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While the AEC industry is moving towards digitalization off-site rebar prefabrication became a common practice. Now most companies use a long-established standard order processing method, where the customer submits 2D paper or PDF-based drawings. Subsequently, the manufacturers are obligated to make additional detailing, redrawing, calculations, and preparation of other required information for manufacturing. Thus, in this typical scenario, there is a great repetition of the same tasks, with the obvious loss of time and increased likelihood of human error. However, improvements can be made by the application of advanced digital production workflow and the use of open BIM standards (e.g., IFC, XML, BVBS). Therefore, this paper presents the typical data flow algorithm in contrast to the automated data flow for reinforcement manufacturing. Further, the two approaches are compared and analyzed based on Multi-Criteria Decision Making (MCDM) methods. The results have shown promising prospects for companies willing to automate their data flow processes by the use of 3D drawings and digital data from the BIM model in their plants.

Trolley line picking is a special warehousing system particularly suited to fulfill high-volume demands for heavy stock keeping units (SKUs). In such a system, unit loads of SKUs are positioned along a given path passed by automated trolleys, i.e., carriers hanging from a monorail or automated guided vehicles. Once a trolley reaches a requested SKU, it automatically stops and announces the requested items on a display. This is the signal for an accompanying human picker to put the demanded items onto the trolley. In this way, picking continues until, at the end of the path, the current picking order is complete and the trolley moves onward to the shipping area. Meanwhile, the picker rushes back to meet the subsequent trolley associated with the next picking order. The picking performance of the trolley line system is mainly influenced by the picker’s unproductive walking from SKU to SKU during order processing and back to the next trolley when switching to the next order. In this paper, we investigate how the storage assignment of SKUs along the path and the order sequence influence picking performance. Specifically, we explore the positive effect of duplicating SKUs and storing them at multiple positions along the path. We formulate the interdependent storage assignment and order sequencing problems and introduce a decomposition heuristic. Our computational study investigates the solution performance of this procedure and shows that SKU duplication can considerably improve picking performance.

Purpose Fast fulfilment is a key performance measure in online retail, and some retailers have achieved faster times by adopting new designs in their order fulfilment infrastructure. This research empirically confirms and quantifies the fulfilment time advantage that Amazon has achieved, relative to other online retailers. The purpose of this paper is to investigate three research questions: what is the overall mean fulfilment time difference between the new logistics designs of Amazon and the alternative designs of other retailers? For each order what is the distribution of the fulfilment time difference? What is the difference in fulfilment time by product category, price and size? Design/methodology/approach This research uses an empirical method to evaluate the fulfilment time performance of consumer orders made through the Amazon website and one or more competing online retailers. For 1,000 different products two fulfilment times, one at Amazon and another at a competing omnichannel retailer, are recorded. The analysis is then focused on the comparison between this paired data. Findings The research confirms that the new logistics methods, including physical facilities, distribution networks and intelligent order processing methods, have resulted in faster order fulfilment times. The performance, though, is not universally dominant and for 33 per cent of orders, the difference is 1 day or less. The fulfilment time difference varied by product, category, price or size. Practical implications The ongoing transformation of fulfilment and logistics operations at online retailers has generated several new research questions. This includes the need to confirm the fulfilment efficiency of the new designs and specify time targets. This paper identifies the fulfilment time gap between new and traditional operations. The results suggest that store-based or distribution centre-based fulfilment strategies may not match the new designs. Originality/value The study provides a quantitative analysis of the fulfilment time differentials in online retailing. The critical role of fulfilment logistics in the rapidly growing online retail industry can now be better modelled and studied. The survey method representing a single buyer allows for order pair equivalency and eliminates order bias. The results suggest that new warehousing and logistics designs can lead to significantly faster fulfilment times.

With the development of E-commerce logistics industry, higher requirements towards logistics and warehousing emerge, including the shorter delivery time and higher operation accuracy when processing massive SKUs and orders. However, the existing full-automated solutions cannot meet the variety and variation of E-commerce warehousing demands. Inspired by cellular manufacturing, a flexible and reconfigurable warehousing system called cellular warehousing is put forward. Although several articles have illustrated this new concept, few systematic studies on cell formation, scenario design and physical emulation model exist. This study develops this conceptual model with three-fold objectives. First, the cell formation and operation flow of cellular warehousing are deciphered. Second, application scenarios are given including intra-cell and inter-cell operation, with the background of different types of order processing. At last, a physical emulation model is created as test-bed for further qualitative evaluation and insight abstraction.

