Supply chain inventory control for the iron and steel industry

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Evaluation of Pull Production Control Strategies Under Uncertainty: An Integrated Fuzzy Ahp-Topsis Approach

Purpose: Just-In-Time (JIT) production has continuously been considered by industrial practitioners and researchers as a leading strategy for the yet popular Lean production. Pull Production Control Policies (PPCPs) are the major enablers of JIT that locally control the level of inventory by authorizing the production in each station. Aiming to improve the PPCPs, three authorization mechanisms: Kanban, constant-work-in-process (ConWIP), and a hybrid system, are evaluated by considering uncertainty. Design/methodology/approach: Multi-Criteria Decision Making (MCDM) methods are successful in evaluating alternatives with respect to several objectives. The proposed approach of this study applies the fuzzy set theory together with an integrated Analytical Hierarchy Process (AHP) and a Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) method. Findings: The study finds that hybrid Kanban-ConWIP pull production control policies have a better performance in controlling the studied multi-layer multi-stage manufacturing and assembly system. Practical implications: To examine the approach a real case from automobile electro-mechanical part production industry is studied. The production system consists of multiple levels of manufacturing, feeding a multi-stage assembly line with stochastic processing times to satisfy the changing demand. Originality/value: This study proposes the integrated Kanban-ConWIP hybrid pull control policies and implements several alternatives on a multi-stage and multi-layer manufacturing and assembly production system. An integrated Fuzzy AHP TOPSIS method is developed to evaluate the alternatives with respect to several JIT criteriaPeer Reviewe

Development of transportation and supply chain problems with the combination of agent-based simulation and network optimization

Demand drives a different range of supply chain and logistics location decisions, and agent-based modeling (ABM) introduces innovative solutions to address supply chain and logistics problems. This dissertation focuses on an agent-based and network optimization approach to resolve those problems and features three research projects that cover prevalent supply chain management and logistics problems. The first case study evaluates demographic densities in Norway, Finland, and Sweden, and covers how distribution center (DC) locations can be established using a minimizing trip distance approach. Furthermore, traveling time maps are developed for each scenario. In addition, the Nordic area consisting of those three countries is analyzed and five DC location optimization results are presented. The second case study introduces transportation cost modelling in the process of collecting tree logs from several districts and transporting them to the nearest collection point. This research project presents agent-based modelling (ABM) that incorporates comprehensively the key elements of the pick-up and delivery supply chain model and designs the components as autonomous agents communicating with each other. The modelling merges various components such as GIS routing, potential facility locations, random tree log pickup locations, fleet sizing, trip distance, and truck and train transportation. The entire pick-up and delivery operation are modeled by ABM and modeling outcomes are provided by time series charts such as the number of trucks in use, facilities inventory and travel distance. In addition, various scenarios of simulation based on potential facility locations and truck numbers are evaluated and the optimal facility location and fleet size are identified. In the third case study, an agent-based modeling strategy is used to address the problem of vehicle scheduling and fleet optimization. The solution method is employed to data from a real-world organization, and a set of key performance indicators are created to assess the resolution's effectiveness. The ABM method, contrary to other modeling approaches, is a fully customized method that can incorporate extensively various processes and elements. ABM applying the autonomous agent concept can integrate various components that exist in the complex supply chain and create a similar system to assess the supply chain efficiency.Tuotteiden kysyntä ohjaa erilaisia toimitusketju- ja logistiikkasijaintipäätöksiä, ja agenttipohjainen mallinnusmenetelmä (ABM) tuo innovatiivisia ratkaisuja toimitusketjun ja logistiikan ongelmien ratkaisemiseen. Tämä väitöskirja keskittyy agenttipohjaiseen mallinnusmenetelmään ja verkon optimointiin tällaisten ongelmien ratkaisemiseksi, ja sisältää kolme tapaustutkimusta, jotka voidaan luokitella kuuluvan yleisiin toimitusketjun hallinta- ja logistiikkaongelmiin. Ensimmäinen tapaustutkimus esittelee kuinka käyttämällä väestötiheyksiä Norjassa, Suomessa ja Ruotsissa voidaan määrittää strategioita jakelukeskusten (DC) sijaintiin käyttämällä matkan etäisyyden minimoimista. Kullekin skenaariolle kehitetään matka-aikakartat. Lisäksi analysoidaan näistä kolmesta maasta koostuvaa pohjoismaista aluetta ja esitetään viisi mahdollista sijaintia optimointituloksena. Toinen tapaustutkimus esittelee kuljetuskustannusmallintamisen prosessissa, jossa puutavaraa kerätään useilta alueilta ja kuljetetaan lähimpään keräyspisteeseen. Tämä tutkimusprojekti esittelee agenttipohjaista mallinnusta (ABM), joka yhdistää kattavasti noudon ja toimituksen toimitusketjumallin keskeiset elementit ja suunnittelee komponentit keskenään kommunikoiviksi autonomisiksi agenteiksi. Mallinnuksessa yhdistetään erilaisia komponentteja, kuten GIS-reititys, mahdolliset tilojen sijainnit, satunnaiset puunhakupaikat, kaluston mitoitus, matkan pituus sekä monimuotokuljetukset. ABM:n avulla mallinnetaan noutojen ja toimituksien koko ketju ja tuloksena saadaan aikasarjoja kuvaamaan käytössä olevat kuorma-autot, sekä varastomäärät ja ajetut matkat. Lisäksi arvioidaan erilaisia simuloinnin skenaarioita mahdollisten laitosten sijainnista ja kuorma-autojen lukumäärästä sekä tunnistetaan optimaalinen toimipisteen sijainti ja tarvittava autojen määrä. Kolmannessa tapaustutkimuksessa agenttipohjaista mallinnusstrategiaa käytetään ratkaisemaan ajoneuvojen aikataulujen ja kaluston optimoinnin ongelma. Ratkaisumenetelmää käytetään dataan, joka on peräisin todellisesta organisaatiosta, ja ratkaisun tehokkuuden arvioimiseksi luodaan lukuisia keskeisiä suorituskykyindikaattoreita. ABM-menetelmä, toisin kuin monet muut mallintamismenetelmät, on täysin räätälöitävissä oleva menetelmä, joka voi sisältää laajasti erilaisia prosesseja ja elementtejä. Autonomisia agentteja soveltava ABM voi integroida erilaisia komponentteja, jotka ovat olemassa monimutkaisessa toimitusketjussa ja luoda vastaavan järjestelmän toimitusketjun tehokkuuden arvioimiseksi yksityiskohtaisesti.fi=vertaisarvioitu|en=peerReviewed

