Spare Parts Demand Forecasting in Maintenance, Repair & Overhaul

Despite a high degree of uncertainty about the scope of future orders and the corresponding capacity and material demands, Maintenance, Repair & Overhaul (MRO) service providers face high expectations regarding due date reliability by their customers. To meet these requirements while at the same time keeping delivery times short, the availability of the required spare parts or pool parts is an essential success factor. As these cannot be kept in stock in large quantities due to their high monetary value, reliable spare parts demand forecasts are of vital importance for the profitability of MRO service providers. As a result of a high degree of information uncertainty and the mostly lumpy demand patterns, conventional time-based and statistical methods do not show sufficient forecasting quality for application in the MRO industry. Data-based approaches incorporating machine learning methods offer promising capabilities to achieve improved predictive accuracy but still need to be adequately linked to production planning and control to realize their full potential. This paper first analyses potential approaches to spare parts demand forecasting in the MRO industry, focusing on forecast accuracy and potential for integration into material and production planning. Based on this, a classification of demand forecasting approaches is presented and an approach for order-based material demand forecasting with two-step feature selection is proposed. Finally, the presented approach is applied on a real dataset provided by a MRO service provider

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

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 Ciência e Tecnologia para Excitotoxicidade e Neuroproteção(UIDB/00319/2020

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

Bedarfsprognosen sind in der Wirtschaft unerlässlich. 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. Fehleinschätzungen bei Bedarfsprognosen können schwerwiegende Auswir-kungen haben, zu Fehlentscheidungen führen, und im schlimmsten Fall den Bankrott einer Firma herbeiführen. Doch in vielen Fällen ist es komplex, den tatsächlichen Bedarf in der Zukunft zu antizipie-ren. Die Einflussfaktoren können vielfältig sein, beispielsweise makroökonomische Ent-wicklung, das Verhalten von Wettbewerbern oder technologische Entwicklungen. Selbst wenn alle Einflussfaktoren bekannt sind, sind die Zusammenhänge und Wechselwirkun-gen häufig nur schwer zu quantifizieren. Diese Dissertation trägt dazu bei, die Genauigkeit von Bedarfsprognosen zu verbessern. Im ersten Teil der Arbeit wird im Rahmen einer überfassenden Übersicht über das gesamte Spektrum der Anwendungsfelder von Bedarfsprognosen ein neuartiger Ansatz eingeführt, 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 für Bedarfsprognosen keine wesentlichen vergleichenden quan-titativen Meta-Studien. Der Grund dafür ist, dass empirische Studien für 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 Ansätzen auf die Prognosegüte auswirken – ohne die aufwändige Notwendigkeit, empirische Experimente erneut durchzuführen, die bereits in Studien beschrieben wurden. Diese Arbeit führt erstmals eine solche Systematik zur Beschreibung ein. Der weitere Teil dieser Arbeit behandelt Prognoseverfahren für intermittierende Zeitreihen, also Zeitreihen mit wesentlichem Anteil von Bedarfen gleich Null. Diese Art der Zeitreihen erfüllen die Anforderungen an Stetigkeit der meisten Prognoseverfahren nicht, weshalb gängige Verfahren häufig ungenügende Prognosegüte erreichen. Gleichwohl ist die Rele-vanz intermittierender Zeitreihen hoch – insbesondere Ersatzteile weisen dieses Bedarfs-muster typischerweise auf. Zunächst zeigt diese Arbeit in drei Studien auf, dass auch die getesteten Stand-der-Technik Machine Learning Ansätze bei einigen bekannten Datensät-zen keine generelle Verbesserung herbeiführen. 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 verfügbaren Prognosealgorithmen eingesetzt werden – durch die Aggregation wird die Stetigkeitsbedingung erfüllt. In Expe-rimenten an insgesamt sieben öffentlich bekannten Datensätzen und einem proprietären Datensatz zeigt die Arbeit auf, dass die Prognosegüte (gemessen anhand des Root Mean Square Error RMSE) statistisch signifikant um 1-5% im Schnitt gegenüber dem gleichen Verfahren ohne Einsatz von STSF verbessert werden kann. Somit führt das Verfahren eine wesentliche Verbesserung der Prognosegüte herbei. Zusammengefasst trägt 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 zuverlässig die Prognosegüte verbessert, und dabei flexibel mit verschiedenen Arten von Prognosealgorithmen einsetzbar ist. Nach dem Erkenntnisstand der umfassenden Literatur-recherche sind keine vergleichbaren Ansätze bislang beschrieben worden

Operations research models and methods for safety stock determination: A review

In supply chain inventory management it is generally accepted that safety stocks are a suitable strategy to deal with demand and supply uncertainty aiming to prevent inventory stock-outs. Safety stocks have been the subject of intensive research, typically covering the problems of dimensioning, positioning, managing and placement. Here, we narrow the scope of the discussion to the safety stock dimensioning problem, consisting in determining the proper safety stock level for each product. This paper reports the results of a recent in-depth systematic literature review (SLR) of operations research (OR) models and methods for dimensioning safety stocks. To the best of our knowledge, this is the first systematic review of the application of OR-based approaches to investigate this problem. A set of 95 papers published from 1977 to 2019 has been reviewed to identify the type of model being employed, as well as the modeling techniques and main performance criteria used. At the end, we highlight current literature gaps and discuss potential research directions and trends that may help to guide researchers and practitioners interested in the development of new OR-based approaches for safety stock determination.This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020, and by the European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Program (COMPETE 2020) [Project no. 39479, Funding reference: POCI-01-0247-FEDER-39479]

