Changes In Product Demand Research Articles

ONION CRACKERS SALES FORECASTING USING ARTIFICIAL NEURAL NETWORK METHOD AND HOLT'S DOUBLE EXPONENTIAL SMOOTHING

Changes in product demand is a problem that is often faced by the industry as well as one of them is onion crackers. Tapioca flour is the main ingredient used to make onion crackers. Because the demand for crackers is always changing, this company often experiences excess or shortage of raw materials. If there is an excess of raw materials, the company must incur additional costs for the maintenance and storage of raw materials so that raw materials can be properly stored in accordance with existing standards, which of course costs a lot. Therefore, companies must plan to solve this problem by planning raw material requirements by forecasting raw material requirements using the artificial neural network method and double exponential smoothing holt. The results showed that the artificial network method had a mean square error of 0.120 and the mean square error using the double exponential smoothing method yielded a value of 206.19. Based on these two values, it can be concluded that the artificial neural network method is more accurate than the double exponential smoothing holt method. This can be seen by comparing the roat mean square error values of the two methods..

Математическая модель выкладки товаров на полочном пространстве торговой сети с наличием расширяемых и сужаемых сегментов для определенных видов товаров

The goal of the study is to develop a mathematical model for displaying products on the existing shelf space of a store. Categories of products (local, everyday demand products) that should be located in certain segments of shelves are considered. A characteristic feature of the model is the possibility of expanding and narrowing these segments depending on the number of assortments or seasonal changes in demand for products The goal of the retailer is to maximize profits from the sale of products while complying with restrictions on the placement of products on shelves. The discussion gives the advantages and disadvantages of the given product display model. The study is important for retail chains.

The principle of cascadic prosilience—Assessment of territorial recovery strategies after disrupted regional infrastructure systems in Iraq

This paper presents an assessment approach to fostering socioeconomic re-development and resilience in Iraqi regions emerging from the destruction and instability, in the aftermath of the war conflict in Iraq. Focusing on the intricate interplay of logistics infrastructure and economic recovery, the present study proposes a novel framework that integrates general resilience insights, data analytics, infrastructure systems, and decision support from Data Envelopment Analysis (DEA). We draw inspiration also from historical cases on “creative destruction” or “Blessing in Disguise” (BiD) phenomena, like the post-WWII reconstruction of Rotterdam, so as to develop the notion of stepwise or cascadic prosilience, analyzing how innovative logistics systems may in various stages contribute to economic rejuvenation. Our approach recognizes the multifaceted nature of regional resilience capacity, encompassing both static (conserving resources, rerouting, etc.) and dynamic (accelerating recovery through innovative strategies) dimensions. The logistics aspect spans both the supply side (new infrastructure, ICT facilities) and the demand side (changing transportation flows and product demands), culminating in an integrated perspective for sustainable growth of Iraqi regions. In our study, we explore several forward-looking strategic future options (scenarios) for recovery and reconstruction policy factors in the context of regional development in Iraq, regarding them as crucial strategic elements for effective post-conflict rebuilding and regeneration. Given that such assets and infrastructures typically extend beyond a single city or area, their geographic scope is broader, calling for a multi-region approach. By leveraging the extended DEA approach by an incorporation of a super-efficiency (SE) DEA approach so as to better discriminate among efficient Decision-Making Units (DMUs)—in this case, regions in Iraq—our research aims to present actionable and effective insights for infrastructure investment strategies at regional-governorate scale in Iraq, that optimize efficiency, sustainability and resilience. This approach may ultimately foster prosperous and stable post-conflict regional economies that display—by means of a cascadic change—a new balanced prosilient future.

Planning of Production Capacities of Precast Construction Plants under Conditions of Changes in Demand for Products