Short response time for order processing is important for modern warehouses, which can be potentially achieved by adopting appropriate processing policy. The parallel processing policy have advantages in improving performance of many autonomous storage and retrieval systems. However, researchers tend to assume a sequential processing policy managing the movement of independent resources in shuttle-based compact storage systems. This paper models and analyses a single-tier of specialized shuttle-based compact storage systems under parallel processing policy. The system is modeled as a semi-open queueing network with class switching and the parallel movement of shuttles and the transfer car is modeled using a fork-join queueing network. The analytical model is validated against simulations and the results show our model can accurately estimate the system performance. Numerical experiments and a real case are carried out to compare the performance of parallel and sequential processing policies. The results suggest a critical transaction arrival rate and depth/width ratio, below which the sequential processing policy outperforms the parallel processing policy. However, the advantage of sequential processing policy is decreasing with the increasing of shuttle number, transaction arrival rate and depth/width ratio. The results also suggest an optimal depth/width ratio with a value of 1.75 for minimizing the expected throughput time in the real system. Given the current system configurations, the parallel processing policy should be considered when the number of shuttles is larger than 2 or the transaction arrival rate is larger than 24 per hour.

In recent years, with the rapid development of information technology and the promotion of emerging media, e-commerce has risen rapidly. As a new business model, e-commerce has broken the shackles of time and space, and has promoted the trade links between countries. As a big trading country, the development of cross-border e-commerce has greatly promoted the import and export trade in China. Cross border e-commerce is a kind of trade behavior in which different customs entities, through the information network technology means of Internet platform, achieve commodity trading and online transaction for the purpose of commodity exchange. We analyze and simulate the intelligent cross-border commodity trading platform "Columbia" and analyze the preliminary products. "Columbia" has realized an intelligent cross-border commodity trading platform with innovative mode, providing a one-stop intelligent e-commerce solution from source development, procurement, quality inspection, inventory management, warehousing management, order processing to global logistics distribution, promoting import and export trade, balancing the benefits of brands and borrowers, and reducing the risks of both sides. Through practice, we come to the conclusion that the intelligent cross-border commodity trading platform is an inevitable trend of cross-border e-commerce. On the basis of a rich collection of commodities, it can provide the best cross-border shopping experience for the majority of users around the world. Additional Keywords and Phrases: cross-border e-commerce, Intelligent, cloud computing, SWOT analysis

In modern e-commerce, warehouse management is very crucial for overall service quality. Moreover, the cost of order picking in outbound logistics is estimated to be more than half of the total operating cost in warehouse activity. This work studies the factors of the order processing on average customer order throughput time where the picking, packing, and sorting functions are executed separately by either a single operator or multiple parallel operators. The proposed staff management by using the queuing theory is demonstrated. The data comprise a complete operational history of an e-commerce company, order by order, over 3 months in the year 2020. The queuing theory and statistic model are used to study this activity of the warehouse. To evaluate the efficiency of these operations, the simulation method is utilized. The result shows that the simulation is adequate in approximating the optimal number of pickers and packers. This work is explained generally to be practical for business leading to future research opportunities and giving more solutions from the existing technology supporting and solving the problems.

Reasonable design of pricing and clearing mechanism of regulation service has a great influence on improving regulation performance of units, improving regulation service quality, promoting the optimal allocation of regulation resources. In order to further stimulate units to improve regulation performance, gradually attract high-quality regulation resources and optimize the allocation of regulation resources, typical province in East China has designed and ran a regulation service market. This paper analyzes the pricing, clearing mechanism and settlement price of regulation market in typical province of East China. From the perspective of the cost of regulation resources, the basis of regulation mileage and capacity bid is analyzed, Based on the principle of regulation order processing, the mathematical model to evaluate and measure the regulation performance score is derived, the adjustment of regulation bid and clearing mechanism based on the regulation performance are discussed, and explains the clearing mechanism of the hour-ahead regulation market and the pricing and settlement mechanism of the real-time FM market. Through the optimization algorithm, the opportunity cost calculation formula and its simplified model is provided. Finally, the effect of the pricing mechanism in improving the regulation performance is verified through the case analysis of the typical province.

