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Desert regions have emerged as ideal places for GW utility‐scale photovoltaic (PV) module installations because of their ultra‐large spaces, abundance of high‐irradiance sunshine hours, and clear sky. However, the harsh desert climate presents challenges to the reliability and bankability of PV modules. This review provides an in‐depth understanding of the unique desert parameters impact, desert‐induced degradation modes, status, and required properties of the bill of materials (BOMs) and suggestions for the development of desert standards. The review reveals that the climatic conditions in the desert are considerably harsher than those in the moderate climate. The main degradation mechanisms caused by the desert are ultraviolet (UV)‐induced discoloration, thermomechanical flaws of interconnects, and glass abrasion (because of soiling). The development of desert modules may necessitate the use of new‐generation modules with low‐temperature coefficients, high efficiency, high bifaciality, stability under UV light, and elevated temperatures. For the desert module application, options include advanced back sheets and encapsulants that are thermally and UV stable, free of acetic acid groups, and have a low water vapor transfer rate. The degradation modes induced by desert climate are not sufficiently addressed by the present environmental and safety standards through accelerated aging tests. As a result, this article provides a summary of current standards and recommends creating a new testing proposal called the "Hot Desert Test Cycle (HDTC)" sequence that is specific to the desert climate. This comprehensive review catalyzes the PV community to explore novel designs and develop desert PV modules while adhering to localized standards. [ABSTRACT FROM AUTHOR]

The increase of software supply chain threats has underscored the necessity for robust security mechanisms, among which the Software Bill of Materials (SBOM) stands out as a promising solution. SBOMs, by providing a machine-readable inventory of software composition details, play a crucial role in enhancing transparency and traceability within software supply chains. This empirical study delves into the practical challenges and solutions associated with the adoption of SBOMs through an analysis of 4,786 GitHub discussions across 510 SBOM-related projects. Through repository mining and analysis, this research delineates key topics, challenges, and solutions intrinsic to the effective utilization of SBOMs. Furthermore, we shed light on commonly used tools and frameworks for SBOM generation, exploring their respective strengths and limitations. This study underscores a set of findings, for example, there are four phases of the SBOM life cycle, and each phase has a set of SBOM development activities and issues; in addition, this study emphasizes the role SBOM play in ensuring resilient software development practices and the imperative of their widespread adoption and integration to bolster supply chain security. The insights of our study provide vital input for future work and practical advancements in this topic. [ABSTRACT FROM AUTHOR]

“Shift To Left” is the cornerstone of the successful implementation of DevSecOps. By testing projects for vulnerabilities in the early stages of development, teams can save overall costs before security issues reach the build phase. As one of the popular practices in “Shift To Left,” the Software Composition Analysis (SCA) system aims to leverage the Software Bill of Materials (SBOM) to enhance software supply chain security. However, the SBOM lacks mature generation and distribution mechanisms, requiring incentive measures to drive industry consensus. Additionally, the data and tools associated with the SBOM lack effective record‐keeping and monitoring, making it challenging to ensure data integrity and tool security. Traditional SCA systems treat SBOM as a regular data format for external service provision, yet fail to solve problems such as lack of shared platforms, inability to guarantee data integrity and tool security, as well as issues with poor interoperation compatibility. This paper introduces blockchain technology into the SCA system, utilizing smart contracts to provide core SBOM tool services and microservices to improve the operational efficiency of smart contract deployment and maintenance. The proposed SCA system effectively provides a shared platform for SBOM with reliable data integrity, guaranteed tool security, and good interoperability. [ABSTRACT FROM AUTHOR]

Blockchain and cloud computing continue to emerge and evolve as important technologies for data management. This has strategic implications for data stored in enterprise resource planning (ERP) systems. We use manufacturing bills of materials (MBOMs) as an example of critical data in ERP. MBOMs define the complex product structures of manufacturing enterprises. We survey five alternatives for storing, controlling, and auditing MBOMs in ERP: Oracle Blockchain Table; Oracle Blockchain Platform Enterprise Edition; Microsoft Azure SQL Database Ledger; SAP HANA Blockchain Ledgers; and IBM Blockchain Platform. We also consider cloud computing without blockchain technologies. We analyze the advantages and disadvantages of each alternative by its effect on the management of MBOMs. Although we use MBOMs in our illustrations, many of the concepts discussed also apply to other critical data stored in ERPs. Data Availability: Data are available from the public sources cited in the text. JEL Classifications: G31; G32; G33; M21. [ABSTRACT FROM AUTHOR]