The main criterion for a feasibility study of the production efficiency of precast construction plants, analysis of internal production reserves and optimal production volume is the production capacity indicator. Thus, the study of various Factors influencing the production capacity indicators is an important task. The results of research of the influence of the number of storeys and the set of block sections in the configuration of industrial residential buildings on changes in the ratio of product types in the range of the production programme of plants are presented in the paper. Fluctuations in the ratio of product types during the construction of different types of houses have been established. Based on the obtained results, the dependence of the production capacity plants on fluctuations in the ratio of product types has been constructed. It has been determined that the main reason for the revealed facts of a decrease in production capacity is the uneven loading of production lines with fluctuations in the ratio of product types in the range of production programs of precast construction plants. Taking into account the known facts of the dependence of production capacity on the concrete capacity of block sections of houses, the results of a comparison of the degree of influence of concrete capacity on the production capacity and the ratio of the main types of products in the range of production programs of plants are presented in the paper. It has been established that a key influence on the indicators of production capacity is exerted by changes in the ratio of product types. On the basis of the conducted research, optimal reserves of production capacity have been determined with a high share of different types of houses in the production programs of the plants. Taking into account the established indicators of production capacity reduction, their optimal reservation is within 20–25 % of the design capacity of the plants, which should serve as a reference point when planning the functioning of the production management system under the conditions of changes in demand for products.

Perceptions About Neighbouring Countries - Evidence from Bulgaria

Perceptions about various countries reverberate in attitudes about products, migration and investments originating from the respective countries, as well as in changes in demand for products, migration and investments. In our study we analyse the changes eventuated in the recent couple of years, extending several studies from previous periods. We apply the psychosemantics approach and the repertory grid as the main research methodology. The countries in focus are the neighbouring Balkan countries, as well as the two big European ones - Russia and Germany. Using correspondence analysis, we compare results from two samples from 2021 and 2023. Discovered is a major change in perceptions about Russia, accompanied with certain shifts in the composition of the main factors. We interpret these changes as reflecting some substantial changes in the political sentiments of Bulgarian society.

Quality Management in Times of Pandemic Case Study of S.R.A, a Freight Transportation Company

This study analyzes the quality of SRA LDA, a Portuguese company that provides automotive parts and transportation services and operates exclusively at the AutoEuropa factory. Despite the significant impact on the logistics sector, the company has managed to maintain its workforce and vehicle fleet while adjusting schedules and services to meet new social distancing measures and changes in product demand. SRA's ability to maintain its services is a sign of its adaptability to any obstacles that arise. The company is committed to ensuring that its employees feel valued in their respective roles, thereby fostering a positive work environment, which is key to success. Companies like SRA have been and continue to be important in ensuring the continuity of operations for other companies, such as AutoEuropa, which is a major supplier of Volkswagen cars in the European market. In conclusion, the analysis of SRA LDA's quality highlights a company that stands out for providing automotive parts and transportation services with a focus on customer satisfaction and maintaining a responsible and dynamic team.

Оценка мультипликативных эффектов в экономике Дальневосточного федерального округа: межотраслевое моделирование

For the Far Eastern constituent entities of the Russian Federation, using the Kronenberg’s Cross-Hauling Adjusted Regionalization Method (CHARM) and its subsequent hybridization, which consists of taking into account the available actual statistical data on foreign trade of the regions, regionalization of national input-output tables for 2019, 2020 was carried out. Estimates were obtained and a decrease in the values of output multipliers was identified for all Far Eastern constituent entities of the Russian Federation in 2020, generated by changes in the aggregate final demand for products of specialized industries (with the exception of the production of vehicles and equipment in the Khabarovsk Territory). The largest reductions in multipliers were recorded for the production of coke and petroleum products (Khabarovsk Territory) and fishing and fish farming (Khabarovsk and Primorsky Territories, Sakhalin Region) and were determined by a decrease in consumer demand for fuel and shifts in the structure of intermediate consumption in the fishing industry. Comparable estimates of the dynamics of multiplier effects generated by changes in the volume of final demand for products of specialized industries in the Far Eastern regions were obtained. It is shown that the maximum increase in output in 2019 and 2020 was provided by the food industry of the Primorsky Territory

Mini Oreo Cup Production Plan with Aggregate Method to Minimize Production Costs

Manufacturing companya PT.Sinar Jatimulia Gemilang produces many types of plastic packaging, one of which is mini oreo cups.The production system in this company is based on job shop, changing product demand and limited product capacity make it difficult for the company to make a productin plan. The purpose of this research is to make a mini cup production plan to meet future demand with the aim of minimize production costs. At the initial stage a field study was conducted to obtain to required data. The data obtained through interviews with production employees from the company. Then do the literature study through books and articles from related journal as the theoretical basis used in this research. Production planning research using exponential smoothing forecasting method with α=0.6 and standard error of 617814.3 was selected with the criteria of the smallest mean square error and aggregate planning.The result of analysis I strategy of inventory level inventory cost is IDR. 441,654,900. In analysis II the strategy for variation in the number workers costs is IDR. 747,373,500. The cost for analysis III subcontracting strategy is IDR. 79,065,843,000. From the research that production cost before the calculation is IDR. 52,002,000,000 and the number of workers is 4. After the result of the research on optimization the number of workers that will be used by 2.