To encourage users to buy more products, many online stores offer coupons. When a customer finds that the price of the products she/he wants to order is below the threshold of a coupon, she/he might want to place the order together with others to meet this threshold and enjoy more instant rebates. However, to conduct these orders and deliver products to receivers, users may need to pay extra delivery costs. When an order comprises several receivers’ requests, the products in the order should first be delivered from stores to an assigned warehouse, packed into different packages, and delivered to the different receivers. It may be costly than directly delivering products from stores to receivers. For the benefits of buyers, we propose a platform, the Crowdrebate platform, which collects requests from users, groups requests into a set of orders to get more rebates, and relays products to different receivers in an order. The platform will make a profit by getting a proportion from the benefit of the receiver (defined as the rebate minus the extra cost) of orders as its revenue. In this paper, we define the Crowdrebate problem, which aims to maximize the benefit of receivers. We prove the NP-hardness of the Crowdrebate problem. Therefore, we propose a heuristic solution to address the problem. Moreover, we evaluate the effectiveness and efficiency of our algorithm via comprehensive experiments.

Military logistics is a complex process where response times, demand uncertainty, high variety of material references and cost effectiveness turn decisive for combat capability. Considered as the bridge between the deployed forces and the industrial base that provides materials and services that the forces needed to accomplish their mission, capacity and efficiency of delivery are required for its processes. The required flexibility could only be achieved by improving the Supply Chain Management (SCM) in order to optimize delivery lead times. To cope with these requirements, lean thinking can be extended to military organizations. This research justifies and proposes the use of Lean Six Sigma (LSS) methodologies from manufacturing to optimize logistics processes in the defense sector. In particular, the article presents the benefits and results obtained using Value Stream Analysis and DMAIC (Define, Measure, Analyze, Improve, Control) problem-solving methodology to improve the order processing lead-time as key performance indicator of a military unit delivery fulfilment.

Different from the previous articles on multi-echelon cold chain system, in addition to traditional transportation/distribution, warehousing and inventory control, DC value-added services, such as joint order processing, product processing and customizing, picking and assorting, joint distribution, etc. are employed to improve the performance of the cold chain system. Case study and sensitivity analysis demonstrate that (1) DC revenue is mainly from processing and customizing service for all kinds of products; (2) Of the value added services, picking and assorting cost has a significant effect on the total profit. These conclusions present good theoretical and managerial insights.

Over the years, the textile industry has become increasingly important, both domestically and internationally, owing to its great contribution to the economy. Therefore, with prior planning of resources and times, companies now need to increase each of their processes' efficiency to achieve the maximum potential in the shortest period of time. Based on this, order processing times become more relevant because they are critical for fulfilling this objective. Companies want to prevent cost overruns and sanctions. Therefore, they seek to implement improvements that may provide greater sustainability and reliability to both their production processes and end customers. For these reasons, this study proposes the application of Lean tools such as SMED, Kanban, 5S, and standardization based on the Deming Continuous Improvement approach. The main objective is to increase On Time In Full (OTIF) service levels from 56% to 63% by increasing the number of orders delivered on time. The case study is conducted in a fishing net textile company. This study is motivated by the lack of in-depth studies executed in this significant subsector, which exhibits a great development potential.

In a dynamic environment, companies face the challenge of increasing customer requirements. Today’s customers expect fast delivery of affordable and to their needs tailored products. As a result, companies need to act more flexible and thus move away from pure make-to-stock and make-to-order productions described in literature. Shifting the customer order decoupling point to in-between order processing strategies such as assemble-to-order can increase the probability of meeting the customised demand on time. In addition, companies in practice use a variety of different methods for processes like order generation, order release, sequencing and lot sizing to adjust their chosen order processing strategy to fit their individual needs. An example known from industry regarding such hybrid order processing strategy is a make-to-order production with fixed lot sizes. The share of a batch, which is not needed to satisfy the customer’s order, is stored. These leftover products allow very short delivery times for small order quantities. In this paper, possibilities to generate such hybrid order processing strategies are identified and structured. Causal diagrams and logistic models are used to analyse the effects of hybrid order processing strategies on economic and logistic objectives.

The Order Processing in Parallel Picking Workstations is an optimization problem that can be found in the industry and is related to the order picking process in a warehouse. The objective of this problem is to minimize the number of movements of goods within a warehouse in order to fulfill the demand made by the customers. The goods are originally stored in containers in a storage location and need to be moved to a processing area. The processing area is composed of several identical workstations. We are particularly interested in minimizing the time needed to fulfill all demands, which corresponds to the highest number of container movements to any given workstation. This problem is \(\mathcal {NP}\)-Hard since it is a generalization of the well-known Order Processing in Picking Workstations which is also known to be \(\mathcal {NP}\)-Hard. In this paper, we provide a mathematical formulation for the problem and additionally, due to its hardness, we have also developed several heuristic procedures based on Variable Neighborhood Search, to tackle the problem. The proposed methods have been evaluated over different sets of instances.

