Supply chain models for an assembly system with preprocessing of raw materials

An assembly line that procures raw materials from outside suppliers and processes the materials into finished products is considered in this research. An ordering policy is proposed for raw materials to meet the requirement of a production facility, which, in turn, must deliver finish products in a fixed quantity at a fixed time interval to the outside buyers. Two different types of raw materials, ‘unfinished’ and ‘ready-to-use’, are procured for the manufacturing system. The ‘unfinished raw materials’ are turned into ‘processed raw materials’ after preprocessing. In the assembly line, the ‘processed raw materials’ and the ‘ready raw materials’ are assembled to convert into the final products. A cost model is developed to aggregate the total costs of raw materials, Work-in-process, and finished goods inventory. Based on the product design and manufacturing requirement a relationship is established between the raw materials and the finished products at different stages of production. A non-linear integer-programming model is developed to determine the optimal ordering policies for procurement of raw materials, and shipment of assembly product, which ultimately minimize the total costs of the model. Numerical examples are presented to demonstrate the solution technique. Sensitivity analysis is performed to show the effects of the parameters on the total cost model. Future research direction is suggested for further improvement of the existing results

EOQ in a Just in Time (JIT) World: An Empirical Analysis of the Impact of EOQ Variables on Operating Profit: The Case of Nigerian Bottling Company Plc

With today’s uncertain economy, companies are searching for alternative methods to keep ahead of their competitors by effectively driving sales and by cost reduction. Big manufacturing companies – as well as other companies, do not stand a chance in today’s environment if they do not have an appropriate inventory control model intact. The Economic Order Quantity (EOQ) and Just in Time (JIT) have been used for many years, but yet some companies have not taken advantage of it. An Economic order quantity could assist in deciding what would be the best optimal order quantity at the company’s lowest price. Similarly JIT focuses on providing customers with stocks at the right time and with the right quantity thereby reducing in process inventory and carrying costs and maximizing profits at the same time (Gonzalez and Gonzalez, 2010). All these in place in any organization are known as its inventory management system which invariable needs to be as efficient as possible in other to reduce costs and translate in profit maximization. In recent times there has been a clarion call to abandon EOQ model in place of JIT.  Perhaps this is because of the perceived benefits of JIT which includes:  time reduction as well as improved flow of goods from warehouse to shelves which in turn leads to regular replenishment of stock amongst others.  However, one might be tempted to ask: is this call for abandonment justifiable? Using JIT does it actually reduce costs as well as lead to profit maximization in the organization?. This study looks at the relevance of EOQ Variables – Cost of goods purchased and overheads in impacting on the profitability of the firm. In doing this,  the relationship of increase in cost of goods purchased against Operating profit  as well as increase in Overhead against Operating profit  of  manufacturing  companies in Nigeria were compared. Using Nigerian Bottling Company (NBC) Plc as a case study, Twenty – Nine (29) Years financial statements (1980-2009) were analyzed and the relationships between these variables were compared using regression analysis.  It was found out that there is a relationship amongst these variables in NBC PLC.  This paper thereafter, suggests that rather than abandon EOQ for JIT, they should complement each other for effective inventory management and ultimately lead to profit maximization. Keywords: Economic Order Quantity (EOQ), EOQ Variables, Just In Time (JIT), Nigerian Bottling Company Plc and Regression Analysis

Design of Closed Loop Supply Chains

Increased concern for the environment has lead to new techniques to design products and supply chains that are both economically and ecologically feasible. This paper deals with the product - and corresponding supply chain design for a refrigerator. Literature study shows that there are many models to support product design and logistics separately, but not in an integrated way. In our research we develop quantitative modelling to support an optimal design structure of a product, i.e. modularity, repairability, recyclability, as well as the optimal locations and goods flows allocation in the logistics system. Environmental impacts are measured by energy and waste. Economic costs are modelled as linear functions of volumes with a fixed set-up component for facilities. We apply this model using real life R&D data of a Japanese consumer electronics company. The model is run for different scenarios using different parameter settings such as centralised versus decentralised logistics, alternative product designs, varying return quality and quantity, and potential environmental legislation based on producer responsibility.supply chain management;reverse logistics;facility location;network design;product design

ARPA Whitepaper

We propose a secure computation solution for blockchain networks. The correctness of computation is verifiable even under malicious majority condition using information-theoretic Message Authentication Code (MAC), and the privacy is preserved using Secret-Sharing. With state-of-the-art multiparty computation protocol and a layer2 solution, our privacy-preserving computation guarantees data security on blockchain, cryptographically, while reducing the heavy-lifting computation job to a few nodes. This breakthrough has several implications on the future of decentralized networks. First, secure computation can be used to support Private Smart Contracts, where consensus is reached without exposing the information in the public contract. Second, it enables data to be shared and used in trustless network, without disclosing the raw data during data-at-use, where data ownership and data usage is safely separated. Last but not least, computation and verification processes are separated, which can be perceived as computational sharding, this effectively makes the transaction processing speed linear to the number of participating nodes. Our objective is to deploy our secure computation network as an layer2 solution to any blockchain system. Smart Contracts\cite{smartcontract} will be used as bridge to link the blockchain and computation networks. Additionally, they will be used as verifier to ensure that outsourced computation is completed correctly. In order to achieve this, we first develop a general MPC network with advanced features, such as: 1) Secure Computation, 2) Off-chain Computation, 3) Verifiable Computation, and 4)Support dApps' needs like privacy-preserving data exchange

