The North American forest products industry is primarily commodity-based and faces challenges. This has led to the proposal of a shift toward revenue diversification through the production of high-value specialty products along with the conventional commodity products. A key consideration for this new business strategy to remain competitive and sustainable is that the forest products supply chain designs must perform satisfactorily under the dynamic market conditions. The notion of supply chain operability attempts to characterize the ability of a supply chain to perform satisfactorily in the face of uncertainty. However, limited quantitative analysis is available in the current body of literature. In this work, the concepts originated within the context of process systems engineering are adapted to develop optimization-based frameworks in order to characterize supply chain operability measures, in particular, supply chain flexibility and dynamic responsiveness. Although motivated by the forest products industry, the practical mathematical formulations presented are widely applicable to general process supply chains in other industries. This thesis aims to extend the supply chain flexibility analysis formulation established by Mastragostino (2012) to include additional quantitative flexibility measures. The resulting framework provides a quantitative mapping to various types of flexibility frequently discussed in the operations research literature. Two case studies are included to illustrate the application of this framework for analyzing the flexibility of existing supply chain processes, as well as utilizing it in supply chain design. The work also builds on the analysis framework established by Mastragostino and Swartz (2014) to assess supply chain responsiveness, and to configure the framework in preparation for tackling design problems under uncertainty. Then a composite operability analysis framework is proposed to address both flexibility and responsiveness metrics simultaneously in forest products supply chain design and operation. A comprehensive case study based on a forest product company is performed and the trade-offs among flexibility, responsiveness and economics are examined. / Thesis / Master of Applied Science (MASc)

Abstract   Problem discussion: The significance of the oil industry’s impact on the global economy is obvious. Oil supply chain management has to solve a lot of challenges caused by the nature of the supply chain in the oil industry such as complexity, inflexible characteristics, long lead time, limited transportation forms at the different stages in the supply chain, rigid take or pay procurement and limited primary distribution capacity. Other challenges are caused by unforeseen events such as political or economic changes which have an impact on the price of the oil. This thesis seeks to add value by signifying and indicating optimization as a way to address uncertainties and points out a way to utilize resources efficiently in order to gain further development and cost savings in the long term. Finding options for optimization of the oil supply chain is vital because any cost saving means vast amounts of money for the oil companies therefore optimization is at the centre of attention in the oil supply chain management. Purpose: The purpose of this thesis is to investigate supply chain management in the oil industry and find options for optimizing the supply chain in the oil industry by reviewing and analyzing previously written literature on the chosen topic for the research.  Method: A single case study was applied in this thesis. The company chosen for the case study is called MOL Hungarian oil and gas PLC and is located in Hungary. To carry out the research, a qualitative research approach was implemented. Primary data was collected through semi structured interviews via telephone and the internet with the company’s staff. In addition to this, secondary data from different sources such as articles and books were used to construct or build the theoretical frame of reference for the thesis.  Delimitation: The scope of the thesis is limited to the supply chain management in the oil industry and its optimization. Further narrowing the scope, this thesis gives more attention to the downstream section of the supply chain in the oil industry. Conclusion: Optimization is recognised as main tool for the oil companies to achieve competitive advantage. Analysing MOL Group gives a factual example how optimization works in an oil company and contributes to manage its supply chain efficiently and handle the many uncertainties surrounding the oil industry. It is demonstrated what factors play key role in optimization and how they interact with each other. MOL Group’s solution for optimization builds around a serious planning process, IT solution, marketing and refinery operation triggering and working in synergy with many other factors which cannot be excluded from the optimization process. Due to the excellence of supply chain optimization, MOL Group has a very strong presence and leading position in the East Central European region generating increasing profit margin year by year in last two decades.

