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Numerous quantitative graph measures have been defined and applied in various disciplines. Such measures may be differentiated according to whether they are information-theoretic or non-information-theoretic. In this paper, we examine an important property of Randic entropy, an information-theoretic measure, and examine some related graph measures based on random roots. In particular, we investigate the degeneracy of these structural graph measures and discuss numerical results. Finally, we draw some conclusions about the measures’ applicability to deterministic and non-deterministic networks.

In this paper, we extend and generalize the spectral theory of undirected networks towards directed networks by introducing the Hermitian normalized Laplacian matrix for directed networks. In order to start, we discuss the Courant–Fischer theorem for the eigenvalues of Hermitian normalized Laplacian matrix. Based on the Courant–Fischer theorem, we obtain a similar result towards the normalized Laplacian matrix of undirected networks: for each i ∈ {1, 2,…, n}, any eigenvalue of Hermitian normalized Laplacian matrix λi ∈ [0, 2]. Moreover, we prove some special conditions if 0, or 2 is an eigenvalue of the Hermitian normalized Laplacian matrix L ( X ) . On top of that, we investigate the symmetry of the eigenvalues of L ( X ) and the edge-version for the eigenvalue interlacing result. Finally we present two expressions for the coefficients of the characteristic polynomial of the Hermitian normalized Laplacian matrix. As an outlook, we sketch some novel and intriguing problems to which our apparatus could generally be applied.

Kurzfassung In Fertigungsprozessen ist die Auswahl der optimalen Prozessparameter eine schwierige Aufgabe und meist stark von der Erfahrung qualifizierter Mitarbeiter abhängig. Könnte man Prozessparameter automatisiert individuell auf jeden Bauteil abstimmen, so werden die Qualität der gefertigten Teile weiter erhöht und der Ausschussanteil reduziert. Durch passende Machine-Learning-Methoden wird es möglich, ein Modell aus Produktionsdaten zu erstellen, um damit Einstellparameter optimal vorherzusagen.

Geschaftsmodellinnovation ist viel mehr als das mechanische Abarbeiten eines Vorgehensmodells. Geschaftsmodellinnovation setzt voraus, dass alles Bestehende kritisch hinterfragt wird und mogliche Zukunftsszenarien des Unternehmens erkannt werden. Geschaftsmodellinnovation heist, im Heute das einzuleiten, was Voraussetzung fur den Erfolg des Unternehmens von Morgen ist. Geschaftsmodellinnovation ruttelt an den etablierten Grundfesten eines Unternehmens und kreiert in wesentlichen Aspekten ein „neues“ Unternehmen.

The United Nations’ Sustainable Development Goals (SDGs) set out to improve the quality of life of people in developed, emerging, and developing countries by covering social and economic aspects, with a focus on environmental sustainability. At the same time, data-driven technologies influence our lives in all areas and have caused fundamental economical and societal changes. This study presents a comprehensive literature review on how data-driven approaches have enabled or inhibited the successful achievement of the 17 SDGs to date. Our findings show that data-driven analytics and tools contribute to achieving the 17 SDGs, e.g., by making information more reliable, supporting better-informed decision-making, implementing data-based policies, prioritizing actions, and optimizing the allocation of resources. Based on a qualitative content analysis, results were aggregated into a conceptual framework, including the following categories: (1) uses of data-driven methods (e.g., monitoring, measurement, mapping or modeling, forecasting, risk assessment, and planning purposes), (2) resulting positive effects, (3) arising challenges, and (4) recommendations for action to overcome these challenges. Despite positive effects and versatile applications, problems such as data gaps, data biases, high energy consumption of computational resources, ethical concerns, privacy, ownership, and security issues stand in the way of achieving the 17 SDGs.

Kurzfassung Ungeplante Maschinenstillstände aufgrund nicht zeitgerecht durchgeführter Wartung führen zu Mehrkosten und temporären Engpässen in der Produktion und verringern im schlechtesten Fall die Liefertreue. Durch vorausschauende Instandhaltung lassen sich diese Probleme signifikant verringern bzw. eliminieren. Voraussetzung ist jedoch die zielgerichtete Auswertung von Maschinendaten. Eine auf das Anwendungsgebiet optimierte Visualisierung und Remote Services ermöglichen es zusätzlich, die Instandhaltungsarbeiten in kurzer Zeit durchzuführen. Der vorliegende Beitrag beschreibt die wichtigsten Kriterien für die Entwicklung einer vorausschauenden Instandhaltungsstrategie und diskutiert aktuelle Trends in der Visualisierung verfügbarer Daten.

