Analysis of Management Strategies for the Aircraft Production Ramp-up

Problem:




New product ramp-up in the aircraft industry is a complex process due to the high number of changes that happen both in product and production processes at the manufacturing stage, which can increase aircraft lead time dramatically. Also, aircrafts are complex, small series products that are highly customized for specific customer needs, which makes launching a new product even more difficult. Today, ramp-ups have become more frequent as the average product lifecycle decreases. All of these issues can make ramp-ups a major challenge for aircraft industry production engineers.


The Airbus Group joined the European Union ARUM (Adaptive Production Management) project, which is focused on creating an IT solution for risk reduction, decision-making, and planning during new product ramp-ups. The project is aimed mainly at aircraft and shipbuilding industries.


Simulation was chosen as a part of the ARUM solution, because it would allow the participants to reproduce the real production facility experience (based on the use case provided by the Airbus Group), and thus provide a benchmark for the ARUM solution testing. AnyLogic was the selected simulation software due to its capability to combine agent-based models with discrete-event approach.


Solution:




The simulation model included a part of the Hamburg Airbus A350 assembly line where two different pieces of the fuselage were completed. This part of the line consisted of six assembly stations with 30-35 people working at each one and approximately 300 work orders per station. The challenge was to simulate the ramp-up process where general productivity increased over time, with the whole ramp-up period lasting up to two years.


The agent-based and discrete-event model consisted of three types of elements:


  • The flow line, that included working stations, each one with its own labor and physical resources. The stations were modeled as agents.  
  • The products (sections), going through the flow line parts. Each section required 200-600 work orders assigned to stations. Work orders formed tasks that required specific materials and resources. When a section entered a station, it started going through work processes modeled using the Process Modeling Library, then left and moved to the next station, and finally, to the assembly line in a different city, which was not modeled. 
  • The control model included plans that were sometimes affected by disturbances. The controller agent modeled the complex behavior of human managers reacting to disturbance events with control strategies.
Simulation based solution architecture

ARUM solution structure.


Among others, the control strategies included open work policy alternatives. This meant that if some of the work could not be done at the moment, it could be delayed until some other point, while the product continued to move beyond this assembly line to the facility in the other city. In this case, Hamburg workers would have to travel to the other facility to complete the work (“traveling work” strategy). Alternatively, the work could be suspended until the disturbance was resolved (“stop and fix” strategy).


The disturbances that occurred during the ramp-up included:

  • Unbalanced workload and resource allocation due to the workers’ learning curve and the fact that the same line produced several different products. 
  • Design non-conformities and changes, as production often began with a not completely prepared product. 
  • Missing material or material incompatibilities due to late design changes.

The measured model statistics included achieved aircraft lead time, amount of traveling work used, and resource utilization rates (labor, materials, and stations).


The experts created a model that was easy to understand and reuse, and that was integrated to the ARUM solution architecture. It also included the visualization of the assembly line.

Production simulation model structure
Simulation model structure. 

Outcome:




The model was run to simulate the impact of the disturbance mitigation strategies currently being applied at the Airbus facility, including “stop-and-fix” and “traveling work” strategies.


The modelers tested multiple ramp-up scenarios with different sets of production plans. They also tried multiple sets of disturbances based on historic data, including extreme scenarios.


The model will be used for comparing plans suggested by the ARUM suite to the current management practices. This will allow development of the best disturbance mitigation strategies for aerospace and shipbuilding manufacturing industries’ ramp-ups.