In recent years the amount of newly registered electric vehicles, hybrid electric vehicles, and plug-in hybrid electric vehicles has increased rapidly in the Swedish market. These vehicles could be classified as battery-powered electric vehicles, and a majority carry a lithium-ion battery. The demand for lithium is expected to increase considerably, as a result of such a swift growth in battery-powered electric vehicles. Thus, if the recycling rate of lithium stays at a low level, demand could reach a scarcity-level by 2050. While neither any infrastructure nor an established process for recycling lithium-ion batteries currently exists in Sweden, this study aims to provide necessary input and verified tools for the design of a future reverse supply chain for discarded lithium-ion batteries in Sweden. The literature review of this study covers the subjects of reverse logistics, supply chain network design, and operations research. A thorough situation analysis of the Swedish market for battery-powered electric vehicles is conducted, and the composition, function, and characteristics of lithium-ion batteries are studied. The study finds that estimations of future demand of recyclable lithium-ion batteries in Sweden could be between 206 711 and 726 974 tons accumulated, based on actual and predicted sales numbers until 2030. Even if it is obvious that there are going to be large quantities of such batteries requiring recycling in the future, and even if some established processes exist, there is no defined supply chain for the collection of those batteries. Finally, a mixed-integer programming model for the design and development of a future reverse supply chain is presented. The model, characterized as a discrete multi-period facility location/allocation model, can with minor modifications be used for problems with fluctuating demand or when the demand is assumed to slowly progress until it has reached a steady state.

Global organizations have adopted Industry 4.0 practices to stay viable through the information shared through billions of digital documents. The information in such documents is vital to the daily functioning of such organizations. Most critical information is laid out in tabular format in order to provide the information in a concise manner. Extracting this critical data and providing access to the latest information can help institutions to make evidence based and data driven decisions. Assembling such data for analysis can further enable organizations to automate certain processes such as manufacturing. A generalized solution for table text extraction would have to handle the variations in the page content and table layouts in order to accurately extract the text. We hypothesize that a table text extraction pipeline can extract this data in three stages. The first stage would involve identifying the images that contain tables and detecting the table region. The second stage would consider the detected table region and detect the rows and columns of the table. The last stage would involve extracting the text from the cell locations generated by the intersecting lines of the detected rows and columns. For first stage of the pipeline, we propose TableDet: a deep learning (artificial neural network) based methodology to solve table detection and table image classification in datasheet (document) images in a single inference. TableDet utilizes a Cascade R-CNN architecture with Complete IOU (CIOU) loss at each box head and a deformable convolution backbone to capture the variations of tables that appear at multiple scales and orientations. It also detects text and figures to enhance its table detection performance. We demonstrate the effectiveness of training TableDet with a dual-step transfer learning process and fine-tuning it with Table Aware Cutout (TAC) augmented images. TableDet achieves the highest F1 score for table detection against state-of-the-art solutions on ICDAR 2013 (complete set), ICDAR 2017 (test set) and ICDAR 2019 (test set) with 100%, 99.3% and 95.1% respectively. We show that the enhanced table detection performance can be utilized to address the table image classification task with the addition of a classification head which comprises of 3 conditions. For the table image classification task TableDet achieves 100% recall and above 92% precision on three test sets. These classification results indicate that all images with tables along with a significantly reduced number of images without tables would be promoted to the next stage of the table text extraction pipeline. For the second stage we propose TableStrDet, a deep learning (artificial neural network) based approach to recognize the structure of the detected tables regions from stage 1 by detecting and classifying rows and columns. TableStrDet comprises of two Cascade R-CNN architectures each with a deformable backbone and Complete IOU loss to improve their detection performance. One architecture detects and classifies columns as regular columns (column without a merged cell) and irregular columns (group of regular columns that share a merged cell). The second architecture detects and classifies rows as regular rows (row without a merged cell) and irregular rows (group of regular rows that share a merged cell). Both architectures work in parallel to provide the results in a single inference. We show that utilizing TableStrDet to detect four classes of objects enhances the quality of table structure detection by capturing table contents that may or may not have hierarchical layouts on two public test sets. Under the TabStructDB test set we achieve 72.7% and 78.5% weighted average F1 score for rows and columns respectively. On the ICDAR 2013 test set we achieve 90.5% and 89.6% weighted average F1 score for rows and columns respectively. Furthermore, we show that TableStrDet has a higher generalization potential on the available datasets.