Multiple-objective optimization of a reconfigurable assembly system via equipment selection and sequence planning

With the development of society and technology, the modern manufacturing system is required to handle unpredictable and rapidly changing market demands during various demand periods. This is a new challenge to the production scheduling of the production line, which requires a faster and smarter response to changes in production demand. To meet the challenge, the reconfigurable manufacturing system (RMS) has emerged as a competitive and promising manufacturing model. The reconfigurable assembly system (RAS), a critical feature of RMS, can be seamlessly incorporated into multi-variety, variable-batch, multi-functional, and fast-delivery manufacturing modes, opening up the possibility of rapid changes for production scheduling. In this paper, we first build a RAS model that can more accurately represent scheduling problems that may occur in real-world industrial operations. More specifically, we consider the assembly process of products could be divided into a number of major steps which are performed in different stations and optimize the assembly sequence of products and the equipment selection within individual assembly stations. After that, three objective functions corresponding to the workload balance, reconfiguration/assembly cost, and lead time are proposed to jointly optimize the assembly sequence and the equipment selection in individual assembly stations. Finally, a hybrid particle swarm optimization (MOHPSO) method is utilized to solve the multiple-objective optimization problem, which combines the advantages of particle swarm optimization and the hybrid method. Experimental results show the superiority of the proposed MOHPSO method over other alternatives including MOPSO and NSGA-II, achieving lower Spacing Metric (SM) and Mean Ideal Distance (MID) indexes and is suitable for solving production scheduling problems.

The role of industry 4.0 technologies in overcoming pandemic challenges for the manufacturing sector.

Industry 4.0 aims to revolutionize the manufacturing sector to achieve sustainable and efficient production. The novel coronavirus pandemic has brought many challenges in different industries globally. Shortage in supply of raw material, changes in product demand, and factories closures due to general lockdown are all examples of such challenges. The adaption of Industry 4.0 technologies can address these challenges and prevent their recurrence in case of another pandemic outbreak in future. A prominent advantage of Industry 4.0 technologies is their capability of building resilient and flexible systems that are responsive to exceptional circumstances such as unpredictable market demand, supply chain interruptions, and manpower shortage which can be crucial at times of pandemics. This work focuses on discussing how different Industry 4.0 technologies such as Cyber Physical Systems, Additive Manufacturing, and Internet of Things can help the manufacturing sector overcome pandemics challenges. The role of Industry 4.0 technologies in raw material provenance identification and counterfeit prevention, collaboration and business continuity, agility and decentralization of manufacturing, crisis simulation, elimination of single point of failure risk, and other factors is discussed. Moreover, a self-assessment readiness model has been developed to help manufacturing firms determine their readiness level for implementing different Industry 4.0 technologies.

Multi-period additive/subtractive product platform design and inventory management

Forming product platforms is an effective strategy to offer products variety economically. The short products life span and new variants pose a challenge to designing platforms, which satisfy the changing customer demands. There is a need to design platforms that can adapt to changes. In addition, it is important to account for the partially completed platforms inventory held in each production period which could be utilised in subsequent production periods. An important contribution of this work is the use of combined additive and subtractive manufacturing in customising the product platform by adding and/or removing features to suit the changing product features and demands for different product variants. In this paper, a holistic non-linear model is presented for designing optimal multi-period additive/subtractive product platforms and managing their inventory. The model provides the optimal product platform design for each production period, macro process plans for customisation, the number of each platform stored as inventory, and the product variant platform assignment. The initial model is subsequently linearised to reduce computation time. A gear shaft family of products is used as a demonstration example. The model redesigns the product platform as needed to meet the changing demand of each product variant while minimising the costs.