Designing pull production control systems:Customization and robustness

In this dissertation we address the issues of selecting and configuring pull production control systems for single-product flowlines. We start with a review of pull systems in the literature, yielding a new classification. Then we propose a novel selection procedure based on a generic system that we test on a case also studied in the literature. We further study our procedure for a variety of twelve production lines. We find new types of pull systems that perform well. Next, we raise the issue of designing pull systems under uncertainty. We propose a novel procedure to minimize the risk of poor performance. Results show that risk considerations strongly influence the selection of a specific pull system

Detailed Inventory Record Inaccuracy Analysis

This dissertation performs a methodical analysis to understand the behavior of inventory record inaccuracy (IRI) when it is influenced by demand, supply and lead time uncertainty in both online and offline retail environment separately. Additionally, this study identifies the susceptibility of the inventory systems towards IRI due to conventional perfect data visibility assumptions. Two different alternatives for such methods are presented and analyzed; the IRI resistance and the error control methods. The discussed methods effectively countered various aspects of IRI; the IRI resistance method performs better on stock-out and lost sales, whereas error control method keeps lower inventory. Furthermore, this research also investigates the value of using a secondary source of information (automated data capturing) along with traditional inventory record keeping methods to control the effects of IRI. To understand the combined behavior of the pooled data sources an infinite horizon discounted Markov decision process (MDP) is generated and optimized. Moreover, the traditional cost based reward structure is abandoned to put more emphasis on the effects of IRI. Instead a new measure is developed as inventory performance by combining four key performance metrics; lost sales, amount of correction, fill rate and amount of inventory counted. These key metrics are united under a unitless platform using fuzzy logic and combined through additive methods. The inventory model is then analyzed to understand the optimal policy structure, which is proven to be of a control limit type

Proactive management of uncertainty to improve scheduling robustness in proces industries

