414 research outputs found

    INTERMITTENT DEMAND FORECASTING USING DATA MINING TECHNIQUES

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    Intermittent demand occurs randomly with changing values and a lot of periods having zero demand. Ad hoc intermittent demand forecasting techniques have been developed which take special intermittent demand characteristics into account. Besides traditional techniques and specialized methods, data mining offers a better alternative for intermittent demand forecasting since data mining methods are powerful techniques. This study contributes to the current literature by showing the benefit of using data mining methods for intermittent demand forecasting purpose by comprising mostly used data mining methods

    Analisis Kinerja Peramalan dan Klasifikasi Permintaan Auto Parts Berbasis Data Mining

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    Kontribusi after sales service yang besar terhadap profit dan pertumbuhan bisnis menyebabkan spare part management menjadi faktor yang penting untuk bersaing di pasar. Termasuk bagi PT. X, produsen mobil penumpang dengan market share terbesar secara global di tahun 2019. Berbagai upaya spare part management telah dilakukan PT. X untuk memenuhi kebutuhan part domestik maupun ekspor. Salah satu upayanya adalah melakukan perencanaan produksi bulanan (Getsudo), termasuk peramalan permintaan spare part setiap bulannya, akan tetapi, metode peramalan Moving Average, yang saat ini digunakan untuk semua spare part di PT. X, kurang efektif pada beberapa pola permintaan spare part yang variasinya tinggi. Penyimpangan pada hasil peramalan berdampak pada lead time back order dan biaya material handling yang semakin besar. Berdasarkan hal tersebut, maka PT. X memerlukan perbaikan terhadap sistem peramalan spare part-nya. Penelitian ini memiliki dua tujuan utama. Tujuan pertama adalah mengusulkan metode klasifikasi spare part berdasarkan pola permintaannya sebelum diramalkan. Tujuan kedua adalah menentukan metode peramalan yang paling sesuai untuk masing-masing kelompok spare part dengan cara membandingkan empat metode peramalan, yaitu: Croston, Modifikasi Croston, SVR, dan ANN. Seluruh metode peramalan dibandingkan berdasarkan parameter forecasting error dan robustness. Hasil penelitian ini menunjukkan bahwa metode SVR memiliki kinerja yang lebih unggul dari metode lainnya di tahap training maupun testing. Selain itu, ketika diimplementasikan untuk peramalan multi-periode, metode SVR juga lebih unggul dan dapat memperbaiki kesalahan peramalan sebesar 19% dari metode peramalan yang saat ini digunakan oleh PT. X

    Forecasting: theory and practice

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    Forecasting has always been in the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The lack of a free-lunch theorem implies the need for a diverse set of forecasting methods to tackle an array of applications. This unique article provides a non-systematic review of the theory and the practice of forecasting. We offer a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts, including operations, economics, finance, energy, environment, and social good. We do not claim that this review is an exhaustive list of methods and applications. The list was compiled based on the expertise and interests of the authors. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of the forecasting theory and practice

    Improving Demand Forecasting: The Challenge of Forecasting Studies Comparability and a Novel Approach to Hierarchical Time Series Forecasting