Kurzfassung Die Mensch-Maschine Kollaboration steht am Beginn der industriellen Verbreitung. Abhängig von der Komplexität der Aufgabe, den erforderlichen Investitionen, variablen Lohnkosten, der geforderten Ausbringungsmenge und den Kosten für Produktwechsel aufgrund der Variantenvielfalt lassen sich die optimalen Einsatzbereiche von starrer Automation, Automation durch klassische Industrieroboter, Mensch-Maschine-Kollaboration und dem manuellen Arbeitsplatz ableiten.

Product and process quality is playing an increasingly important role in the competitive success of manufacturing companies. To ensure a high quality level of the produced parts, the appropriate selection of parameters in manufacturing processes plays in important role. Traditional approaches for parameter setting rely on rule-based schemes, expertise and domain knowledge of highly skilled workers or trial and error. Automated and real-time adjustment of critical process parameters, based on the individual properties of a part and its previous production conditions, have the potential to reduce scrap and increase the quality. Different machine learning methods can be applied for generating parameter estimation models based on experimental data. In this paper, we present a comparison of linear and symbolic regression methods for an adaptive parameter setting approach. Based on comprehensive real-world data, collected in a long-term study, multiple models are generated, evaluated and compared with regard to their applicability in the studied approach for parameter setting in manufacturing processes.

The hierarchical structure of production planning has the advantage of assigning different decision variables to their respective time horizons and therefore ensures their manageability. However, the restrictive structure of this top-down approach implying that upper level decisions are the constraints for lower level decisions also has its shortcomings. One problem that occurs is that deterministic mixed integer decision problems are often used for long-term planning, but the real production system faces a set of stochastic influences. Therefore, a planned utilisation factor has to be included into this deterministic aggregate planning problem. In practice, this decision is often based on past data and not consciously taken. In this paper, the effect of long-term forecast error on the optimal planned utilisation factor is evaluated for a production system facing stochastic demand and the benefit of exploiting this decision's potential is discussed. Overall costs including capacity, backorder and inventory costs, are determined with simulation for different multi-stage and multi-item production system structures. The results show that the planned utilisation factor used in the aggregate planning problem has a high influence on optimal costs. Additionally, the negative effect of forecast errors is evaluated and discussed in detail for different production system environments., production planning, MRP II, simulation, aggregate production planning, demand and forecast uncertainty

This article addresses a reorder point method with an extended inventory position. The inventory position is decreased by near future known demand spikes. This advance demand information helps to improve the performance of the reorder point method. The proposed extension leads to a hybrid model: make-to-stock for demand less than the spike threshold and make-to-order for demand greater than the spike threshold. Furthermore, the number of cycles is modeled more generally and takes into account that the replenishment order has lot-size Q or an integer multiple of Q. An iterative solution procedure is developed for the proposed model, and the numerical results are shown to underpin the usefulness of the approach.

Predictive Maintenance (PdM) is among the trending topics in the current Industry 4.0 movement and hence, intensively investigated. It aims at sophisticated scheduling of maintenance, mostly in the area of industrial production plants. The idea behind PdM is that, instead of following fixed intervals, service actions could be planned based upon the monitored system condition in order to prevent outages, which leads to less redundant maintenance procedures and less necessary overhauls. In this work we will present a method to analyze a continuous stream of data, which describes a system’s condition progressively. Therefore, we motivate the employment of symbolic regression ensemble models and introduce a sliding-window based algorithm for their evaluation and the detection of stable and changing system states.

Kurzfassung Obwohl unter dem Begriff Industrie 4.0 vielfach eine Weiterführung der Automatisierung mittels neuer Technologien verstanden wird, ist dies eine Fehlinterpretation. Mittels Kriterien aus dem Reifegradmodell Industrie 4.0 wird in diesem Beitrag ein detaillierteres Verständnis zur Thematik gegeben. Dadurch wird aufgezeigt, welche Eigenschaften zukünftige intelligente Automatisierungen besitzen und wie dadurch Kundenbedürfnisse besser erfüllt werden können.