Semantic data integration for supply chain management: with a specific focus on applications in the semiconductor industry

Supply Chain Management (SCM) is essential to monitor, control, and enhance the performance of SCs. Increasing globalization and diversity of Supply Chains (SC)s lead to complex SC structures, limited visibility among SC partners, and challenging collaboration caused by dispersed data silos. Digitalization is responsible for driving and transforming SCs of fundamental sectors such as the semiconductor industry. This is further accelerated due to the inevitable role that semiconductor products play in electronics, IoT, and security systems. Semiconductor SCM is unique as the SC operations exhibit special features, e.g., long production lead times and short product life. Hence, systematic SCM is required to establish information exchange, overcome inefficiency resulting from incompatibility, and adapt to industry-specific challenges. The Semantic Web is designed for linking data and establishing information exchange. Semantic models provide high-level descriptions of the domain that enable interoperability. Semantic data integration consolidates the heterogeneous data into meaningful and valuable information. The main goal of this thesis is to investigate Semantic Web Technologies (SWT) for SCM with a specific focus on applications in the semiconductor industry. As part of SCM, End-to-End SC modeling ensures visibility of SC partners and flows. Existing models are limited in the way they represent operational SC relationships beyond one-to-one structures. The scarcity of empirical data from multiple SC partners hinders the analysis of the impact of supply network partners on each other and the benchmarking of the overall SC performance. In our work, we investigate (i) how semantic models can be used to standardize and benchmark SCs. Moreover, in a volatile and unpredictable environment, SC experts require methodical and efficient approaches to integrate various data sources for informed decision-making regarding SC behavior. Thus, this work addresses (ii) how semantic data integration can help make SCs more efficient and resilient. Moreover, to secure a good position in a competitive market, semiconductor SCs strive to implement operational strategies to control demand variation, i.e., bullwhip, while maintaining sustainable relationships with customers. We examine (iii) how we can apply semantic technologies to specifically support semiconductor SCs. In this thesis, we provide semantic models that integrate, in a standardized way, SC processes, structure, and flows, ensuring both an elaborate understanding of the holistic SCs and including granular operational details. We demonstrate that these models enable the instantiation of a synthetic SC for benchmarking. We contribute with semantic data integration applications to enable interoperability and make SCs more efficient and resilient. Moreover, we leverage ontologies and KGs to implement customer-oriented bullwhip-taming strategies. We create semantic-based approaches intertwined with Artificial Intelligence (AI) algorithms to address semiconductor industry specifics and ensure operational excellence. The results prove that relying on semantic technologies contributes to achieving rigorous and systematic SCM. We deem that better standardization, simulation, benchmarking, and analysis, as elaborated in the contributions, will help master more complex SC scenarios. SCs stakeholders can increasingly understand the domain and thus are better equipped with effective control strategies to restrain disruption accelerators, such as the bullwhip effect. In essence, the proposed Sematic Web Technology-based strategies unlock the potential to increase the efficiency, resilience, and operational excellence of supply networks and the semiconductor SC in particular

Two intracellular and cell type-specific bacterial symbionts in the placozoan Trichoplax H2

Placozoa is an enigmatic phylum of simple, microscopic, marine metazoans(1,2). Although intracellular bacteria have been found in all members of this phylum, almost nothing is known about their identity, location and interactions with their host(3-6). We used metagenomic and metatranscriptomic sequencing of single host individuals, plus metaproteomic and imaging analyses, to show that the placozoan Trichoplax sp. H2 lives in symbiosis with two intracellular bacteria. One symbiont forms an undescribed genus in the Midichloriaceae (Rickettsiales)(7,8) and has a genomic repertoire similar to that of rickettsial parasites(9,10), but does not seem to express key genes for energy parasitism. Correlative image analyses and three-dimensional electron tomography revealed that this symbiont resides in the rough endoplasmic reticulum of its host's internal fibre cells. The second symbiont belongs to the Margulisbacteria, a phylum without cultured representatives and not known to form intracellular associations(11-13). This symbiont lives in the ventral epithelial cells of Trichoplax, probably metabolizes algal lipids digested by its host and has the capacity to supplement the placozoan's nutrition. Our study shows that one of the simplest animals has evolved highly specific and intimate associations with symbiotic, intracellular bacteria and highlights that symbioses can provide access to otherwise elusive microbial dark matter

