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 

24.05.2020

Kadıköy, İstanbul – TURKEY

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

https://www.linkedin.com/in/mehmet-temel-aygun-1066a514/

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