In the era of Industry 4.0, conventional supply chains are undergoing a transformation into digital supply chains with the wide application of digital technologies such as big data, cloud computing, and Internet of Things. A digital supply chain is an intelligent and value-driven process that has superior features such as speed, flexibility, transparency, and real-time inventory monitoring and management. This concept is further included in the framework of Supply Chain 4.0, which emphasizes the connection between supply chain and Industry 4.0. In this context, data analytics for supply chain management presents a promising research opportunity. This thesis aims to investigate the use of data analytics in supply chain decision-making, including modelling, monitoring, and optimization. First, this thesis investigates supply chain monitoring (SCMo) using data analytics. The goal of SCMo is to raise an alarm when abnormal supply chain events occur and identify the potential reason. We propose a framework of SCMo based on a data-driven method, principal component analysis (PCA). Within this framework, supply chain data such as inventory levels and customer demand are collected, and the normal operating conditions of a supply chain are characterized using PCA. Fault detection and diagnosis are implemented by examining the monitoring statistics and variable contributions. A supply chain simulation model is developed to carry out the case studies. The results show that dynamic PCA (DPCA) successfully detected abnormal behaviour of the supply chain, such as transportation delay, low production rate, and supply shortage. Moreover, the contribution plot is shown to be effective in interpreting the abnormality and identify the fault-related variables. The method of using data-driven methods for SCMo is named data-driven SCMo in this work. Then, a further investigation of data-driven SCMo based on another statistical process monitoring method, canonical variate analysis (CVA), is conducted. CVA utilizes the state-space model of a system and determines the canonical states by maximizing the correlation between the combination of past system outputs and inputs and the combination of future outputs. A state-space model of supply chain is developed, which forms the basis of applying CVA to detect supply chain faults. The performance of CVA and PCA are assessed and compared in terms of dimensionality reduction, false alarm rate, missed detection rate, and detection delay. Case studies show that CVA identifies a smaller system order than PCA and achieves comparable performance to PCA in a lower-dimensional latent space. Next, we investigate data-driven supply chain control under uncertainty with risk taken into account. The method under investigation is reinforcement learning (RL). Within the RL framework, an agent learns an optimal policy that maps the state to action during the process of interacting with the non-deterministic environment, such that a numerical reward is maximized. The current literature regarding supply chain control focuses on conventional RL that maximizes the expected return. However, this may be not the best option for risk-averse decision makers. In this work, we explore the use of safe RL, which takes into account the concept of risk in the learning process. Two safe RL algorithms, Q-hat-learning and Beta-pessimistic Q-learning, are investigated. Case studies are carried out based on the supply chain simulator developed using agent-based modelling. Results show that Q-learning has the best performance under normal scenarios, while safe RL algorithms perform better under abnormal scenarios and are more robust to changes in the environment. Moreover, we find that the benefits of safe RL are more pronounced in a closed-loop supply chain. Finally, we investigate real-time supply chain optimization. The operational optimization problems for supply chains of realistic size are often large and complex, and solving them in real time can be challenging. This work aims to address the problem by using a deep learning-based model predictive control (MPC) technique. The MPC problem for supply chain operation is formulated based on the state space model of a supply chain, and the optimal state-input pairs are precomputed in the offline phase. Then, a deep neural network is built to map the state to input, which is then used in the online phase to reduce solution time. We propose an approach to implement the deep learning-based MPC method when there are delayed terms in the system, and a heuristic approach to feasibility recovery for mixed-integer MPC, with binary decision variables taken into account. Case studies show that compared with solving the nominal MPC problem online, deep learning-based MPC can provide near-optimal solution at a lower computational cost. / Thesis / Doctor of Philosophy (PhD)

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This paper proposes a safety stock calculation function based on their distribution properties and create a guideline for the stock status optimization problem. The motivation for this paper originates the cooperation with a drilling tools company, Epiroc Drilling tools AB. The safety stock calculation divides all items into three distribution and design the safety stock for each types separately considering the influence of service level value and lead time. During the process of guideline design, complicated production chain framework is taken into account through recursive algorithm. The stock status combination which can give the minimum storage cost is the optimal guideline for stock item and non-stock item. The time for approximating the global minimum through exhaustive search is remarkably reduced due to the application of Parallel programming and statistical model.