Automatic definition of engineer archetypes: A text mining approach

With the rapid and continuous advancements in technology, as well as the constantly evolving competences required in the field of engineering, there is a critical need for the harmonization and unification of engineering professional figures or archetypes. The current limitations in tymely defining and updating engineers' archetypes are attributed to the absence of a structured and automated approach for processing educational and occupational data sources that evolve over time. This study aims to enhance the definition of professional figures in engineering by automating archetype definitions through text mining and adopting a more objective and structured methodology based on topic modeling. This will expand the use of archetypes as a common language, bridging the gap between educational and occupational frameworks by providing a unified and up-to-date engineering professional figure tailored to a specific period, specialization type, and level. We validate the automatically defined industrial engineer archetype against our previously manually defined profile

Generic models for the integrated design of domestic and global supply chain networks with remanufacturing

This research focuses on the modeling of strategic supply chain network design. Several comprehensive mixed-integer-programming models are developed for the strategic integrated design of domestic and global supply chain networks with remanufacturing capacity. The models allow simultaneous determination of supplier selection, manufacturing and distribution facility selection and allocation, production quantities, transportation flows, reverse distribution facility selection, and disassembly plant allocation. Additionally, our models incorporate bill of material (BOM) both in the manufacturing process and in disassembly process. Management policies are also considered in the model formulation so that specific management choices, such as multi- sourcing strategy or single sourcing strategy, can be fulfilled in the strategic supply chain network design. Global factors considered in the model include currency exchange rates, transfer prices, allocation of transportation costs, local content requirements, local income taxes, and tariffs. The models are verified by medium-sized numerical examples. Compared to previous literature, the proposed models have two distinctive features. First, the corresponding integrated logistics problem of a global supply chain is formulated with a generalized mathematical form, and thus is not limited to applications for specific industries. Such a methodological measure is rare in previous literature, and has exhibited its potential advantages in addressing complicated global supply chain problems. Second, remanufacturing factors oriented from the enforcement of corresponding governmental regulations for environmental protection are considered in the proposed model. Thus, the corresponding effects may help to determine solution alternatives to improve the performance of a global supply chain with remanufacturing capacit

Modeling that Leads to the Prediction of Photocatalytic Coatings Characterization

One of the abundant sources of energy on earth is a solar energy which is the clean and safest energy source. It is also known as universal energy, the most important source of renewable energy available today. On realizing that the light source has a crucial role in daily life, several scientists and researchers from centuries ago have studied to establish photo induced systems and utilized them. Long after the knowledge of thermal energy, photovoltaic energy, and photosynthesis in plants, two prominent scientists, Fujishima and Honda, have discovered the electrochemical photolysis of water with the Titanium dioxide electrode which was reported in Nature by Analogy with a natural photosynthesis in 1972 [21]. This discovery leads to the development of heterogeneous photocatalysis in various applications including air and water purification treatment and organic synthesis. Since then it has drawn the wide scientific interest of many academicians and commercial industries. Over the past few decades, the extensive study focused on photocatalysis. Titanium dioxide photocatalysis has been promoted as a leading and emerging green technology for air and water purification systems because of its versatile nature being non-toxic environment friendly, stability to photocorrosion, low cost and potential to function under solar light better than any other artificial light source. It can be exploited for both harvesting solar energy and the destruction of organic and inorganic pollutants, even micro-organisms, in water and air by solar light irradiation. Recently several researches have been focused on improving the operating efficiency of the photocatalytic process on both the mechanistic aspects and other operating parametric aspects including catalyst concentration load, irradiation time, relative humidity, reaction temperature and many more; however, rate limiting properties still remain elusive. Many issues hindering its application on large scale production still exists. Several chemists and materials scientists focused mainly on the synthesis of more efficient materials and the investigation of degradation mechanism while engineers and computational scientists focused mainly on the development of appropriate models both mathematical and statistical, graphical representations to evaluate the intrinsic kinetics parameters and to build the prediction models that allow the scale up or re-design of efficient large-scale photocatalytic reactors. The number of raw data points and raw data files collected by sensors during several experiments grows rapidly over a time. With a large number of raw data sets, a tool to handle such a large raw data set is a practical necessity both for visualization and data analysis along with the computing power. With an aim to build the prediction model of the photocatalytic characterization, scientific computing tools NumPy, SciPy, Pandas, and Matplotlib based on the python programming language are used. For graphical analysis and statistical significance, a custom tool was built using the wxPython package

Developing a Decision Support System for Integrated Decision-Making in Purchasing and Scheduling under Lead Time Uncertainty

Decision-making in supply chain management is complex because of the relations between planning tasks from different stages and planning levels. Uncertainties such as unpredictable supplier lead times and supply chain disruptions further complicate decision-making. Considering the case study of a company in printed circuit board assembly, a three-level concept is proposed that includes a decision support system. The global single-source supply network is characterized by highly variable lead times. Hence, the company maintains high inventory levels to prevent running out of stock. The decision support system considers the purchasing and scheduling decision problems in an integrated way. The prototypical implementation of the purchasing algorithm uses a genetic algorithm that recommends reorder days and order quantities using a simulation model. In addition, it evaluates the risks of the recommended solution by calculating the probability of stockouts for each order cycle