Operations strategy processes: how significant are they?

Ongoing challenges associated with the implementation of formally developed strategies call for studying the functional level strategy processes from fresh perspectives. This paper presents evidence drawn from the Canadian oil and gas industry towards establishing the statistical significance of alternative operations strategy processes and organisational contextual factors. The analysis discerned four strategy process configurations representing singular and multiple combinations of three strategy process modes identified in previous qualitative studies. The findings will help advance the understanding of operations strategy processes and may contribute to theory building, as the evidence was drawn from a relatively large sample of data representing an industry sector that has not been previously reported

A simulation model to access the impact of underground coal mine logistics strain on an operation

A novel approach has been developed to strategically identify logistical bottlenecks and the impacts that mine planning parameters might have on these at any point in time throughout life of a mine plan. An XPAC-based model is employed at a macroscopic level to understand trends and shifts in logistical strain for a timeframe up to the entire life of mine. However, on a day-to-day basis the XPAC-based model cannot, at a very detailed level, provide information to analyse if logistical strain can be alleviated through finer adjustments. The finer adjustments that can be identified and rectified, can be determined using a discrete simulation FlexSim model. The paper presents details of the FlexSim discrete simulation model and its applications

Modelling sustainable supply networks with adaptive agents

© 2018 IEEE. This paper proposes a multi-agent modelling approach that supports supply network configuration decisions towards sustaining operations excellence in terms of economic, business continuity and environmental performance. Two types of agents are employed, namely, physical agents to represent supply entities and auxiliary agents to deal with supply network configuration decisions. While using the evolutionary algorithm, Non-dominated Sorting Genetic Algorithm-II to optimize both cost and lead time at the supply network level, agents are modelled with an architecture which consists of decision-making, learning and communication modules. The physical agents make decisions considering varying situations to suit specific product-market profiles thereby generating alternative supply network configurations. These supply network configurations are then evaluated against a set of performance metrics, including the energy consumption of the supply chain processes concerned and the transportation distances between supply entities. Simulation results generated through the application of this approach to a refrigerator production network show that the selected supply network configurations are capable of meeting intended sustainable goals while catering to the respective product-market profiles

Multiagent Optimization Approach to Supply Network Configuration Problems With Varied Product-Market Profiles

IEEE This article demonstrates the application of a novel multiagent modeling approach to support supply network configuration (SNC) decisions toward addressing several challenges reported in the literature. These challenges include: enhancing supply network (SN)-level performance in alignment with the goals of individual SN entities; addressing the issue of limited information sharing between SN entities; and sustaining competitiveness of SNs in dynamic business environments. To this end, a multistage, multiechelon SN consisting of geographically dispersed SN entities catering to distinct product-market profiles was modeled. In modeling the SNC decision problem, two types of agents, each having distinct attributes and functions, were used. The modeling approach incorporated a reverse-auctioning process to simulate the behavior of SN entities with differing individual goals collectively contributing to enhance SN-level performance, by means of setting reserve values generated through the application of a genetic algorithm. A set of Pareto-optimal SNCs catering to distinct product-market profiles was generated using Nondominated Sorting Genetic Algorithm II. Further evaluation of these SNCs against additional criteria, using a rule-based approach, allowed the selection of the most appropriate SNC to meet a broader set of conditions. The model was tested using a refrigerator SN case study drawn from the literature. The results reveal that a number of SNC decisions can be supported by the proposed model, in particular, identifying and evaluating robust SNs to suit varied product-market profiles, enhancing SC capabilities to withstand disruptions and developing contingencies to recover from disruptions

A novel model to measure supplier performance in the supplier selection process

Supplier evaluation has become a significant topic over the past decades, as companies have started to become more outsourced oriented. However, previous research on this topic has not paid adequate attention to the limitations associated with availability of accurate and reliable data relating to the performance of potential suppliers. In an attempt to address this issue, this paper proposes a novel supplier evaluation model that can handle imprecise quantitative and qualitative data. Additionally, Decision Maker’s opinions regarding both qualitative and quantitative criteria are incorporated into this model so that a more comprehensive and realistic assessment of supplier performance can be achieved. The model combines five separate methods that have specific capabilities to handle multiple limitations in the existing methods: Fuzzy Analytical Hierarchy Process and Fuzzy TOPSIS method are used to analyse qualitative criteria/data; Analytical Hierarchy Process and Axiomatic Design are used to analyse quantitative criteria/data, with a particular focus on handling variability in performance data; and Data Envelopment Analysis is used to integrate the results of the two approaches above so as to comparative assessment of supplier performance. This model is verified using a numerical example