Mathematical model of software development project team composition optimization with fuzzy initial data

Increasingly frequent changes in demand for products, reducing product life cycles, changes in the business environment during a crisis, the innovative nature of projects, the complexity of forecasting external and internal conditions, the impact of the human factor lead to increased uncertainty and inability to plan team activities with a given degree of accuracy. In this regard, the subject matter of the article is the task of creating an adaptive project team that can work effectively in the mentioned above conditions. This task is especially relevant for the sphere of software development. The sphere is dynamic and characterized by frequent changes in product requirements, technologies, working conditions, and restrictions on project implementation. Agile approaches are used to manage such projects, which can help the team respond to uncertainties and frequent changes. To date, there are many agile approaches to project management, but the issue of selecting team members in such approaches is insufficiently covered. Therefore, this work formalizes the task of deciding on the selection of software development team members, considering the uncertainty and subjectivity of the information that affects the selection of candidates for the team. The task of the work is to create a decision-making model based on the use of the mathematical apparatus of fuzzy sets and methods of operations research. Such a model should allow considering the uncertainty of estimates of project requirements and the level of competence of team candidates. The result is a mathematical model of a two-criterion constrained optimization problem. The first objective function is aimed at finding a team composition that maximizes the maximum competencies of its members. The second criterion is aimed at forming a team with the maximum sum of competencies for all indicators, considering the weight of each indicator. The first constraint assumes that at least one team member meets the competency requirements expressed by a specific indicator. Additionally, it is required that the available time fund of the team members allows the project to be completed on time. It considers the limitation on the salary of the team. Conclusions. Solving the problem in accordance with the proposed mathematical model will allow making a team as readily as possible to meet the existing and new requirements for the project staff. The last circumstance is especially important when implementing a software development projects.

Responding to rapidly changing product demand through a coordinated additive manufacturing production system: a COVID-19 case study

COVID-19’s lockdown policies saw Modern Manufacturing Practice (MMP) – batch/mass/just-in-time supply chains – severed and societal demands rapidly change from products such as vehicles and clothing to Personal Protection Equipment (PPE), ventilators and equipment for remote working. The critical and, in many cases, life preserving, need for responsive manufacturing resulted in government and frontline services turning to society’s Additive Manufacturing (AM) capability in homes, schools, universities, and industry to provide essential products and product replacements. While AM managed to respond and support some government and frontline services, the highly distributed and diverse nature of the nation’s AM resources resulted in potentially avoidable production inefficiencies and delays. This paper develops and evaluates a series of strategies for coordinating AM for rapidly changing product demand to further enhance the responsiveness and productivity of AM. The strategies presented in the paper employ a host-client agent-based architecture that enable local governance of production thereby enabling distributed AM resource to come together to tackle society’s production needs without the need for centralised coordination. To enable and support local governance, it is necessary to understand how the combination of production logics impact the overall performance of the production system. Correspondingly, the contribution of this paper lies in the characterisation and quantification of the impacts of production logic through the metrics of Time in System, Lateness and Jobs in Queue and the consequences for responsive AM production systems.

Robust facility layout design for flexible manufacturing: a doe-based heuristic

Flexible manufacturing systems (FMS) should be able to respond to changing manufacturing requirements and environments. From the layout point of view, FMS need to be rearranged to fit the new requirements. However, rearranging the layout is often undesirable due to its unpredicted high costs and production disruption. This paper proposes a practical approach to mitigate the effects and repercussions of changing environments and avoid rearranging the layout. A robust layout approach is presented, where changes in product demand and mix are absorbed by altering product routes and not rearranging the layout. In this approach, the problem is decomposed into two sub-problems: sub-problem 1 (SP1) where a robust layout is constructed, and sub-problem 2 (SP2) to obtain the best routes of products. To solve SP1, design of experiments is used to find a critical period, which is the period most affected under demand changes. Then, the layout for the critical period is determined using a hybridized genetic-tabu search algorithm. Then SP2 is solved by a branch and cut algorithm to obtain the optimal routes of the products in each period. The performance of the proposed methodology is illustrated using a case study and is benchmarked against rival ones from the literature.

Scalability analysis of selected structures of a reconfigurable manufacturing system taking into account a reduction in machine tools reliability

Scalability is a key feature of reconfigurable manufacturing systems (RMS). It enables fast and cost-effective adaptation of their structure to sudden changes in product demand. In principle, it allows to adjust a system's production capacity to match the existing orders. However, scalability can also act as a "safety buffer" to ensure a required minimum level of productivity, even when there is a decline in the reliability of the machines that are part of the machine tool subsystem of a manufacturing system. In this article, we analysed selected functional structures of an RMS under design to see whether they could be expanded should the reliability of machine tools decrease making it impossible to achieve a defined level of productivity. We also investigated the impact of the expansion of the system on its reliability. To identify bottlenecks in the manufacturing process, we ran computer simulations in which the course of the manufacturing process was modelled and simulated for 2-, 3-, 4- and 5-stage RMS structures using Tecnomatix Plant Simulation software.