Limited information about the demand for some of the resources needed to produce goods and services (e.g., incomplete and imperfect bills of materials) forces firms to use heuristics when planning resource capacity. We examine the performance of five heuristics: two drawn from practice, two that modify observed approaches, and one motivated by theory. We measure performance as the ratio of the expected cost of supply–demand mismatch from using a heuristic to the value in the full‐information solution. Numerical analysis shows that a simple heuristic that is common in practice—plan rigorously for a few "driver" resources with high‐quality information and use ratios (e.g., 0.25 indirect labor hours per machine hour) to project the capacities for the remaining "non‐driver" resources—is robust and efficient. Using more than one driver resource to plan for the same non‐driver resource delivers significant gains. Reducing measurement error with respect to the consumption of driver resources dominates the gain from reducing errors in other aspects. Indeed, with high measurement error, collecting information that reduces other sources of error could decrease overall performance. Finally, a greedy algorithm of choosing the most expensive resources as drivers is optimal. [ABSTRACT FROM AUTHOR]

Push and pull methods have been adopted for specific volume production and uncertainty scenarios in order to plan and control production. The further development of hybrid or integrated methods allows benefit to be drawn from opposing approaches. The literature concerning Demand-Driven Material Requirements Planning (DDMRP) proves its superiority under conditions of internal and external uncertainty for high-volume production compared to the most implemented push method (manufacturing requirements planning MRPII). Companies that have adopted this method, manufacture on average 10–15 parts per product, with 2 or 3 levels of bills of materials. In this paper, we evaluate the applicability of DDMRP in a complex manufacturing environment (e.g. products of four levels of bill of materials) in terms of customer satisfaction and stock levels. Buffered and non-buffered items clustered in seven types of decoupling structures contributed to this complexity. We developed a DDMRP model for planning and execution purposes, which was simulated in ARENA's discrete events software. We analysed the model's on-hand stock and delayed orders. DDMRP works effectively under the manufacturing conditions considered. It is found to prevent inventory stockouts and overstocks, reduce lead time by 41% and reduce stock levels by 18%. The success of this method; however, depends on the strategic positioning of the buffers. [ABSTRACT FROM AUTHOR]

In the course of increasing individualization of customer demand, configurable products are gaining importance. Nowadays, variant-specific bills of materials and routings for configurable products are created with the help of rule-based configuration systems, so-called low-level configuration systems. The rules and generic structures on which such configuration systems are based are created manually today. This is challenging because it can be difficult and sometimes impossible to directly transfer expert knowledge into those systems. Furthermore documents that have already been created by experts in the past such as bills of material and routings contain relevant information as well which may be exploited to compose configuration systems. However, in the literature, there are no approaches yet to systematically transfer expert knowledge into configuration systems or to consider existing documents. In addition, the creation of such configuration systems is prone to error due to their complexity. Although there are already numerous approaches to the formal testing of configuration systems, approaches based on data analysis to support the validation of such systems have not yet been considered. Therefore, in this paper an approach is presented to automatically create low-level configuration systems by means of exemplary variant-specific bill of materials and routings using machine learning. The super bill of materials and the super routing as well as the dependencies between the product characteristics and the components respectively the operations are learned. Furthermore, it is shown how errors in the input data as well as errors in the resulting low-level configuration system can be detected by means of anomaly detection. [ABSTRACT FROM AUTHOR]

In this paper, we study how the presence of coordination, competition and cost structures affect the product variety and pricing decisions in a two-stage supply chain. We consider vertically differentiated industries. Products reach the end customers via assembly companies positioned in the middle-tier between the producers and end customers. The assembly operations incur bill-of-materials (BOM) cost, which includes the cost of raw materials and components as well as the cost of producing, managing and synchronising supply, storage and distribution of these materials and components. We consider three coordination scenarios in the supply chain, and compare an uncoordinated supply chain with a horizontal and a vertical coordination scenario using the Nash equilibria of a multi-leader Stackelberg game between the producers and assembly companies. For exogenously given quality levels, we show that producers and assembly companies should either differentiate their product offering, or offer the high-end product only depending on the BOM cost. In addition, the uncoordinated scenario helps reduce the intensity of competition in the supply chain. For endogenous quality levels, partial product-differentiation equilibrium can emerge. Numerical experiments are performed to illustrate the impacts of BOM cost and quality levels on the equilibrium. [ABSTRACT FROM AUTHOR]