An organizational process is a series of interconnected sub-processes. A process can be transactional as well as non-transactional. Transactional processes such as order processing, inventory control, order preparation/generation, credit checking/rating, mortgage application processing, and admission process.

The maintenance of serial order information is a core component of working memory (WM). Many theoretical models assume the existence of specific serial order mechanisms. Those are considered to be independent from the linguistic system supporting maintenance of item information. This is based on studies showing that psycholinguistic factors strongly affect the ability to maintain item information, while leaving order recall relatively unaffected. Recent language-based accounts suggest, however, that the linguistic system could provide mechanisms that are sufficient for serial order maintenance. A strong version of these accounts postulates serial order maintenance as emerging from the pattern of activation occurring in the linguistic system. In the present study, we tested this assumption via a computational modeling approach by implementing a purely activation-based architecture. We tested this architecture against several experiments involving the manipulation of semantic relatedness, a psycholinguistic variable that has been shown to interact with serial order processing in a complex manner. We show that this activation-based architecture struggles to account for interactions between semantic knowledge and serial order processing. This study fails to support activated long-term memory as an exclusive mechanism supporting serial order maintenance.

In the previous chapter, we worked on building our basket – no e-commerce site is any good, though, without some means to pay for our products! It's time for us to add in a checkout and payment process: the question is, how will it look?

With the intensification of market competition and the development of market globalization, the efficiency of supply chain management orders has become an important part of enterprise competition resources. The competition among enterprises is fierce. To achieve effective customer response quickly, the time for supply chain order management is minimized, and refine the order processing process. This article introduces the strategy research of supply chain management order based on a reinforcement learning algorithm. This article first combines the reinforcement learning algorithm and deep learning algorithm, using the optimal decision-making ability of reinforcement learning algorithm and deep learning algorithm. The combination of data perception and the optimal ability to analyze examine the data of the order process, order cycle, and order delivery process of the supply chain order management and give the optimal decision. The supply chain order management process conducts questionnaire surveys and seminars to understand the current process of supply chain order management and the problems derived from the analysis of data based on the deep learning algorithm. Finally, through the output of the optimal strategy of the reinforcement learning algorithm, the supply chain order management process was improved, and the satisfaction survey was conducted again. The survey showed that the satisfaction was improved, and the satisfaction reached more than 90%.

In this research, we study an extended version of the joint order batching and scheduling optimization for manual vegetable order picking and packing lines with consideration of workers’ fatiguing effect. This problem is faced by many B2C fresh produce grocers in China on a daily basis which could severely decrease overall workflow efficiency in distribution center and customer satisfaction. In this order batching and sequencing problem, the setup time for processing each batch is volume-dependent and similarity dependent, as less ergonomic motion is needed in replenishing and picking similar orders. In addition, each worker’s fatiguing effect, usually caused by late shift and repetitive operation, which affects order processing times, is assumed to follow a general form of logistic growth with respect to the start time of order processing. We develop a heuristic approach to solve the resultant NP-hard problem for minimization of the total completion time. For order batching, a revised similarity index takes into account not only the number of common items in any two orders but also the proportion of these items based on the vegetable order feature. To sequence batches, the genetic algorithm is adapted and improved with proposed several efficient initialization and precedence rules. Within each batch, a revised nondecreasing item quantity algorithm is used. The performance of the proposed heuristic solution approach is evaluated using numerical instances generated from practical warehouse operations of our partnering B2C grocer. The efficiency of the proposed heuristic approach is demonstrated.

Computer simulations of manufacturing processes are in widespread use for optimizing production planning and order processing. If unforeseeable events are common, real-time decisions are necessary to maximize the performance of the manufacturing process. Pre-trained AI-based decision support offers promising opportunities for such time-critical production processes. Here, we explore the effectiveness of deep reinforcement learning for real-time decision making in a car manufacturing process. We combine a simulation model of a central production part, the line buffer, with deep reinforcement learning algorithms, in particular with deep Q-Learning and Monte Carlo tree search. We simulate two different versions of the buffer, a single-agent and a multi-agent one, to generate large amounts of data and train neural networks to represent near-optimal strategies. Our results show that deep reinforcement learning performs extremely well and the resulting strategies provide near-optimal decisions in real-time, while alternative approaches are either slow or give strategies of poor quality.