This thesis deals with the problems of design and operation of process supply chains. Process supply chains face many challenges due to volatile market conditions, production and transportation delays, and stiff market competition, which ultimately affect their profitability. Supply chain management (SCM) is the process of managing the flow of materials and information within supply chain to optimize the SC performance. SCM is carried out using a hierarchical decision-making framework, where the top most layer looks at network design and the bottom-most layer deals with scheduling day-to-day activities. In this research, the systems engineering principles are applied to devise an improved methodology for supply chain optimization (SCO). First, we consider the design of supply chain in the presence of demand uncertainty. The representation of network topology plays an important role in deriving the optimal network design. In real practice, the shipping cost for transferring goods from one location to another is determined based on service time and quantity. More importantly, the cost associated with establishing a transportation linkage is relatively small for existing transportation infrastructure and can be changed if beneficial. The flexibility of changing the transportation routes is included in the network topology representation by the explicit inclusion of time limited transportation contract agreements. Further, the customer demand is volatile, and it is very difficult to predict accurately. To handle the demand uncertainty, a two-stage stochastic programming formulation is applied in the SC design approach. Next, we consider the problem of handling uncertainty in SC planning by applying a system engineering control principle, robust model predictive control (MPC). The uncertainty in model parameters (yield) and demand are captured by stochastic programming. In this approach, the planning activities are represented by a hybrid model with decisions governed by logical conditions/rulesets. An MPC based rolling horizon control framework is used to schedule the planning activities, where the SC performance is expressed using a multi-criterion objective comprising customer service and economics. The uncertainty in demand and yield are propagated by two mechanisms - an open-loop approach, and an approximate closed-loop strategy. Finally, we consider the problem of integration of SC planning and scheduling. Due to the use of different time scale models for planning and scheduling, the decision derived from the planning layer may result in infeasibility when those targets are implemented at the scheduling level, which ultimately affects the supply chain efficiency. To address this issue, we model tactical and operational planning activities using an integrated hybrid time modeling approach in which the first few planning periods are formulated using an operational planning model and the remaining time periods are modeled with a tactical planning model. The main rationale for formulating an integrated model is that customer demand forecast becomes less accurate for a future time, therefore making a detailed planning model unnecessary. A key benefit of using a hybrid modeling approach is that it avoids the problem of infeasibility encountered in the hierarchical decision framework, as well as the computational burden associated with the use of a detailed planning model over a long time horizon. We employ an MPC based rolling horizon framework as a tactical decision policy where the integrated model is used to predict the system behavior. / Thesis / Doctor of Philosophy (PhD)

<p>Process operability represents the ability of a process plant to operate satisfactorily away from the nominal operating or design condition, where flexibility and dynamic operability are two important attributes of operability considered in this thesis. Today's companies are facing numerous challenges, many as a result of volatile market conditions. Key to sustainable profitable operation is a robust process supply chain. Within a wider business context, flexibility and responsiveness, i.e. dynamic operability, are regarded as key qualifications of a robust process supply chain.</p> <p>The first part of this thesis develops methodologies to rigorously evaluate the dynamic operability and flexibility of a process supply chain. A model is developed which describes the response dynamics of a multi-product, multi-echelon supply chain system. Its incorporation within a dynamic operability analysis framework is shown, where a bi-criterion, two-stage stochastic programming approach is applied for the treatment of demand uncertainty, and for estimating the Pareto frontier between an economic and responsiveness criterion. Two case studies are presented to demonstrate the effect of supply chain design features on responsiveness. This thesis has also extended current paradigms for process flexibility analysis to supply chains. The flexibility analysis framework, where a steady-state supply chain model is considered, evaluates the ability to sustain feasible steady-state operation for a range of demand uncertainty.</p> <p>The second part of this thesis develops a decision-support tool for supply chain management (SCM), by means of a robust model predictive control (MPC) strategy. An effective decision-support tool can fully leverage the qualifications from the operability analysis. The MPC formulation proposed in this thesis: (i) captures uncertainty in model parameters and demand by stochastic programming, (ii) accommodates hybrid process systems with decisions governed by logical conditions/rulesets, (iii) addresses multiple supply chain performance metrics including customer service and economics, and (iv) considers both open-loop and closed-loop prediction of uncertainty propagation. The developed robust framework is applied for the control of a multi-echelon, multi-product supply chain, and provides a substantial reduction in the occurrence of back orders when compared with a nominal MPC framework.</p> / Master of Applied Science (MASc)

This research examines the impact of purchasing constraints and demand variability on working capital balances. The working capital accounts considered are creditors, debtors and raw material inventories. Purchasing constraints and demand uncertainty are defined. The supply chain of the South African cable industry, and one manufacturer in particular, and the challenges faced in the cable manufacturing process are discussed. To quantify the influences, a comparison between working capital accounts in the case of economic order quantity and actual purchasing practices is performed. A simulation model is developed to reproduce a larger sample of demand data, matching the cumulative probability density function of each cable type contained in the annual sales budget. The results show, that the working capital accounts react differently to changes in purchasing conditions and variations in demand, the most sensitive being raw material inventories. The study quantifies the influence of purchasing constraints on each working capital value. / Business Management / M.Com. (Business Management)