The customisation of a vehicle on a production line results from the assembly of several hundreds of alternative components. Bills of Material (BOM) are usually made for operational purposes (to define the list of components to be assembled on a vehicle), for planning (to anticipate component procurement needs) and for commercial needs (guide customer choice and prepare sales forecasts). In the automotive industry, the diversity of end products which results from this combinatorial process (several millions) is such as no solution proposed in the literature allows to easily list the BOMs for all these vehicles. In our paper, we describe and theorise a solution used for many years by several carmakers, which consists in introducing a commercial description of the products in addition to their common organic representation. We show that this solution is an extension of the generic and modular BOM theory, recognised to be the most advanced solutions proposed in the literature. While fully meeting commercial and production control needs, this product description paradigm has limitations when it comes to determining the Master Production Schedules (MPS) at end product level beyond the frozen horizon, as performed by several automakers. An alternative approach defining MPSs at alternative component level is proposed in the literature. However, this approach also has several drawbacks that we point out. To overcome these issues, we propose a number of possible strategic and organisational tracks of improvement. [ABSTRACT FROM PUBLISHER]

Two-stage hybrid flow shop (HFS) scheduling problem followed by single assembly machine is addressed in this paper. To produce the final product, parts need to be processed on the HFS stages and thereafter, several parts are joined under the assembly operations based on the predefined Bill of Materials of the product. The aim of this research is to find the schedule which minimises completion time of the last product, i.e. makespan. For the considered problem, lower bound, heuristic algorithms and two metaheuristic techniques based on artificial immune system are developed. Computational results demonstrate that the proposed lower bound and heuristic algorithms outperform the existent lower bounds and heuristic algorithms. [ABSTRACT FROM PUBLISHER]

In order to save individual virgin materials by recycling products, manufacturing companies must adopt new information technology to evaluate the environmental impact of their management. The transformative adoption of digital technology is referred to as digital transformation (DX), and the DX technique can be used to digitalize reverse supply chain management strategies for environmental issues. Before the use of the DX technique for a reverse supply chain, a decision maker (DM) was not be able to know the material types of the end-of-life (EOL) products based on their status until they had been recycled. However, using the DX technique, the DM is able to understand EOL product data such as the number and the amount of each material within the EOL products in advance. Therefore, the usage of the DX technique enables the DM to design the reverse supply chain network not only environmentally-friendly but also economical. In applying DX to supply chain networks, supply chain managers must cut costs and evaluate environmental impacts. In order to balance environmental and economic concerns, the DM can design a reverse supply chain network using a solving method for multi-criteria decision making such as linear physical programming (LPP). Using the LPP algorithm, the DM can calculate a harmonized weight for objective functions with a trade-off relationship. This study designs a reverse supply chain network for individual material recovery environmentally-friendly and economical by using LPP as one of the support methods in the era of DX. The academic contribution of this study is that the effect of objective functions for individual material types can be identified, and the practical contribution is that it enables us to find a reverse network solution based on individual materials being collected from EOL products recycled throughout numerical experiments with LPP. [ABSTRACT FROM AUTHOR]

Abstract This study complies with the reorganization of health care provider processes based on the cost of life cycle of the product. The aim is to determine the costs of treatment administration of extracorporeal dialysis and other treatments in extracorporeal systems. The strategy here was to apply for the first time the “bill of material” (BoM) method to the production and management of the services above. By using standard replacement flows, we build the “bill of materials” to calculate the cost of a CRRT (continuous renal replacement therapy) with anticoagulant. The standard cost of the process is thus obtained. The method here developed can be used for accounting purposes and for economic planning, as a tool for accurate statistical evaluation of medical care.