Simulation-based evaluation of an integrated planning and scheduling algorithm for maintenance projects

The field of maintenance project planning and scheduling is attracting increasing attention due to ever growing competition among manufacturing organisations. There is a lack of studies that has tackled all the aspects of maintenance project implementation such as costs, resources, down times, uncertainties, operational constraints, among others. Therefore, an approach which uses a unitary structuring method and discrete event simulation to integrate relevant data about the maintenance projects is proposed. The results of the evaluation, on a case from paper-pulp industry, have shown that the proposed approach is able to overcome most of the issues of maintenance planning and scheduling

An approach to solve underground coal mine logistics strain

There is currently no mechanism for mine management and strategic planners to identify potential future logistical supply chain bottlenecks within a mine plan and address them proactively. By identifying any logistics constraints as early as possible, the best opportunity to rectify the problem at the least expense is realised. The paper discusses a novel approach which has been developed to strategically identify logistics bottlenecks and the impacts that mine planning parameters might have on these at any point in time throughout a life of mine plan. The developed system was applied to a real-world Australian mine with actual logistics tracking data input for both calibration and prediction testing. It confirmed that the system was indeed “bolt on” and could predict the number of delivery machines operating at any point in time within the mine’s life

Design and Fabrication of a Li-Ion Battery Electrode Coating Fixture

ME450 Capstone Design and Manufacturing Experience: Winter 2015The goal of this project is to design and fabricate a loading station for users to load very thin metal foils into a Lithium Ion Battery electrode coating machine without wrinkling or otherwise damaging the material. The final design has to be compatible with the existing fixtures on the coating machine while still preventing the foils from shifting during the loading process.http://deepblue.lib.umich.edu/bitstream/2027.42/111316/1/19_Report.pd

Solving closed-loop supply chain problems using game theoretic particle swarm optimisation

© 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group. In this paper, we propose a closed-loop supply chain network configuration model and a solution methodology that aim to address several research gaps in the literature. The proposed solution methodology employs a novel metaheuristic algorithm, along with the popular gradient descent search method, to aid location-allocation and pricing-inventory decisions in a two-stage process. In the first stage, we use an improved version of the particle swarm optimisation (PSO) algorithm, which we call improved PSO (IPSO), to solve the location-allocation problem (LAP). The IPSO algorithm is developed by introducing mutation to avoid premature convergence and embedding an evolutionary game-based procedure known as replicator dynamics to increase the rate of convergence. The results obtained through the application of IPSO are used as input in the second stage to solve the inventory-pricing problem. In this stage, we use the gradient descent search method to determine the selling price of new products and the buy-back price of returned products, as well as inventory cycle times for both product types. Numerical evaluations undertaken using problem instances of different scales confirm that the proposed IPSO algorithm performs better than the comparable traditional PSO, simulated annealing (SA) and genetic algorithm (GA) methods

Development and Implementation of a Flipped-Classroom Delivery in Engineering Computing and Analysis for First Year Engineering Students

University of Wollongong recently undertook a major restructure of its academic and professional units, after the appointment of a new Vice Chancellor in 2012. As a result, the previous 11 faculties have been merged and rationalised into five new faculties. The Faculty of Engineering and the Faculty of Informatics merged to become the Faculty of Engineering and Information Sciences (EIS), consisting of six schools representing a total of 13 disciplines. Following the restructuring, EIS made the decision to develop a new common first year curriculum for all engineering undergraduate programs, spanning nine disciplines, they being; civil, mining, environmental, electrical, computer, telecommunications, mechanical, materials and mechatronic engineering. The process of developing the new first year subjects was undertaken in 2014 by a Task and Finish (T&F) group aiming for full implementation at the commencement of 2015. Through consultation with key stakeholders from each discipline area, as well as teaching teams from existing first year programs, five new engineering subjects were to be created, to coexist with the unaltered physics and mathematics subjects. The T&F group met regularly over the course of 2014, where they initially tasked with identifying the key mastery skills that all engineering students should have developed by the end of their first year of full time study. These skills were then grouped into themes, leading to the creation of the five new subjects. The final role of the T&F group was to report back to the Heads of School who would then assign key personnel to develop the curriculum content for each new subject. This paper will focus on the development of one of those newly created subjects, ENGG105 Engineering Computing and Analysis, which adopted the flipped-classroom approach to deliver the subject content