Dinamisme, capacitat de resposta i flexibilitat són característiques essencials en el desenvolupament de la societat actual. Les noves tendències de globalització i els avenços en tecnologies de la informació i comunicació fan que s'evolucioni en un entorn altament dinàmic i incert. La incertesa present en tot procés esdevé un factor crític a l'hora de prendre decisions, així com un repte altament reconegut en l'àrea d'Enginyeria de Sistemes de Procés (PSE). En el context de programació de les operacions, els models de suport a la decisió proposats fins ara, així com també software comercial de planificació i programació d'operacions avançada, es basen generalment en dades estimades, assumint implícitament que el programa d'operacions s'executarà sense desviacions. La reacció davant els efectes de la incertesa en temps d'execució és una pràctica habitual, però no sempre resulta efectiva o factible. L'alternativa és considerar la incertesa de forma proactiva, és a dir, en el moment de prendre decisions, explotant el coneixement disponible en el propi sistema de modelització.Davant aquesta situació es plantegen les següents preguntes: què s'entén per incertesa? Com es pot considerar la incertesa en el problema de programació d'operacions? Què s'entén per robustesa i flexibilitat d'un programa d'operacions? Com es pot millorar aquesta robustesa? Quins beneficis comporta? Aquesta tesi respon a aquestes preguntes en el marc d'anàlisis operacionals en l'àrea de PSE. La incertesa es considera no de la forma reactiva tradicional, sinó amb el desenvolupament de sistemes proactius de suport a la decisió amb l'objectiu d'identificar programes d'operació robustos que serveixin com a referència pel nivell inferior de control de planta, així com també per altres centres en un entorn de cadenes de subministrament. Aquest treball de recerca estableix les bases per formalitzar el concepte de robustesa d'un programa d'operacions de forma sistemàtica. Segons aquest formalisme, els temps d'operació i les ruptures d'equip són considerats inicialment com a principals fonts d'incertesa presents a nivell de programació de la producció. El problema es modelitza mitjançant programació estocàstica, desenvolupant-se finalment un entorn d'optimització basat en simulació que captura les múltiples fonts d'incertesa, així com també estratègies de programació d'operacions reactiva, de forma proactiva. La metodologia desenvolupada en el context de programació de la producció s'estén posteriorment per incloure les operacions de transport en sistemes de múltiples entitats i incertesa en els temps de distribució. Amb aquesta perspectiva més àmplia del nivell d'operació s'estudia la coordinació de les activitats de producció i transport, fins ara centrada en nivells estratègic o tàctic. L'estudi final considera l'efecte de la incertesa en la demanda en les decisions de programació de la producció a curt termini. El problema s'analitza des del punt de vista de gestió del risc, i s'avaluen diferents mesures per controlar l'eficiència del sistema en un entorn incert.En general, la tesi posa de manifest els avantatges en reconèixer i modelitzar la incertesa, amb la identificació de programes d'operació robustos capaços d'adaptar-se a un ampli rang de situacions possibles, enlloc de programes d'operació òptims per un escenari hipotètic. La metodologia proposada a nivell d'operació es pot considerar com un pas inicial per estendre's a nivells de decisió estratègics i tàctics. Alhora, la visió proactiva del problema permet reduir el buit existent entre la teoria i la pràctica industrial, i resulta en un major coneixement del procés, visibilitat per planificar activitats futures, així com també millora l'efectivitat de les tècniques reactives i de tot el sistema en general, característiques altament desitjables per mantenir-se actiu davant la globalitat, competitivitat i dinàmica que envolten un procés.Dynamism, responsiveness, and flexibility are essential features in the development of the current society. Globalization trends and fast advances in communication and information technologies make all evolve in a highly dynamic and uncertain environment. The uncertainty involved in a process system becomes a critical problem in decision making, as well as a recognized challenge in the area of Process Systems Engineering (PSE). In the context of scheduling, decision-support models developed up to this point, as well as commercial advanced planning and scheduling systems, rely generally on estimated input information, implicitly assuming that a schedule will be executed without deviations. The reaction to the effects of the uncertainty at execution time becomes a common practice, but it is not always effective or even possible. The alternative is to address the uncertainty proactively, i.e., at the time of reasoning, exploiting the available knowledge in the modeling procedure itself. In view of this situation, the following questions arise: what do we understand for uncertainty? How can uncertainty be considered within scheduling modeling systems? What is understood for schedule robustness and flexibility? How can schedule robustness be improved? What are the benefits? This thesis answers these questions in the context of operational analysis in PSE. Uncertainty is managed not from the traditional reactive viewpoint, but with the development of proactive decision-support systems aimed at identifying robust schedules that serve as a useful guidance for the lower control level, as well as for dependent entities in a supply chain environment. A basis to formalize the concept of schedule robustness is established. Based on this formalism, variable operation times and equipment breakdowns are first considered as the main uncertainties in short-term production scheduling. The problem is initially modeled using stochastic programming, and a simulation-based stochastic optimization framework is finally developed, which captures the multiple sources of uncertainty, as well as rescheduling strategies, proactively. The procedure-oriented system developed in the context of production scheduling is next extended to involve transport scheduling in multi-site systems with uncertain travel times. With this broader operational perspective, the coordination of production and transport activities, considered so far mainly in strategic and tactical analysis, is assessed. The final research point focuses on the effect of demands uncertainty in short-term scheduling decisions. The problem is analyzed from a risk management viewpoint, and alternative measures are assessed and compared to control the performance of the system in the uncertain environment.Overall, this research work reveals the advantages of recognizing and modeling uncertainty, with the identification of more robust schedules able to adapt to a wide range of possible situations, rather than optimal schedules for a hypothetical scenario. The management of uncertainty proposed from an operational perspective can be considered as a first step towards its extension to tactical and strategic levels of decision. The proactive perspective of the problem results in a more realistic view of the process system, and it is a promising way to reduce the gap between theory and industrial practices. Besides, it provides valuable insight on the process, visibility for future activities, as well as it improves the efficiency of reactive techniques and of the overall system, all highly desirable features to remain alive in the global, competitive, and dynamic process environment