To master ongoing market competitiveness, manufacturing companies try to increase process efficiency through process improvements. Mapping the end-to-end order processing is particularly important, as one needs to consider all order-fulfilling core processes to evaluate process performance. However, process mapping in manufacturing companies is mostly applied in partial processes and not on the end-to-end order processing. Process mining provides a data-based description of processes and their performance and thus objectively and effortlessly maps real as-is processes. The data basis for process mining is an event log. Nevertheless, the generation of an event log in end-to-end order processing is not as trivial as in partial processes, as different data from different information systems must be merged. This paper discusses the development of a data model through an Action Design Research (ADR) method, derived and validated across ADR-cycles. The data model presents, which data can be extracted from integrated database sources to create the required event log for process mining in end-to-end order processing.

This paper presents a case study of building of a cloud factory, a name given to digital order processing. This is achieved with the help of a Web-based interface as front end to capture inputs. The inputs are processed by a set of back end programs. The output will be the price and can trigger order placement if confirmation is received. This is an automation of order processing and plays an important role in future smart manufacturing regime under Industry 4.0. It widens the reach to customers and helps deliver value. The proposed model has been tested successfully.

Agriproducts have the characteristics of short lifespan and quality decay due to the maturity factor. With the development of e-commerce, high timelines and quality have become a new pursuit for agriproduct online retailing. To satisfy the new demands of customers, reducing the time from receiving orders to distribution and improving agriproduct quality are significantly needed advancements. In this study, we focus on the joint optimization of the fulfillment of online tomato orders that integrates picking and distribution simultaneously within the context of the farm-to-door model. A tomato maturity model with a firmness indicator is proposed firstly. Then, we incorporate the tomato maturity model function into the integrated picking and distribution schedule and formulate a multiple-vehicle routing problem with time windows. Next, to solve the model, an improved genetic algorithm (the sweep-adaptive genetic algorithm, S-AGA) is addressed. Finally, we prove the validity of the proposed model and the superiority of S-AGA with different numerical experiments. The results show that significant improvements are obtained in the overall tomato supply chain efficiency and quality. For instance, tomato quality and customer satisfaction increased by 5% when considering the joint optimization, and the order processing speed increased over 90% compared with traditional GA. This study could provide scientific tomato picking and distribution scheduling to satisfy the multiple requirements of consumers and improve agricultural and logistics sustainability.

COVID-19 made the world stop, with people trapped inside their homes and governments trying to restrict the public movement. However, to accomplish this, one big problem that emerged and outscored everything else was catering to the day to day necessary items of the people without human involvement. In this regard, we propose a blockchain-enabled secure communication framework for delivering the goods in COVID-19 like scenarios by leveraging the drones that are available with commercial retail providers. The blockchain scheme is used to create smart contracts to build the trust of buyers and sellers on the framework as the payments are made through the smart contract executions. The blockchain based order processing ensures the integrity and authenticity of the information. Moreover, a communication model is presented along with the order, delivery and payment phases. The results prove the effectiveness of the proposed scheme by evaluating it based on gas price, transaction time, and mining time.

Todays’ increasing market volatility and product variety result in growing business processes complexity. To master the arisen challenges of high process complexity, the process performance description of the end-to-end order processing process (ETEOPP) is crucial. With the trend of digitalization in manufacturing companies, an increasing availability of data is created that can be used to master process complexity by data-based methods. One suitable method is process mining (PM), which offers a continuous analysis of event data from business information systems. This paper aims to describe the minimum viable dataset to thoroughly evaluate the process performance in ETEOPP by PM. Therefore, process performance indicators (PPI) are first scientifically derived by a systematic literature review and afterward defined across the ETEOPP as quantifiable parameters based on processes and data. By doing so, the required event log attributes, as well as corresponding data requirements, are presented and information systems for data extraction are pointed out.

Desde a origem até o consumo, a gestão logística acompanha a entrega de bens e serviços com a finalidade de responder as necessidades dos clientes. Todavia, a ineficiência na integração das atividades logísticas poderá contribuir de forma negativa na lucratividade do empreendimento. O elemento central deste artigo foi identificar quais os principais desafios na gestão logística de integração das atividades primárias e de apoio em uma microempresa fabricante de biscoitos. O estudo justifica-se em função da expansão acelerada que contribuiu para o não planejamento efetivo do setor logístico, além da queda no desempenho da empresa ante aos clientes. Nesse contexto, buscou-se desenvolver uma pesquisa de natureza aplicada, abordagem qualitativa e objetivo exploratório. O procedimento técnico utilizado foi o estudo de caso. A pesquisa consistiu em (i) definir a estrutura conceitual-teórica; (ii) planejar o caso; (iii) conduzir o teste piloto; (iv) coletar os dados; (v) analisar os dados; e (vi) gerar o relatório. Foram evidenciados múltiplos desafios durante a análise, como por exemplo, atrasos nas entregas das mercadorias aos clientes. Sugere-se a contratação de um operador logístico na fábrica de biscoitos para auxiliar a criação, planejamento, implementação, desenvolvimento e gestão das atividades de transportes, manutenção de estoques e processamento de pedidos. As conclusões provenientes do estudo destacam que a presença e atuação do operador logístico na microempresa poderá gerar benefícios como o aumento na qualidade do nível de serviço ao cliente e obtenção de vantagem competitiva perante a concorrência.

