive industries like energy, mining, aviation, transportation, chemicals, paper, metals and steel.

By effective and efficient asset management;

Unexpected downtime can be analyzed for root-causes and continous improvement activities can be implemented with the Kaizen philosophy,

Real-time data can be fed as inputs to investment and feasibility due-diligence, thus risks and opportunities can be correctly identified, while unnecessary expenditure is avoided,

Through predictive/preventive maintenance practices, equipment can diagnosed and maintained before downtime occurs, labour-materials-time resources can be utilized effectively,

Maintenance function can be handled under asset management methodology, but deserves a seperate focus due to its significance in production.

Downtime in the simplest of equipment may sometimes cause serious production loss, quality issues or worsely, have health and safety consequences. A melting furnace stoppage because of a clogged valve or production loss on a refinery process due to a pump disorder are unacceptable occasions for modern facilities. Disorders with low probability but high impact on production or OHS have to be managed under an effective maintenance system.

Maintenance managers and supervisors have some tools at hand to use seperately or in coordination for this purpose.

Predictive Maintenance, on the other hand, is the practice of using condition based monitoring techniques to make accurate forecasts for disorders and maintaining equipment only when disorder is imminent. Techniques like non-destructive testing (NDT), vibration and noise monitoring, oil analysis, ultrasonic inspection, temperature measurement with thermal camera, acoustic leakage monitoring, shaft alignment with laser, current-voltage monitoring and many others can be implemented to give field managers the opportunity to instantly check critical equipment and carry out effective maintenance planning.

Predictive Maintenance requires more investment and effort to manage compared to Preventive Maintenance, however if applied properly offers potential for more effective asset management and achieve more uptime.

As a strategic operational function in modern facilities, maintenance teams utilize both practices according to necessities in order to reduce downtime, spare part counts and inventory costs, while maximizing overall equipment effectiveness (OEE). In this way total cost of ownership (TCO) for critical and expensive equipment is minimized.

All this maintenance planning and equipment performance monitoring – if done at all – is impossible with manual data entry into simple forms and spreadsheets. Big Data Analytics software customized for maintenance managers presents efficient labour-materials-time resource planning tools and generates automated reports evaluating maintenance effectiveness. Equipment and process based comparisons enable root-cause analyses which constitute vital input for investment planning, continuous improvement (Kaizen) and spare parts management.

Planning and organizing Predictive and Preventive Maintenance activities with specialized Industry 4.0 software provide significant value to production facilities.

Energy and resources constitute some of the largest expenditure items on production facilities’ daily operations. This is especially true for energy intensive sectors where mechanization and automation are utilized extensively.

Energy Efficiency Act of 2007 has made it mandatory to employ an energy manager for facilities with annual energy usage over 1000 BOE (barrel of oil equivalent) in Turkey – (1 BOE : 10 million kCal, approx. 11600 kWh)

Inspection of energy consumption and prevention of waste is a prominent area of achieving fastest payback. An effective energy management practice may facilitate significant savings especially in production plants with various machines, processes and production lines.

Beyond the need for legal compliance, facility managers shall consider energy management in a strategic point of view. Their leadership in action planning and implementation plays an important role in successful practices.

Energy and resources can be classified under 5 main groups

  • Electricity
  • Water
  • Air
  • Gas
  • Steam

Each of these resources require individual engineering effort and methodologic approach. There is extensive literature about Best Practices for all of these resources.

As the ancient but wise saying goes, “You can’t improve what you don’t measure”. In order to effectively and efficiently use resources, first step requires the detailed identification of consumption points and their weights in the aggregate usage.

Traditionally site personnel make periodic patrolling and manually enter meter data into shift notebooks, which are seldomly, if any at all, inspected. This method of data collection obviously doesn’t lead to past-to-present comparisons, correlations between production and consumption of resources or trend analyses. In many facilities it is possible to see that even manual bookkeeping is done incompletely.

Industry 4.0 envisions accurate site data gathering, analysis and delivery of personalized comprehensive reports to line managers. Thus, waste points can be identified easily, improvements and investment planning can be done effectively based on actual and up-to-date field data.

Components and sensors need to be in place for each significant point for data gathering. Therefore, the software to be used for reporting must be flexible and scalable to make way for future machines, processes and production lines.

Big Data Analytics Software customized for Energy and Resources Management offer investments with fast payback and major reductions in utilities bills.

Product Lifecycle is a very important managerial concept encompassing the whole timeframe from when a product idea is conceived till its obsolescence. In this respect it is relevant to design, production, maintenance, quality, logistics, sales and marketing processes. Effective PLM practices convey multi-faceted mission-critical information, under whose light companies may offer value-added, innovative, high quality and low cost products and services to their customers.

There are 5 stages in a conventional PLM process :

  • Fikir
  • Idea
  • Product Design
    • (CAD – CAM)
  • Planning
  • Production
  • Services

For the successful implementation of PLM, all relevant data for each point must be fed back into the previous stage and iterations made at all stages shall lead to improved products and services with extra features.

A comprehensive PLM software orchestrates changes made in CAD-CAM sheets to be automatically updated in all relevant documents and served to all stakeholders. Revisions and changes are traditionally carried out manually which costs valuable time and effort especially for companies with large product portfolios.

