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Herbert Jodlbauer

Bio: Herbert Jodlbauer is an academic researcher from Steyr Mannlicher. The author has contributed to research in topics: Production planning & Lead time. The author has an hindex of 13, co-authored 55 publications receiving 465 citations.


Papers
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Journal ArticleDOI
TL;DR: In this article, an eM-Plant-based simulation model is presented that discusses the service-level performance of material requirement planning (MRP), kanban, constant work in process (CONWIP) and drum-buffer-rope (DBR) in a flow shop with attention to the work-in-process (WIP).
Abstract: Decisions regarding production planning and control strategy (PPCS) choices can be classified as strategic, whereas parametrization issues are of a tactical nature. However, readjustment is often skipped either as a result of a lack of planning expertise or because it would require extended planning. For this reason, robustness, which is defined as PPCS behaviour within dynamic environments, is investigated. To achieve a greater understanding of the sensitivity on parameter changes in a production system, PPCS stability is examined. An eM-Plant based simulation model is presented that discusses the service-level performance of material requirement planning (MRP), kanban, constant work in process (CONWIP) and drum–buffer–rope (DBR) in a flow-shop with attention to the work in process (WIP). Although the service-level performance of CONWIP exceeds that of the other systems, CONWIP struggles to maintain its advantage under dynamic conditions. The paper seeks to support industrial practitioneers both in their...

81 citations

Journal ArticleDOI
TL;DR: In this article, an analytical model to calculate service level, FGI and tardiness for a make-to-order (MTO) production system based on the production leadtime, utilisation and WIP is presented.
Abstract: In this paper an analytical model to calculate service level, FGI and tardiness for a make-to-order (MTO) production system based on the production leadtime, utilisation and WIP is presented. The distribution of customer required leadtime is linked to the already available equations for an M/M/1 production system from queuing theory. Explicit equations for service level, FGI, FGI leadtime and tardiness are presented for an M/M/1 production system within an MTO environment. For a G/G/1 production system an approximation based extension is provided – discussing the influence of variation in the inter-arrival and processing time distribution in this framework. Moreover, the integration of a work ahead window (WAW) work release policy is discussed. Based on a numerical study, a high potential to decrease FGI (up to 97% FGI reduction) when applying such a WAW strategy is found and it is shown that the higher the targeted service level is, the higher the FGI reduction potential. The paper contributes to a bette...

35 citations

Journal ArticleDOI
TL;DR: The method can be applied in order to determine the WIP cap and the work-ahead-window of a CONWIP controlled production and can also be used to implement a new market driven production planning.

34 citations

Journal ArticleDOI
TL;DR: In this paper, the authors developed an approach (material and capacity requirements planning; MCRP) to integrate capacity planning into MRP, where different measures for capacity adjustment such as alternative routeings, safety stock, lot splitting and lot summarization are discussed.
Abstract: Traditional material requirements planning (MRP) does not consider the finite capacity of machines, and assumes fixed lead times. This paper develops an approach (material and capacity requirements planning; MCRP) to integrate capacity planning into MRP. To obtain a capacity feasible production plan, different measures for capacity adjustment such as alternative routeings, safety stock, lot splitting and lot summarisation, are discussed. Additionally, lead times are no longer assumed to be fixed. They are calculated dynamically with respect to machine capacity utilisation. A detailed example is presented to illustrate how the MCRP approach works successfully.

27 citations

01 Jan 2016
TL;DR: Konkrete Projektvorschläge zur Erreichung dieses Sollzustandes lassen sich ebenfalls aus dem Reifegradmodell für die teilnehmenden Unternehmen ableiten.
Abstract: Zahlreiche Kongresse, Tagungen und Symposien werden unter dem Begriff „Industrie 4.0“ abgehalten. Für eine breite Anwendung in Unternehmen fehlt es oft an Umsetzungsideen mit entsprechender Bewertungsmöglichkeit. Das Reifegradmodell Industrie 4.0 ist ein strategiegeleitetes Vorgehensmodell für Unternehmen, das in den Dimensionen Daten, Intelligenz und Digitale Transformation den Industrie 4.0 Reifegrad bestimmt. Nach Feststellung des ISTReifegrades lässt sich strategiegeleitet der angestrebte SOLL-Reifegrad bestimmen. Konkrete Projektvorschläge zur Erreichung dieses Sollzustandes lassen sich ebenfalls aus dem Reifegradmodell für die teilnehmenden Unternehmen ableiten.

26 citations


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Journal ArticleDOI
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

13,246 citations

Proceedings ArticleDOI
22 Jan 2006
TL;DR: Some of the major results in random graphs and some of the more challenging open problems are reviewed, including those related to the WWW.
Abstract: We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

7,116 citations

01 Jan 2012

3,692 citations

Journal ArticleDOI
TL;DR: Chapman and Miller as mentioned in this paper, Subset Selection in Regression (Monographs on Statistics and Applied Probability, no. 40, 1990) and Section 5.8.
Abstract: 8. Subset Selection in Regression (Monographs on Statistics and Applied Probability, no. 40). By A. J. Miller. ISBN 0 412 35380 6. Chapman and Hall, London, 1990. 240 pp. £25.00.

1,154 citations

Journal ArticleDOI
TL;DR: In this article, the authors classify the literature on the application of big data business analytics (BDBA) on logistics and supply chain management (LSCM) based on the nature of analytics (descriptive, predictive, prescriptive) and the focus of the LSCM (strategy and operations).

938 citations