Lojistik performans, firmalarda değer oluşturmanın yanında kurum etkinliğinin artmasına da katkı sağlayarak müşteri memnuniyetinin artmasında ve entegre olarak firma başarısında kritik bir öneme sahip olmuştur. Sipariş zamanlarının azalması, daha güvenilir ve hızlı bir akış ile teslimatların gerçekleşmesinde optimum sayıda tedarikçi sayısı ile tedarik etkinliğinin sürdürülmesi gibi faaliyetler işletmelere önemli kazanımlar sağlamaktadır. Diğer taraftan etkin olmayan bir lojistiğin, firmaların ticari anlamdaki maliyetlerini yükseltmekte olduğu ve küresel bütünleşme potansiyelini düşürmekte olduğu gözlenmektedir. Üretim işletmelerinin bu noktada tedarik etkinliği ve sipariş işleme noktasında entegre bir yaklaşımı takip ederken, lojistik performansı göz ardı etmeyen bir süreç izlemesi önemsenmektedir. Bu çalışmada işletmelerin sipariş işleme fonksiyonunun lojistik performansa etkisinde tedarik etkinliği fonksiyonunun aracılık rolü YEM ile test edilmiştir. Bu doğrultuda hazırlanan anket ölçeği Gaziantep OSB’lerinde faaliyet göstermekte olan 246 imalat işletmesinde uygulanmıştır. Tespit edilen bulgular neticesinde işletmelerdeki sipariş işlemenin lojistik performans üzerinde hem doğrudan etkisi hem de tedarik etkinliği aracılığı ile dolaylı etkisi olduğu tespit edilmiştir., Logistics performance has been of critical importance in increasing customer satisfaction by providing added value to the enterprises as well as increasing the efficiency of the enterprise and integrated business success. Reducing ordering times and faster and more reliable deliveries, such as maintaining optimum supplier and supplier efficiency, make significant gains for businesses. The other reason is that for an ineffective logistics, firms are increasing their commercial costs and reducing their global integration potential. Production companies follow an integrated approach to procurement efficiency and order processing at this point, while maintaining a process that does not disregard logistic performance. In this study, the intermediary role of the supply efficiency function in the effect of the order processing function of the enterprises on the logistic performance was tested with SEM. The questionnaire scale prepared in this direction was implemented in 246 manufacturing companies operating in Gaziantep OIZs. As a result of the findings, it has been determined that order processing in the enterprises has both direct impact and indirect effect on logistics performance.

Background: There has been substantial research addressing physical distribution challenges within emerging markets, such as Asia and South America. However, this topic is not sufficiently covered for emerging markets in Africa. Emerging markets in Africa present unique challenges that should be known to any manager intending on operating in these markets. Objectives: This study aims to provide insights into the prevalent physical distribution challenges that organisations face in emerging markets in Africa. Emphasis is placed on the adaptations organisations make to overcome these challenges. The study focused on multinational organisations operating in the fast-moving consumer goods, third-party logistics and retail industry, mostly from the perspective of the South African division. Method: A generic qualitative research design was employed. Twelve semi-structured interviews were conducted with middle to senior managers who have knowledge of their organisation’s distribution operations in African markets. Results: The findings highlight the existence of prevalent challenges identified in the literature, namely, border congestion, informal markets and weak infrastructure, while providing insight into the sources of these challenges. Furthermore, other challenges, such as warehouse system misalignment, order processing times and variable border legislation are highlighted. Adaptations to these areas are discussed. Conclusion: The study provides further academic understanding of the challenges multinationals are facing and adaptations they are implementing when trading in emerging African markets. Theoretically, the body of knowledge of physical distribution is enhanced by addressing distribution challenges in the African context. Additionally, the study assists organisations to understand how they can possibly adapt their physical distribution operations.