Enter Industry 4.0. IoT architectures in manufacturing environments envision automatic retrieval of data from the field, while necessary actions and planning arising from updated PLM information will be automatically organized with the power of Big Data Analytics enabled PLM software.

Once PLM foundation is established, responsible personnel won’t need to have software knowledge to create user-defined reports and rule-based processes. With such articulated infrastructure it will be possible to :

  • Optimize product design towards minimal materials, energy and labour usage in manufacturing, with reference to instantaneous data gathered from all machines and equipment on the field,
  • Analyze Bills-of-Material (BOM), Manufacturing    Bills-of-Material (MBOM), design, procurement and service process data to meet customer demands,
  • Simulate the effect of design changes on product quality and customer experience, calculate the impact of quality enhancements on unit manufacturing costs,
  • Analyze at which PLM stages revisions need to be made to meet sudden customer demand changes or ramp up production, identify the best alternatives in terms of cost-benefit analyses.

All these analyses require instantaneous collection of plant floor data and unified in a common database. All PLM revisions pertaining to design, manufacturing, planning and logistics must be automatically updated for and presented to line managers and relevant personnel for shared access.

Product development processes can be geared towards creating a competitive advantage by implementing a PLM infrastructure under the Industry 4.0 vision that enables innovative, value-added, fast and flexible manufacturing units.

Thanks to globalization and the digital revolution, companies are able to reach customers all over the world. This brings many corporate opportunities on the condition of effectively managing daily operations to satisfy different customer demands with various product portfolios. Especially the need to optimize customer satisfaction, quality and costs while taking into account numerous production planning and supply chain parameters has made the usage of complex business applications and seamless cooperation between business units mandatory.

Products in the automotive, aviation, consumer electronics, durable goods and similar sectors have evolved into assemblies that have on occasions thousands of sub-assemblies and components. Markets are moving very fast with short product lifecycles. Bills-of-Material (BOM) and Manufacturing Bills-of-Material (MBOM) are composed of parts with various technical specifications supplied by various OEMs. For this reason, all raw materials, components, parts and sub-assemblies that make up the finished product have to be brought together with a clinical precision to meet customer demands and quality requirements.

Manufacturing products with the right quality, at the right amounts, with minimal inventory and costs in today’s dynamic markets is only possible with a complete traceability of all processes and a seamless coordination between them.

Traceability becomes very important especially in situations of recalls for mass produced items. Only when traceability is achieved with respect to production lot, line, shift and operator that defective products can be identified precisely and excessive costs are prevented.

Purchasing and production planning optimization can be carried out if raw materials procurement and product recipes are effectively managed. Short-medium-long term management decisions regarding supply chain channels, investment and operations planning decisions can be ideally taken in light of instant and accurate field data.

Traceability infrastructure has to be configured together with dedicated Big Data Analytics software. In this manner technical specifications, acceptance criteria, supply details, product recipes and processes can be aggregated and mapped with respect to supplier, lot, production period, customer and factory. Queries and analyses in changing scenarios, together with supply chain optimization algorithms lend valuable inputs for strategic planning.

Another very important output of effective traceability are the insights gained in time to be utilized as inputs for continuous improvement (Kaizen) efforts. With the right infrastructure, Manufacturing Intelligence oriented real-time data gathering results in “Useful Information” for unit managers and decision makers.

Big Data Analytics Software customized for raw materials and recipe management offer valuable opportunities for process improvements through harmonizing the supply chain and production.

On the road to Industry 4.0, software solutions that facilitate process based monitoring offer managers a bird’s eye view of the production facility. The integration of manufacturing, maintenance, asset management, quality and other related business units enables wide access to data and opportunities for more efficient teamwork across the organization. However, in order to complete the chain and see the Big Picture, business units pertaining to production also have to be unified with finance, purchasing, supply chain, human resources, sales and marketing to create a common data management infrastructure.

Latter units are manager by ERP software in many companies, although in most cases many different additional software run alongside them which create information silos that are not cross-accessable. Usage of different databases cause inefficiencies in resource pooling and dark spots due to incomplete analyses.

Industry 4.0 envisions not only process integration on the plant floor, but full business integration including traditional HQ business units.

In addition to consolidating all data coming from components and systems that use different communication and operating protocols in a common database, the unification and cross correlation of these data with ERP data will give rise to unprecedented efficiencies in planning and operations. IT and OT merger will add significant value to companies.

For instance, if after calculations made by real-time data shows that meeting demand in a market traditionally supplied by a certain factory turns out to be more economical by switching to products produced by another factory, then the decision to make this switch can be taken fast and confidently. If a problem effecting the supply chain or a change in customer expectations forces a fast reaction, short term procurement of raw materials or unfinished goods can be organized with data-driven decisions. Or if an alarming situation in customer satisfaction or service level point to an investment necessity, additional labour or machinery can be transferred from another factory with unfilled capacity utilization, thus easing the hand of decision takers. These operation-critical decisions require agility. However, complex calculations made with multiple variables and incomplete data may lead companies to faulty strategies and decisions.