We propose a new optimisation approach to address a multi-period, inventory control problem for an assembly system under stochastic environment. Significant drawback in a construction of a real-life model for a multi-product, multi-component assembly system is the fact that the shortage cost is typically known for a final product while the optimisation is carried out on assembly components. In the cases when the same type of components assemble different types of finished products, this becomes insuperable obstacle for most of the known approaches. In addition, restrictive assumptions about component demand are common for number of published models. Therefore, we develop a framework, which has the ability to incorporate real shortage costs of the final products within the optimisation process and to incorporate more realistic assumptions about product demand. The aim is to define a completely new optimisation approach in the case of complex component’s interdependencies in assembly systems. The inclusion of the proposed model in the optimisation process is shown by the example of a known simple stochastic demand model. Simple SQL procedures have been presented which benefits from the relational structure of the problem. [ABSTRACT FROM PUBLISHER]

Under the computer-aided design (CAD) software architecture, this study aims to develop navigation processes for plastic injection mould manufacturing scheduling optimisation. Mould manufacturing is a job-shop scheduling problem, with components processing sequence under limited conditions. This study uses the search capabilities of the ant colony system (ACS) to determine a set of optimal schedules, under the condition of not violating the processing sequences, in order to minimise the total processing time and realise makespan minimisation. As the test results suggest, it can save up to 52% of manufacturing time, and also substantially shorten the processing time of the production plan. This study completes the algorithm steps and manufacturing process time estimation by operations on the navigation interface, and uses mould manufacturing scheduling to make optimised arrangements of finished components. The method can comply with the on-site manufacturing processes, improve scheduling prediction accuracy and consistently and efficiently integrate the optimisation scheduling system and mould manufacturing system. Visualised information of the scheduling results can be provided, thus allowing production management personnel to ensure smooth scheduling. [ABSTRACT FROM AUTHOR]

This paper suggests a new technique to facilitate consistent transition between planning and production stages of small lots of complex products (in quantities that do not justify full automation). Despite the usage of modern approaches such as design for assembly and design for manufacturing the typical product planning ends up before production planning begins, instead of having a unified seamless planning process. This paper presents a new three stage technique to fill the gap between product planning and production. The paper shows how to generate a rigorous production plan by: (1) extending the Bill-of-Materials to include additional required information, (2) constructing an assembly ‘method prototype’ and (3) using the ‘method prototype’ to calculate time standards for executing the assembly. A case study illustrates the proposed technique. [ABSTRACT FROM PUBLISHER]

In the Internet of Things (IoT) era, manufacturers collect and share data to provide products that are more functional and less expensive. This embedded manufacturers to construct the global supply chain comprising suppliers, factories and recyclers to assemble products at a lower cost. On the other hand, global warming has become a serious environmental issue, and CO2 emissions on the global supply chain should be visualized and reduced by life cycle assessment. However, CO2 emissions vary for each country because of disparities in the energy mix. Therefore, manufacturers need to select appropriate suppliers for specific components, especially to ensure a lower procurement cost of parts and material-based GHG (GreenHouse Gas) emissions. Additionally, the economic model in the world shifts to circular economy which includes recycling the products economically because of the regenerative use for materials. If the parts inside the end-of-life (EOL) products are recycled, CO2 emissions in the procurement stage can be recovered with recycling cost. Therefore, recyclers need a disassembly part selection that selects recycling or disposal for each part in order to recover CO2 emission and reduce recycling cost in the EOL stage. Thus, certain product data, such as GreenHouse Gas (GHG) emissions and costs, needs to be shared with not only suppliers/factories but also recyclers by IoT technology on the global supply chain for connecting supplier and disassembly part selections. This study proposes a decision support model for economical carbon recovery by connecting supplier and disassembly part selections on procurement and EOL stages. First, a bill of materials (BOM) is prepared using an Asian supplier selection with the 3D-CAD model and Life Cycle Inventory (LCI) database. Second, disassembled parts of the EOL assembly products from the BOM data are selected for either recycling or disposal using 0-1 integer programming with ε constraint method. Finally, the results of the disassembly part selection, in terms of CO2 emission reduction and costs are discussed.