Block chain technology, hailed as the new internet is a revolutionary concept that can ensure transparent transactions through data authenticity and integrity with wide coverage area ranging from business to healthcare to real estate and designed to operate in the framework of an interlinked network or cluster of computing systems. Taking the example of the manufacturing sector, there are several transactions including sales, purchase and other expenditures that are to be accounted and there is a need for several ledgers maintained such as cash books, sales and purchase accounts which can be replaced by a single distributed ledger in public domain in chronological order. Also, in project activities there are capital costs that include infrastructure as well as technology costs, operational expenses and several direct and indirect expenses. Block chain technology can play a vital role in these transactions and can have wide scope in the area of logistics particularly in supply order processing including smart contracting, supply chain management, energy management, assets transfer, part processing, design, patent handling, electronic commerce, digital strategies and in ERP methodologies. While the idea of such a system is proposed to be the most preferred mode of transactions with data security and traceability and decentralization there are several challenges that need to be addressed before its implementation in totality. This paper intends to bring out the requirements analysis through expectations of the manufacturing sector by which it can possibly benefit from the integration of blockchain technology into the system.

The proposed study presents an economic lot size and production rate model for a single vendor and a single buyer setup. This model involves greenhouse gas (GHG) emissions from industrial sources. The carbon emissions in this model are considered as two types: direct emissions and indirect emissions. The production rate affects carbon emissions generation in production, i.e., generally, higher production rates result in more emissions, which is governable in many real-life cases. The production rate also impacts the process reliability and quality. Faster production deteriorates the production system quickly, leading to machine failure and defective items. Such reliability and quality problems increase energy consumptions and supply chain (SC) costs. This paper formulates a vendor-buyer SC model that tackles these issues. It considers two decision-making policies: integrated or centralized as well as decentralized, where the aim is to obtain the optimal values of the decision variables that give the minimum total SC cost. It includes the costs of setup, holding inventory, carbon emissions, order processing, production, reworking, and inspection processes. The decision variables are the production rate, lead time, order quantity, the number of shipments, and the investments for setup cost reduction. In the later sections, this paper compares the numerical outcomes of the two centralized and decentralized policies. It also provides sensitivity analysis and useful insights on the economic and environmental execution of the SC.

One of the most common interventions in health care is medication use. Although simple when contrasted against interventions such as surgery, the high incidence of adverse drug events highlights the complexity and risk inherent in the medication-use process. This process comprises multiple interconnected stages: prescribing, order processing, dispensing, administration, and monitoring. Not only is there considerable variation in how steps are executed across stages, but the interconnected nature of the stages may also mean that errors made in one stage often have a cascading effect on subsequent stages. This chapter offers guidelines for designing medication reconciliation technology and presents a mixed methods empirical study in medication reconciliation.

The competitiveness of a retailer is highly dependent on an efficient distribution system. This is especially true for the supply of stores from distribution centers. Stores ask for high flexibility when it comes to their supply. This means that fast order processing is essential. Order processing affects different subsystems at the distribution center: orders are picked in multiple picking zones, transferred to intermediate storage, and delivered via dedicated tours. These processing steps are highly interdependent. The schedule for picking needs to be synchronized with the routing decisions to ensure availability of orders at the DC’s loading docks when their associated tours are scheduled. Concurrently, intermediate storage represents a bottleneck as capacity for order storage is limited. The simultaneous planning of picking and routing operations with restricted intermediate storage is therefore relevant for retail practice but has not so far been considered within an integrated planning approach. Our work addresses this task and discusses an integrated zone picking and vehicle routing problem with restricted intermediate storage. We present a comprehensive model formulation and introduce a general variable neighborhood search for simultaneous consideration of the given planning stages. We also present two alternative sequential approaches that are motivated by the prevailing planning situation in industry. Numerical experiments and a case study show the need for an integrated planning approach to obtain practicable results. Further, we identify the impact of the main problem characteristics on overall planning and provide valuable insights for the application of these findings in industry.

Military logistics is a complex process where response times, demand uncertainty, wide variety of material references, and cost-effectiveness are decisive for combat capability. The demanding flexibility can only be achieved by improving supply chain management (SCM) to minimize lead times. To cope with these requirements, lean thinking can be extended to military organizations. This research justifies and proposes the use of lean methodologies to improve logistics processes with the case study of a military unit. In particular, the article presents the results obtained using value stream mapping (VSM) and value stream design (VSD) tools to improve the order processing lead time of spare items. The procedure starts with an order generation from a military unit that requests the material and ends before transportation to the final destination. The whole project was structured, considering the define&ndash, measure&ndash, analyze&ndash, improve&ndash, control (DMAIC) problem-solving methodology. The results show that the future state map might increase added-value activities from 44% to 70%. After implementation, it was demonstrated that the methodology applied reduced the lead-time average and deviation up to 69.6% and 61.9%, respectively.