More Case Studies

  • 自動生産ラインのプランニングおよび最適化
    ドイツCentrotherm Photovoltaics AG 社は、太陽光発電、半導体およびマイクロエレクトロニクス業界への、技術および設備を提供するグローバルサプライヤーです。同社は、コストを最小限に抑え、スループットと信頼性を最大限にするため、最良の自動化生産ラインおよび工場構成を確認する必要がありました。
  • GE Manufacturing Plant Uses AnyLogic for Real Time Decision Support
    In 2012, GE opened a new battery manufacturing plant in conjunction with the launch of an innovative energy storage business. GE’s exciting opportunity brought on many new challenges, such as increasing production throughput and yield under evolving processes and uncertainties, and reducing manufacturing costs in order to gain market share. The GE Global Research Center sought out a powerful and flexible tool to analyze, not just the specific process, but the manufacturing system as a whole.
  • AnyLogicでキャパシティー分析
    NASSCOは、現在の生産および可能性のある新しい仕事の両方に関して、きわめて詳細でかつ正確なキャパシティー分析を提供するために、The Large Scale Computer Simulation Modeling System for Shipbuilding (LSMSe)と呼ばれる特注の分析システムを利用します。
  • トンネル掘削機でのトンネル建設シミュレーション
    トンネル掘削機の休止による1時間のコストは、通常高いのが実情です。また、プロジェクト・マネージャーは、工事現場において作業遅れを回避するために最善を尽くさなければなりません。 Ruhr University Bochum in Germanyで開発されたシミュレーション・プロジェクトの目標は、可能性のある金銭上のロスを最小化するために、トンネル建設プロセスでのボトルネックを究明することができるシミュレーション・モデルを作成することでした。
  • 船舶産業における生産計画
    イタリアは歴史的に、世界に誇るヨットやスーパーヨットを生産す る国として知られています。多数の有名なブランドを市場に浸 透させるために、原価管理とライトサイジングは製品とプロセス・ イノベーションと同様に重要です。豪華ヨットの製造工程は複 雑であり、また完成品と技量の質は落とすことができません。 ヨットの製造工程は膨大な時間および労働力を必要とし、多く の生産タスク、非常に熟練した手仕事が必要です。
  • 造船所はオーダー履行能力を検証するため、生産設備やその分布を可視化
    アドミラルティ造船所(Admiralty Shipyards JSC)は、ディーゼル潜水艦の大口注文に直面し、現在の生産設備で受注に十分対応できるか、できない場合は2016年までにどれだけ生産できるかを評価しなければなりませんでした。アドミラルティ造船所JSCはまた、受注に対応するために新たな生産設備が必要になるかを検証します。
  • アイスクリーム製造 シミュレート:制限認識および生産計画の最適化
    ウルグアイで最大手の乳製品製造会社Conaprole社は、生産ライン5本を稼働させ、ラインごとに異なる5種類の包装形態で、150SKU(Stock Keeping Unit)種類を超える商品を生産します。需要と供給のバランスを保つために彼らの計画を再構築して重要な商品の在庫切れを回避することが経営陣の挑戦になっていました。さらに、生産設備利用の最適化も求めていました。
  • Simple Simulation Model Helps Intel Avoid Production Plant Downtime
    Intel factories used a particular type of equipment that often broke down, which caused capacity constraints. These expensive parts were used in critical factory operations, and the repairs took significant time, so it was necessary to have extra spare parts on hand to avoid downtimes. Broken parts caused constraints at some of the factories while other factories over purchased spares.
  • Modeling the Cladding Leak Detection Shop of a Nuclear Reactor's Module
    The cladding leak detection shop is part of an automated line of fuel assembly production. Leak control is based on heating the fuel element groups. While warming up, defective units eject the control gas, which is detected by a leak locator. The defected group is divided into two parts. Each part is screened in the same way until the leaking fuel element is found.
  • Improving Mining Outbound Logistics with Agent-Based Simulation Modeling
    One of the largest resource companies in the world, with over $80 billion in sales, decided to enter a new market. It was planning to build a new potash mine with 90% of the resources exported. They wanted to design a reliable supply chain, with a high speed of supply replenishing, and the ability to recover from natural disasters and man-made crises benefiting from such volatility. Amalgama and Goldratt companies contracted this project to design the potash mining operations and a full supply chain of outbound logistics.