This paper examines the capacitated lot-sizing and scheduling problem (CLSP) with sequence-dependent setup times, time windows, machine eligibility and preference constraints. Such a problem frequently arises in the semiconductor manufacturing industry by which this paper is motivated. A mixed integer programming (MIP) model is constructed for the problem. Two MIP-based fix-and-optimise algorithms are proposed in which the binary decision variables associated with the assignment of machines are first fixed using the randomised least flexible machine (RLFM) rule and the rest of the decision variables are settled by an MIP solver. Extensive experiments show that the proposed algorithms outperform the state-of-the-art MIP-based fix-and-optimise algorithms in the literature, especially for instances with high machine flexibility and high demand variation. [ABSTRACT FROM PUBLISHER]

Manufacturing planning and control (MPC) plays a significant role in effective planning of requirements and execution of those plans. It also influences various performance measures, in particular on-time delivery, which is dependent on many factors including manufacturing lead time. This paper aims to address key issues of infinite loading of resources, lack of forward planning, and limited simultaneous planning capabilities of MRP through a holistic approach to planning of various components and execution of those plans, based on integrated data structures. It is shown that integrated data structures, combining hierarchical single-level BOMs and sequential operations routings using a holistic approach to planning and execution enable reducing lead time by eliminating slack times associated with traditional methods. Furthermore, manufacturing lead time, based on integrated data structures is a true representation of operation times and is shorter compared with traditional lead time. Sensitivity analysis on lead-time with bills-of-materials (BOM) levels and floats shows that the holistic approach to planning and execution with integrated data structures is an effective way of improving lead-time, in particular when product structures involve many levels of BOMs and floats (slack times). The proposed holistic approach provides forward planning, simultaneous planning of components, and finite loading of resources. It is expected that the concept of integrated data structures be extended to model transaction data with process functions (e.g. production order cycle), which would improve overall cycle times by eliminating manual functions associated with current methods. [ABSTRACT FROM AUTHOR]

Most available studies on configuration focus on either sales configuration specifying functional features or production configuration addressing product components. It has been well recognised that automating most of the activities associated with specification, engineering, and process planning of customised products and their interactions is one key in achieving product customisation. Thus, treating sales configuration and product configuration separately may not contribute to product customisation from a systematic view although they may lead to the improvement of individual stages. Recognising this limitation of existing studies, in this paper, we propose integrated SAles, Product and Production (SAP2) configuration, which helps achieve product customisation from a holistic view. Its rationale lies in automating consistently sales, product and production configuration activities in one system. In view of the importance of configuration models, we focus on the model underpinning SAP2 configuration called generic bill of functions, materials and operations (GBoFMO) and discuss it in detail. As the core of SAP2 configuration, GBoFMO can provide companies with an insight into organising the large volumes of data and knowledge in the life cycle of product family development. We also report a case study of light passenger aircrafts to illustrate the GBoFMO. [ABSTRACT FROM AUTHOR]

Over the past two decades, globalized outsourcing in the semiconductor supply chain has lowered manufacturing costs and shortened the time-to-market for original equipment manufacturers (OEMs). However, such outsourcing has rendered the printed circuit boards (PCBs) vulnerable to malicious activities and alterations on a global scale. In this article, we take an in-depth look into one such attack, called the "Big Hack," that was recently reported by Bloomberg Buisnessweek. The article provides background on the Big Hack from three perspectives: an attacker, a security investigator, and the societal impacts. This study provides details on vulnerabilities in the modern PCB supply chain, the possible attacks, and the existing and emerging countermeasures. The necessity for novel visual inspection techniques for PCB assurance is emphasized throughout the article. Further, a review of various imaging modalities, image analysis algorithms, and open research challenges are provided for automated visual inspection. [ABSTRACT FROM AUTHOR]

Consumer electronic products have a complex life cycle, characterized by environmental, social, and economic impacts and benefits associated with their manufacturing, use, and disposal at end-of-life. Accurately analysing these trade-offs and creating sustainable solutions requires data about the materials and components that make up these devices. Such information is rarely disclosed by manufacturers and only exists in the open literature in disparate case study format. This study presents a comprehensive database of bill of material (BOM) data describing the mass of major materials and components contained in 95 unique consumer electronic products. Data are generated by product disassembly and physical characterization and then validated against external benchmarks in the literature. The study also contributes a reproducible framework for organizing BOM data so that they can be expanded as new products enter the market. These data will benefit researchers studying all aspects of electronics and sustainability, including material scarcity, product design, environmental life cycle assessment, electronic waste policy, and environmental health and safety. Measurement(s) consumer electronic product • bill of materials • material type Technology Type(s) Disassembly • weighing • characterization Factor Type(s) product category • product design Sample Characteristic - Environment manufactured product Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.12513695 [ABSTRACT FROM AUTHOR]