A General Approach to Electrical Vehicle Battery Remanufacturing System Design

One of the major difficulties electrical vehicle (EV) industry facing today is the production and lifetime cost of battery packs. Studies show that using remanufactured batteries can dramatically lower the cost. The major difference between remanufacturing and traditional manufacturing is the supply and demand variabilities and uncertainties differences. The returned core for remanufacturing operations (supply side) can vary considerably in terms of the time of returns and the quality of returned products. On the other hand, because different contracts can be used to regulate suppliers, it is almost always assumed zero uncertainty and variability for traditional manufacturing systems. Similarly, customers demand traditional manufacturers to sell newly produced products in constant high quality. But, remanufacturers usually sell in aftermarket, and the quality of the products demanded can vary depends on the price range, usage, customer segment and many other factors. The key is to match supply and demand side variabilities so the overlapping between them can be maximized. Because of these differences, a new framework is needed for remanufacturing system design. This research aims at developing a new approach to use remanufactured battery packs to fulfill EV warranties and customer aftermarket demands and to match supply and demand side variabilities. First, a market lifetime EV battery return (supply side) forecasting method is develop, and it is validated using Monte Carlo simulation. Second, a discrete event simulation method is developed to estimate EV battery lifetime cost for both customer and manufacturer/remanufacturer. Third, a new remanufacturing business model and a simulation framework are developed so both the quality and quantity aspects of supply and demand can be altered and the lifetime cost for both customer and manufacturer/remanufacturer can be minimized. The business models and methodologies developed in this dissertation provide managerial insights to benefit both the manufacturer/remanufacturer and customers in EV industry. Many findings and methodologies can also be readily used in other remanufacturing settings. The effectiveness of the proposed models is illustrated and validated by case studies.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/143955/1/xrliang_1.pd

A review of discrete-time optimization models for tactical production planning

This is an Accepted Manuscript of an article published in International Journal of Production Research on 27 Mar 2014, available online: http://doi.org/10.1080/00207543.2014.899721[EN] This study presents a review of optimization models for tactical production planning. The objective of this research is to identify streams and future research directions in this field based on the different classification criteria proposed. The major findings indicate that: (1) the most popular production-planning area is master production scheduling with a big-bucket time-type period; (2) most of the considered limited resources correspond to productive resources and, to a lesser extent, to inventory capacities; (3) the consideration of backlogs, set-up times, parallel machines, overtime capacities and network-type multisite configuration stand out in terms of extensions; (4) the most widely used modelling approach is linear/integer/mixed integer linear programming solved with exact algorithms, such as branch-and-bound, in commercial MIP solvers; (5) CPLEX, C and its variants and Lindo/Lingo are the most popular development tools among solvers, programming languages and modelling languages, respectively; (6) most works perform numerical experiments with random created instances, while a small number of works were validated by real-world data from industrial firms, of which the most popular are sawmills, wood and furniture, automobile and semiconductors and electronic devices.This study has been funded by the Universitat Politècnica de València projects: ‘Material Requirement Planning Fourth Generation (MRPIV)’ (Ref. PAID-05-12) and ‘Quantitative Models for the Design of Socially Responsible Supply Chains under Uncertainty Conditions. Application of Solution Strategies based on Hybrid Metaheuristics’ (PAID-06-12).Díaz-Madroñero Boluda, FM.; Mula, J.; Peidro Payá, D. (2014). A review of discrete-time optimization models for tactical production planning. International Journal of Production Research. 52(17):5171-5205. doi:10.1080/00207543.2014.899721S51715205521