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    Bedarfsprognosen sind in der Wirtschaft unerlĂ€sslich. Anhand des erwarteten Kundenbe-darfs bestimmen Firmen beispielsweise welche Produkte sie entwickeln, wie viele Fabri-ken sie bauen, wie viel Personal eingestellt wird oder wie viel Rohmaterial geordert wer-den muss. FehleinschĂ€tzungen bei Bedarfsprognosen können schwerwiegende Auswir-kungen haben, zu Fehlentscheidungen fĂŒhren, und im schlimmsten Fall den Bankrott einer Firma herbeifĂŒhren. Doch in vielen FĂ€llen ist es komplex, den tatsĂ€chlichen Bedarf in der Zukunft zu antizipie-ren. Die Einflussfaktoren können vielfĂ€ltig sein, beispielsweise makroökonomische Ent-wicklung, das Verhalten von Wettbewerbern oder technologische Entwicklungen. Selbst wenn alle Einflussfaktoren bekannt sind, sind die ZusammenhĂ€nge und Wechselwirkun-gen hĂ€ufig nur schwer zu quantifizieren. Diese Dissertation trĂ€gt dazu bei, die Genauigkeit von Bedarfsprognosen zu verbessern. Im ersten Teil der Arbeit wird im Rahmen einer ĂŒberfassenden Übersicht ĂŒber das gesamte Spektrum der Anwendungsfelder von Bedarfsprognosen ein neuartiger Ansatz eingefĂŒhrt, wie Studien zu Bedarfsprognosen systematisch verglichen werden können und am Bei-spiel von 116 aktuellen Studien angewandt. Die Vergleichbarkeit von Studien zu verbes-sern ist ein wesentlicher Beitrag zur aktuellen Forschung. Denn anders als bspw. in der Medizinforschung, gibt es fĂŒr Bedarfsprognosen keine wesentlichen vergleichenden quan-titativen Meta-Studien. Der Grund dafĂŒr ist, dass empirische Studien fĂŒr Bedarfsprognosen keine vereinheitlichte Beschreibung nutzen, um ihre Daten, Verfahren und Ergebnisse zu beschreiben. Wenn Studien hingegen durch systematische Beschreibung direkt miteinan-der verglichen werden können, ermöglicht das anderen Forschern besser zu analysieren, wie sich Variationen in AnsĂ€tzen auf die PrognosegĂŒte auswirken – ohne die aufwĂ€ndige Notwendigkeit, empirische Experimente erneut durchzufĂŒhren, die bereits in Studien beschrieben wurden. Diese Arbeit fĂŒhrt erstmals eine solche Systematik zur Beschreibung ein. Der weitere Teil dieser Arbeit behandelt Prognoseverfahren fĂŒr intermittierende Zeitreihen, also Zeitreihen mit wesentlichem Anteil von Bedarfen gleich Null. Diese Art der Zeitreihen erfĂŒllen die Anforderungen an Stetigkeit der meisten Prognoseverfahren nicht, weshalb gĂ€ngige Verfahren hĂ€ufig ungenĂŒgende PrognosegĂŒte erreichen. Gleichwohl ist die Rele-vanz intermittierender Zeitreihen hoch – insbesondere Ersatzteile weisen dieses Bedarfs-muster typischerweise auf. ZunĂ€chst zeigt diese Arbeit in drei Studien auf, dass auch die getesteten Stand-der-Technik Machine Learning AnsĂ€tze bei einigen bekannten DatensĂ€t-zen keine generelle Verbesserung herbeifĂŒhren. Als wesentlichen Beitrag zur Forschung zeigt diese Arbeit im Weiteren ein neuartiges Verfahren auf: Der Similarity-based Time Series Forecasting (STSF) Ansatz nutzt ein Aggregation-Disaggregationsverfahren basie-rend auf einer selbst erzeugten Hierarchie statistischer Eigenschaften der Zeitreihen. In Zusammenhang mit dem STSF Ansatz können alle verfĂŒgbaren Prognosealgorithmen eingesetzt werden – durch die Aggregation wird die Stetigkeitsbedingung erfĂŒllt. In Expe-rimenten an insgesamt sieben öffentlich bekannten DatensĂ€tzen und einem proprietĂ€ren Datensatz zeigt die Arbeit auf, dass die PrognosegĂŒte (gemessen anhand des Root Mean Square Error RMSE) statistisch signifikant um 1-5% im Schnitt gegenĂŒber dem gleichen Verfahren ohne Einsatz von STSF verbessert werden kann. Somit fĂŒhrt das Verfahren eine wesentliche Verbesserung der PrognosegĂŒte herbei. Zusammengefasst trĂ€gt diese Dissertation zum aktuellen Stand der Forschung durch die zuvor genannten Verfahren wesentlich bei. Das vorgeschlagene Verfahren zur Standardi-sierung empirischer Studien beschleunigt den Fortschritt der Forschung, da sie verglei-chende Studien ermöglicht. Und mit dem STSF Verfahren steht ein Ansatz bereit, der zuverlĂ€ssig die PrognosegĂŒte verbessert, und dabei flexibel mit verschiedenen Arten von Prognosealgorithmen einsetzbar ist. Nach dem Erkenntnisstand der umfassenden Literatur-recherche sind keine vergleichbaren AnsĂ€tze bislang beschrieben worden