[Objectives] During ship design,designers often need to extract models of a zone,system or cabin from total ship models.[Methods] Aiming at the application demand for distilling cabin models and taking the Jupiter Tessellation(JT) models exported by FORAN as the research object,an automatic technique for 3D cabin model extraction is developed based on the JT format. By analyzing the composition of cabin models,the information embodied in the diversified models composing the cabin,the relationship between the cabin and that information,and the product code,zone number,construction segment number and coordinates of a cabin's bounding boxes are identified as the retrieving conditions of the cabin model. Based on predefined distilling terms by users and the objects' types,names and coordinates of JT models' bounding boxes in the Product Lifecycle Management(PLM)platform,all the parts contained in a cabin can be efficiently distilled from the model of the whole ship. Furthermore,all the obtained models are assembled as a single JT model which possesses the same assembly configuration as the ship's Bill of Materials(BOM)structure.[Results] Finally,a tool which can search for one or many cabin models is implemented on the basis of the proposed method with a JT lightweight model management system, [Conclusions] and the gained cabin's JT model can be further used for collocation harmonizing,clearance analysis,virtual review,etc.

In this study, a general comprehensive model is proposed for strategic closed-loop supply chain network design under interval data uncertainty. The proposed model considers various assumptions such as multiple periods, multiple products, and multiple supply chain echelons as well as uncertain demand and purchasing cost. In addition, bill of materials for each product is considered via a new approach in management of forward and reverse flows of products for producing new products and reusing or disassembling returned products. Uncertainty of parameters in the proposed model is handled via an interval robust optimisation technique. The model assumptions are well matched with decision making environments of food and high-tech electronics manufacturing industries. The factors that make these two industries similar are time-dependent properties of products such as prices and warehousing lifetime period. The computational results of solving the proposed model via LINGO 8 demonstrate efficiency of the proposed model in dealing with uncertainty in an agile manufacturing context. [ABSTRACT FROM PUBLISHER]

The current global competition requires companies to follow a demand driven, product-oriented manufacturing approach, on which manufacturing complexity increases as a result of product configuration. Even though different manufacturing complexity measures have been developed, none of them seem to notice the blocking effect a product's BOM imposes on the process flow. The main original contribution of this paper is the development of an entropic formulation to address this last issue. Its validity and usefulness is put to the test via a discrete-event simulation study of a job shop. Our findings show that the entropic formulation act as a fairly good trend indicator of the system's performance parameters increase/decrease. Future research will focus on developing correcting factors so the entropic formulation can be used as an estimator of the final values. [ABSTRACT FROM AUTHOR]

Product family design is currently facing a multitude of challenges, the main problem stemming from the diversity offered to consumers. To design a product family, designers have to define an efficient bill of materials which ensures product assembly within a predefined length of time in order to satisfy the synchronised delivery principle. In addition, the modules used to assemble the finished products have to be competitive in terms of logistical costs. The ability to anticipate the constraints associated with the production process and with transportation is consequently of great interest. In this paper, we focus on the process of identifying a set of modules to be used in the assembly of the finished product. The objective is to define the bill of materials for each product from the modules belonging to that set, and to assign these modules to distant facilities where they will be manufactured and then shipped to a nearby facility for final assembly within a specific time. We use a set partitioning formulations to represent the problem, and solve it by adapting a Tabu Search algorithm in which the assembly process and the supply chain design are considered at the same time. [ABSTRACT FROM AUTHOR]

This paper deals with a correlated storage location assignment problem by considering the production bill of material (BOM) information. Due to the large number of parts in a BOM, the picking capacity constraint is considered. A mathematical model is formulated and a multi-stage heuristic is proposed. The heuristic relaxes the close interrelationships within the original problem through an improvement algorithm and an iterated approach to incorporate the effect of BOM splitting. In order to evaluate the performance of the heuristic, numerical experimentation is conducted in a single-block-multi-aisles warehouse, by using a randomly generated data set. [ABSTRACT FROM AUTHOR]