Distributed energy resources and the application of AI, IoT, and blockchain in smart grids

Smart grid (SG), an evolving concept in the modern power infrastructure, enables the two-way flow of electricity and data between the peers within the electricity system networks (ESN) and its clusters. The self-healing capabilities of SG allow the peers to become active partakers in ESN. In general, the SG is intended to replace the fossil fuel-rich conventional grid with the distributed energy resources (DER) and pools numerous existing and emerging know-hows like information and digital communications technologies together to manage countless operations. With this, the SG will able to “detect, react, and pro-act” to changes in usage and address multiple issues, thereby ensuring timely grid operations. However, the “detect, react, and pro-act” features in DER-based SG can only be accomplished at the fullest level with the use of technologies like Artificial Intelligence (AI), the Internet of Things (IoT), and the Blockchain (BC). The techniques associated with AI include fuzzy logic, knowledge-based systems, and neural networks. They have brought advances in controlling DER-based SG. The IoT and BC have also enabled various services like data sensing, data storage, secured, transparent, and traceable digital transactions among ESN peers and its clusters. These promising technologies have gone through fast technological evolution in the past decade, and their applications have increased rapidly in ESN. Hence, this study discusses the SG and applications of AI, IoT, and BC. First, a comprehensive survey of the DER, power electronics components and their control, electric vehicles (EVs) as load components, and communication and cybersecurity issues are carried out. Second, the role played by AI-based analytics, IoT components along with energy internet architecture, and the BC assistance in improving SG services are thoroughly discussed. This study revealed that AI, IoT, and BC provide automated services to peers by monitoring real-time information about the ESN, thereby enhancing reliability, availability, resilience, stability, security, and sustainability

Demand forecasting using a hybrid model based on artificial neural networks: A study case on electrical products

Purpose: This work aims to evaluate demand forecasting models to determine if using exogenous factors and machine learning techniques helps improve performance compared to univariate statistical models, allowing manufacturing companies to manage demand better. Design/methodology/approach: We implemented a multivariate Auto-Regressive Moving Average with eXogenous input (ARMAX) statistical model and a Neural Network-ARMAX (NN-ARMAX) hybrid model for forecasting. Later, we compared both to a standard univariate statistical model to forecast the demand for electrical products in a Colombian manufacturing company. Findings: The outcomes demonstrated that the NN-ARMAX model outperformed the other two. Indeed, demand management improved with the reduction of overstock and out-of-stock products. Research limitations/implications: The findings and conclusions in this work are limited to Colombian manufacturing companies that sell electrical products to the construction industry. Moreover, the experts from the company that provided us with the data also selected the external factors based on their own experiences, i.e., we might have disregarded potential factors. Practical implications: This work suggests that a model using neural networks and including exogenous variables can improve demand forecasting accuracy, promoting this approach in manufacturing companies dealing with demand planning issues. Originality/value: The findings in this work demonstrate the convenience of using the proposed hybrid model to improve demand forecasting accuracy and thus provide a reliable basis for its implementation in supply chain planning for the electrical/construction sector in Colombian manufacturing companies

Advanced Statistical Modeling, Forecasting, and Fault Detection in Renewable Energy Systems

Fault detection, control, and forecasting have a vital role in renewable energy systems (Photovoltaics (PV) and wind turbines (WTs)) to improve their productivity, ef?ciency, and safety, and to avoid expensive maintenance. For instance, the main crucial and challenging issue in solar and wind energy production is the volatility of intermittent power generation due mainly to weather conditions. This fact usually limits the integration of PV systems and WTs into the power grid. Hence, accurately forecasting power generation in PV and WTs is of great importance for daily/hourly efficient management of power grid production, delivery, and storage, as well as for decision-making on the energy market. Also, accurate and prompt fault detection and diagnosis strategies are required to improve efficiencies of renewable energy systems, avoid the high cost of maintenance, and reduce risks of fire hazards, which could affect both personnel and installed equipment. This book intends to provide the reader with advanced statistical modeling, forecasting, and fault detection techniques in renewable energy systems

Human Aspect on Chain of Custody (CoC) System Performance

The tropical forests cover 24% of tropical land area. They are the most productive terrestrial ecosystems on earth with high priorities for biodiversity conservation. These forests store a substantial amount of carbon in biomass and soil, and they also regulate the transfer of carbon into the atmosphere as carbon dioxide (CO2). Indonesia is having the third tropical forest area in the world after Brazil and Congo. Over 50 years forest has been felled both legally as well as illegally. High rate of forest degradation resulted from unsustainable forest management, rampant illegal logging, forest area encroachment, conversion and natural disaster. All urges rapid improvement of management system of Indonesia’s forest resources (Holmes, 2002). Forest certification is one tool that can support the achievement of sustainable forest management goal. Under current operation of join certification protocol between the Forest Stewardship Council (FSC) and the Indonesian Ecolabelling Institute (LEI) in Indonesia, forest management units must be able to show the required performance indicated in LEI criteria and indicator as well as FSC principles and criteria to attain certification of their products. The gap between current practices and performance required by forest certifications schemes is still enormous. The performance of forest certification system from LEI is determined very much by the human that is involved in the process of planning and operation. The name of certification system is chain of custody (CoC) certification. CoC operation involves activities such as tracing raw material from the forest to the factory, through shipping and manufacturing, to the final end product. In all of the above processes, the roles of human are critical, although the specific roles played from one process to another are different. In this paper we present an identification of human aspect and other factors that predominantly affect CoC system performance