    A multivariate approach for multi-step demand forecasting in assembly industries: Empirical evidence from an automotive supply chain

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    PreprintDemand forecasting works as a basis for operating, business and production planning decisions in many supply chain contexts. Yet, how to accurately predict the manufacturer's demand for components in the presence of end-customer demand uncertainty remains poorly understood. Assigning the proper order quantities of components to suppliers thus becomes a nontrivial task, with a significant impact on planning, capacity and inventory-related costs. This paper introduces a multivariate approach to predict manufacturer's demand for components throughout multiple forecast horizons using different leading indicators of demand shifts. We compare the autoregressive integrated moving average model with exogenous inputs (ARIMAX) with Machine Learning (ML) models. Using a real case study, we empirically evaluate the forecasting and supply chain performance of the multivariate regression models over the component's life-cycle. The experiments show that the proposed multivariate approach provides superior forecasting and inventory performance compared with traditional univariate benchmarks. Moreover, it reveals applicable throughout the component's life-cycle, not just to a single stage. Particularly, we found that demand signals at the beginning of the life-cycle are predicted better by the ARIMAX model, but it is outperformed by ML-based models in later life-cycle stages.INCT-EN - Instituto Nacional de CiĂȘncia e Tecnologia para Excitotoxicidade e Neuroproteção(UIDB/00319/2020

    Forecasting: theory and practice

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    Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases.info:eu-repo/semantics/publishedVersio

    Forecasting: theory and practice

    Get PDF
    Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases

    OR in Spare Parts Management:A Review

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    Abstract Spare parts are held to reduce the consequences of equipment downtime, playing an important role in achieving the desired equipment availability at a minimum economic cost. In this paper, a framework for OR in spare parts management is presented, based on the product lifecycle process and including the objectives, main tasks, and OR disciplines for supporting spare parts management. Based on the framework, a systematic literature review of OR in spare parts management is undertaken, and then a comprehensive investigation of each OR discipline's contribution is given. The gap between theory and practice of spare parts management is investigated from the perspective of software integration, maintenance management information systems and adoption of new OR methods in software. Finally, as the result of this review, an extended version of the framework is proposed and a set of future research directions is discussed

    Artificial Intelligence in Supply Chain and Operations Management: A Multiple Case Study Research

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    Artificial intelligence (AI) is increasingly considered a source of competitive advantage in operations and supply chain management (OSCM). However, many organisations still struggle to adopt it successfully and empirical studies providing clear indications are scarce in the literature. This research aims to shed light on how AI applications can support OSCM processes and to identify benefits and barriers to their implementation. To this end, it conducts a multiple case study with semi-structured interviews in six companies, totalling 17 implementation cases. The Supply Chain Operations Reference (SCOR) model guided the entire study and the analysis of the results by targeting specific processes. The results highlighted how AI methods in OSCM can increase the companies' competitiveness by reducing costs and lead times and improving service levels, quality, safety, and sustainability. However, they also identify barriers in the implementation of AI, such as ensuring data quality, lack of specific skills, need for high investments, lack of clarity on economic benefits and lack of experience in cost analysis for AI projects. Although the nature of the study is not suitable for wide generalisation, it offers clear guidance for practitioners facing AI dilemmas in specific SCOR processes and provides the basis for further future research
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