In this paper the influence of different dispatching rules on the average production lead time is investigated. Two theorems based on covariance between processing time and production lead time are formulated and proved theoretically. Theorem 1 links the average production lead time to the “processing time weighted production lead time” for the multi-stage production systems analytically. The influence of different dispatching rules on average lead time, which is well known from simulation and empirical studies, can be proved theoretically in Theorem 2 for a single stage production system. A simulation study is conducted to gain more insight into the influence of dispatching rules on average production lead time in a multi-stage production system. We find that the “processing time weighted average production lead time” for a multi-stage production system is not invariant of the applied dispatching rule and can be used as a dispatching rule independent indicator for single-stage production systems.

A multi-item make-to-order production system in a stochastic environment is analyzed. Assuming a common cycle production approach, the impact of safety stock, cycle time, demand, processing time and setup time on service-level and total relevant cost (holding, setup and backorder cost) is determined. To illustrate this relationship a trajectory for the service-level with respect to the relevant cost (holding and setup) is presented. Furthermore algorithms to calculate the cycle time which leads to maximum service-level at constant safety stock and to calculate the pair cycle time and safety stock which minimize total relevant cost are introduced.

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...

In this paper a model for evaluating the influence of utilization, WIP, FGI, service level and production lead time on EVA (economic value added) is developed in order to link the available logistical key figures with a market perspective and discuss their influence on company value. The question answered in this paper is: What is the optimal utilization and service level of a production system in order to achieve the maximum possible EVA for a company? A single-product, single-machine, make-to-order production system with investment dependent machine capacity is modeled. The model combines a market share concept based on delivery/production lead time and service level with concepts describing the logistical relationships between WIP, utilization, production lead time and service level. In addition to the explanatory use of the model, it can provide support for strategic decisions concerning investing or divesting machinery. The main application of this model is in describing the link between capacity investment and company value comprehensively based on data available from cost accounting in real companies. Furthermore, this paper shows that high flexibility of personnel and machine capacity increases the maximum possible EVA of production systems even if this flexibility is linked to additional costs.

In the context of a single machine, multi-item, continuous-time lot sizing and scheduling problem with a production rate alternating between the two values zero and full production rate, this paper identifies classes of equivalent problem instances which differ from one another in the way in which demand is represented. Each class contains an instance with a continuous cumulated demand function, and may contain demand functions with very different shapes, including functions with discontinuities. When approaching the problem (in order to solve it numerically, or to prove analytical results etc.), it therefore becomes possible to select, from many different representations, the problem instance which best meets the requirements of the applied method.

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...

Because of the fact that some logistical objectives are controversial, a trade-off between short actual lead times, low inventories, high utilization, high service levels and low backorders is necessary. To make good decisions in the design and, in particular, the optimization of a production system or supply chain a deep understanding of the relationship between the logistical figures is essential. In this article relationships between these logistical figures are developed by an analytical approach for a time-continuous multi-item production model. Furthermore, the deviation of work in process is used to describe the fluctuation and disturbances of real systems.

A customer order driven production planning method is developed by combining the buying behaviour of the customers, i.e. the required customer delivery lead time, with the production capacity needed to meet the customer orders. 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. In addition an a priori check is presented to investigate the ability to install a pure make to order system

In this paper we show that the recently introduced “input-weighted average lead-time” is equivalent to the average lead-time measured per item. Furthermore, we state that measuring the average lead-time does not depend on knowing the applied priority rule. Consequently, the relationship “average lead-time is equal to the fraction average inventory over average utilization” holds true for every priority rule. Altogether this can be seen as Little's Law in a continuous setting.

For the design of a supply chain as well as for a production system, the relationship between utilization, range, lead time and work in progress is very important. Because of the cost of the available capacity, the utilization should be high, but on the other hand full utilization has a negative impact on the lead time and work in progress. In this paper, some relationships for a continuous production system are formulated and theoretically proven between average lead time, average range, average work in progress and average utilization for a multi-item system with a dynamic demand. The basic equation that is developed is the continuous and deterministic analogous relationship to the well-known Little Law based on queuing theory. The theoretically deduced and proven logistical curves are similar to the results of the funnel model based on empirical studies and simulation. One interesting result is that the variability of the inventory or of the production load causes a waste of capacity and therefore a re...

