ブログ

修理可能なスペアパーツの資産管理最適化


修理可能なスペアパーツの資産管理最適化

ITC Infotechは、複雑な資産集約型業界での在庫管理を最適化するための作業に着手しました。シミュレーション、機械学習、最適化を組み合わせることで、サービスレベルと在庫コストのバランスをとることで、循環/修理可能(rotable/repairable)なスペアパーツの効果的な資産管理と在庫最適化を実証しました。

生産最適化ソフトウェアを使用した鉄鋼製造ユニットのスループット向上


生産最適化ソフトウェアを使用した鉄鋼製造ユニットのスループット向上

Tata Steelは、世界最大の鉄鋼メーカーおよびサプライヤーの1つとして、鉄鋼製造ユニットの内部ロジスティクスを最適化する必要性に直面していました。同社は、AnyLogicを生産最適化ソフトウェアとして使用することで、ユニットの全体的なスループットを向上させる可能性があると考えました。

制限エリア:トランスポーターのアクセスを制御する方法(パート6)


制限エリア:トランスポーターのアクセスを制御する方法(パート6)

AnyLogic 8.6アップデートで追加されたマテリアルハンドリングライブラリに関連する重要な機能は、AGVなどのトランスポーターの移動するエリアを制限でき、アクセスは条件付きで許可することができます:トランスポーター番号、スケジュール、スループット等。

このテクニカルブログでは、これらの制限エリアの使用方法を説明し、実用的なサンプルモデルであるAreas with Limited Access for Transporters(トランスポーターのアクセスが制限されたエリア)使用して学習します。

How a digital twin can help decision making


How a digital twin can help decision making

Digital twins are part of Industry 4.0. How are they being used on automobile production lines? Here we investigate how a digital twin is made and used in the automotive industry.

One of the world’s largest capital goods companies, CNHi, wanted to evaluate Industry 4.0 technologies. It chose its IVECO production plant in Suzzara, Italy. Fair Dynamics were contracted and proposed a digital twin. Read on and discover how maintenance costs can be reduced with a digital twin — Production line downtime was shown in a study of over 100 automotive executives to cost an average of $22,000 per minute.

The development of Industry 4.0 in Germany: an interview with Yuri Toluyev


The development of Industry 4.0 in Germany: an interview with Yuri Toluyev

Industry 4.0 was the subject of Yuri Toluyev’s plenary presentation at the IMMOD simulation modeling conference in Saint Petersburg. He described how new approaches to enterprise development, united by the concept of Industry 4.0, are driving technological development.

AnyLogic attended the event and interviewed Toluyev about Industry 4.0, its development, and its implementation. Interesting and informative, read on for Yuri Toluyev’s Industry 4.0 insight...

Ford Motor Company Selects AnyLogic for their Simulation and Modeling Technology


Ford Motor Company Selects AnyLogic for their Simulation and Modeling Technology

We are excited to announce Ford Motor Company's recent selection of AnyLogic for their simulation and modeling needs. Ford Motor Company's substantial analytics team was looking for simulation modeling technology that goes above and beyond discrete-event only tools. The analytics team working with AnyLogic will provide decision-making and problem-solving techniques for multiple industries inside Ford, such as manufacturing, finance, and supply chain. AnyLogic is proud to support Ford Motor Company. Check out the press release announced on a variety media outlets.

anyLogistix Version 2.0, Released!


anyLogistix Version 2.0, Released!

We have released the second version of anyLogistix - a new tool for optimizing supply chains and logistics networks. What is anyLogistix? anyLogistix (ALX) - the only multimethod software for supply chain optimization. ALX combines traditional analytical methods of optimization and innovative simulation technology. The combination of different technologies allows you to model and analyze the supply chain, at any level of detail, therefore, finding more effective ways to improve. Who is anyLogistix made for? Companies with large and complex supply chains (i.e. manufacturers, distributors, retailers and logistics providers) can take advantage of anyLogistix. Implementation of ALX is carried out by our partners,consulting companies.

Automated Production Line Optimization: Case Study


Automated Production Line Optimization: Case Study

Centrotherm Photovoltaics AG is a global supplier of technology and equipment for the photovoltaics, semiconductor, and microelectronics industries. The company needed to identify the best configuration of the automated production line and factory, to minimize costs and maximize throughput and reliability. Throughput and equipment utilization rate metrics were utilized to compare alternatives. Centrotherm needed to avoid possible bottlenecks in the material flow and optimize in-factory logistics. Also, management required taking into account casualties and stochasticity, for instance, the probability of scrap or how the factory would operate in case of equipment breakdowns.

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