Today there are a large number of different companies involved in non-regular passenger transport. Most of them use special software to reduce the speed of order processing and for better service management. Such programming tools often provide a convenient system for processing orders and a user interface for the contractors. Therefore, such companies have alreadу forced out other competitors from the market of passenger transport. The purpose of the research is to develop and implement an algorithm of driver’s path optimization and to develop a decentralized on-line service, oriented on support of the management of nonregular passenger transport, consisting of a service for car ordering, a service for processing and executing orders and a service for managing internal private organizations. Each service is an application accessible from both a web browser and a mobile device. In the paper the theoretical basis of the management of passenger transport has been analyzed and the basic problems have been defined. The route optimization task, which was reduced to the asymmetrical task of the salesman, was initiated. The Little algorithm for the resolution of this task was used.

“Digital twin” more often perceived as a twin terminology along with industry virtualization of physical assets. The usage of digital twin on physical asset is well known, such as to predict when the individual parts of a machine must be replaced. However, digital twin technology in non-physical modeling is a vibrant research area. One area where digital twin can be effective is predicting the customer's needs. Most businesses to predict the customer's needs uses risk analysis and profitability assessment which holds its own pitfalls. One of the major downfalls arises during the analysis on historical data is the time consumed. Time is one of the crucial factors that determines the profit a company makes, holding of customers, satisfying the customers' needs at the right time, fails because of the static behavior. This can be made more effective by enforcing Digital twin to track the customer behavior dynamically such as the products they consume, their satisfaction. So instead of relying on the historical data, the data for digital twin will be from CRMs, logs, order processing info etc. Right product at right time can be achieved by creating suitable machine learning models on this dynamic dataset and this trained model are held in the digital twin, which runs them in real time. For achieving this approach, a specific technology called Tarantool Data Grid is very useful. In this chapter, we will explore how this technology can be used to create consumer choice modeling using Digital Twin with suitable use cases.

The coordination of materials, personnel and other resources for proper order processing in assembly.

Целью работы является создать сайт под ключ и внедрить его в компанию. Задачи работы: – анализ компании и постановка задачи; – создание сайта с нуля; – оценка экономической эффективности. Работа выполнена на Ð¼Ð°Ñ‚ÐµÑ€Ð¸Ð°Ð»Ð°Ñ Ð¾ деятельности компании ООО «Фави» Методы: сравнительный анализ, экспертное оценивание, прогнозирование, обработка Ð´Ð°Ð½Ð½Ñ‹Ñ . Основные результаты работы: – проведен анализ компании; – выявлена проблема в компании; – выполнено сравнение Ñ‚ÐµÑ Ð½Ð¾Ð»Ð¾Ð³Ð¸Ð¹ для реализации проекта; – создан дизайн сайта; – разработан сайт; – спрогнозированы Ð´Ð¾Ñ Ð¾Ð´Ñ‹ и траты компании; – выполнена оценка экономической целесообразности проекта. Практическая значимость работы определяется разработкой сайта для компании ООО «Фави» для привлечения клиентов из интернета., The purpose of the work is to create a website and implement it in the company. Tasks of work: – company analysis and task setting; – website creation from scratch; – assessment of economic efficiency. Work is performed on the basis of the company Favi Methods: comparative analysis, expert assessment, forecasting, data processing The main results of the work: – analysis of the company; – a problem has been identified in the company; – a comparison of technologies for the implementation of the project; – created site design; – developed site; – predicted income and expenses of the company; – an assessment of the economic feasibility of the project. The practical significance of the work is determined by the development of a site for the company Favi LLC to attract customers from the Internet.

In cooperative warehousing, peaks in demand for order picking of one user can have a significant influence on order processing times of the other users, especially when order picking resources are limited. However, the literature on cooperative warehousing dealing with uneven order picking demand is scarce. This work contributes to closing this research gap by developing a pricing model that assigns a price for order picking and promotes balanced resource demand by users inside a cooperative warehouse. The developed pricing model is subsequently applied using an agent-based simulation model to prove its principal applicability. Results show that the pricing model makes order picking more expensive in times of high overall demand and yields prices that cover the total cost of the warehouse.

To master ongoing market competitiveness, manufacturing companies try to increase process efficiency through process improvements. Mapping the end-to-end order processing is particularly important, as one needs to consider all order-fulfilling core processes to evaluate process performance. However, today's traditional process mapping methods such as workshops are subjective and time-consuming. Therefore, process improvements are based on gut feeling rather than facts, leading to high failure probabilities. This paper presents a process mining approach that provides data-based description of process performance in order processing and thus objectively and effortlessly maps as-is end-to-end processes. The approach is validated with an industrial case study.