Correct standard times are needed for logistic operating curves, manufacturing planning and control systems and Advanced Planning Systems (APS) for scheduling, lot-sizing and sequencing. This paper presents a model for calculation of standard times based on the knowledge of the total busy time of a work center and the produced number of items in a fixed time period. The developed method requires little effort in comparison to other methods such as Work-Factor or time observation. It can regularly determine standard times using current confirmation data.

The input-weighted average standard processing time for a multi-item machine is discussed in this paper. For practical purposes it is important to have a short average processing time. It is shown that the input-weighted average processing time does not depend on the scheduling, sequencing and lot-sizing; it is only influenced by the total input and by the individual standard processing times. Furthermore, the relationship between the individual standard processing time and the average standard processing time is investigated. It is proven that the input-weighted average standard processing time is a convex function of the standard processing time of one certain item. An interesting consequence of the convexity is the fact that a decrease of the standard processing time of an item with a standard processing time less than half of the input-weighted average standard processing time causes an increase of the input-weighted average processing time.

Customer satisfaction is the most important target of manufacturing companies to stay competitive. To reach a high level of customer satisfaction on the one hand the product quality has to fulfil the customer requirements. On the other hand the soft facts like the logistical figures regarding service level and supply availability become more and more important. The ability to know if a due date can be met (with a high probability) or how much additional capacity is needed to ensure a requested due date (with a high probability) does ensure a high supply availability. If additional capacity is not available the ability to know what the earliest possible due date which can be met is (with a high probability) does ensure a high service level if postponing due dates by negotiation is possible. This task is often described in literature as a complex and difficult one.

The purpose of this article is to develop a method for short and medium term capacity setting decisions for providing a market oriented level of available capacity for the investigated machine groups. An MTO (make to order) production system is considered. The basic concept is that the cumulative available capacity of the machine group has to be greater than or equal to the cumulative needed capacity influenced by the customer orders. The cumulative needed capacity is corrected with an operation characteristic which defines the slack of the production system, in order to include enough capacity for short term orders.

Companies have to focus on their customers to stay competitive in the future. Therefore, metrics are necessary to measure the performance of production plants. Based on the TPM approach a metric is introduced in this paper which includes the market. This is done by taking only the produced products into account which fulfill a customer order. Products produced on stock are excluded. As a result the new metric is suitable as a performance measurement in production plants because it supports on the one hand the management objective of optimizing quality rate, performance rate and availability of the equipment. And on the other hand the management has to adjust the loading time on the customer demand. A numerical example emphasis the major drawbacks of the classical TPM metrics and illustrates the advantages of the presented performance metric.

The choice of a Production Planning and Control (PPC) method can be classified as strategic and the required continuous parameterisation issues as tactical. This continuous parameterisation after implementation of a PPC system is often ignored because it requires planning expertise and is time consuming. The effect of not fulfilling these tactical issues, which results in not adapting the PPC parameters to changed environmental influences, is evaluated in this study. Therefore the behaviour of Material Requirements Planning (MRP), Kanban, Constant Work in Process (CONWIP) and Drum Buffer Rope (DBR) under dynamic environments is evaluated in a simulation model, where a high service level at a low WIP is defined as desirable. The environment is described by means of variability of Mean Time To Repair (MTTR), setup time, machine availability, scrap rate and demand.

The time continuous integrated multi-item capacitated dynamic scheduling model with non-granular lot sizes and lot start times and dynamic demand functions as its key elements has recently been developed by Jodlbauer. Here we present a reformulation for improved ease of handling as well as several methods to obtain schedules close to the optimum.

This paper presents a model for determining an optimum demand plan which has been developed whereby both demand figures from the past as well as the results of market research concerning price elasticity are known. The sales demand demonstrates a high seasonal fluctuation as well as a shortterm fluctuation, which is also relevant. The production capacity is restricted . The main idea of the model is first of all to approximate the past demand figures in order to predict future demand figures through extrapolation. In the second step, a non-linear production optimisation model is applied for calculation of the cumulative demand plan taking into account both the price elasticity and resource restrictions. The fmal predicted demand data is shown by an extrapolated curve corrected by a constant. The latter is determined by the fact that the integral of the demand curve has to be the cumulative demand plan. In addition to this result, a beta level of service and consequently the level of the safety stock can be calculated by means of a curvilinear regression analysis .