An Improved Pigeon-inspired Optimization Algorithm for Solving Dynamic Facility Layout Problem with Uncertain Demand

In the layout planning, changes in product demand and uncertainty caused by demand prediction need to be considered simultaneously to cope with the market changes. To this end, a dynamic facility layout problem (DFLP) is studied to optimize cost and area utilization considering the uncertain product demands. An improved multi-objective pigeon-inspired optimization algorithm (IMOPIO) is proposed. A global collaboration mechanism is structured to balance the global and local search. The validity of the proposed approach is demonstrated by an industrial case. The results suggest that the search ability of IMOPIO is better than compared algorithms in solving the proposed problem.

Workforce Adjustment to Absorb Changes in Production Rate in Continuous Production Systems.(Dept.M)

This paper presents some mathematical models that deal with the planning of the workforce requirement on continuous production lines to react with changes in production rates in order to meet sudden changes in product's demand. Norms for operation times and workers production rates are normally set by management based on the design capacity of the production line. The models presented in this paper can assist management in evaluating the workforce needed to react to the changes in production required over the normal production volume in order to satisfy any sudden changes in product demand, especially for short term planning. Cost aspects and analysis are also considered to include the profitability impact of workforce scheduling according to any adopted new production plan. It is expected that these models will provide management with powerful tools to alleviate the effect of any disturbances that may occur due to sudden changes in production plans, and thereby maximizing profitability.

Dual-Channel Global Closed-Loop Supply Chain Network Optimization Based on Random Demand and Recovery Rate.

In the process of globalization, customer demand is usually difficult to predict, and product recycling is generally difficult to achieve accurately. It is also urgent to deal with increased inventory while avoiding shortages, with the purpose of reducing supply chain risks. This study analyzes the integrated supply chain decision-making problem in the random product demand and return environment. It proposes a multi-objective optimization model, which is an effective tool to solve the design and planning problems of the global closed-loop supply chain. It consists of a multi-period, single-product and multi-objective mixed integer linear programming model, which can solve some strategic decision problems, including the network structure, entity capacities, flow of products and components, and collection levels, as well as the inventory levels. From the perspective of economic, environmental and social benefits, three objective functions are defined, including maximizing the net present value (NPV) of the system, minimizing the total emissions of supply chain activities, and maximizing social sustainability indicators. Finally, a numerical example is provided to verify the advantages of this model, and sensitivity analysis results are provided. The results show that changes in product demand and return rate will have a great impact on economic and social performance.

Ecosystem service function of green manure and its application in dryland agriculture of China

Dryland agriculture, with wide distribution and high yield potential, plays an important role in ensuring food security in China. It is currently limited by water scarcity, soil depletion, water and soil loss, and low non-renewable resource-use efficiency. Green manure has the potential to improve growth environment of crops and promote sustainable high-yield crops by increasing soil quality, balancing soil nutrients, and enhancing soil water-storage capacity. In addition, green manure has ecological benefits, including enhancing agroecosystem biodiversity, increasing soil surface cover degree, reducing ineffective nutrient loss to environment, improving air balance of farmland systems, and biological control of diseases, insect pests, and weeds. Under current scenario of intensified global climate change, environmental deterioration, and agricultural product demand changes, the traditional agronomic techniques of using green manure as a fertilizer cannot satisfy the requirements of agricultural development. Thus, it is necessary to strengthen the selection and bree-ding of green manure genetic resources for dryland agriculture, to develop a new regionalization of green manure, and to establish a cropping pattern based on green manure suitable for different regions. Furthermore, it is important to study and optimize the tillage and cultivation techniques to satisfy modern production and to establish an evaluation system for the comprehensive benefits of green manure. It is needed to establish a green manure application pattern that enables resource and ecological protection for improving ecological environment and economic efficiency of dryland agriculture and provides theoretical basis and technical support for exploiting green manure benefits.

An alternative approach to measuring demand changes in meat markets

Commodity groups, academics, government agencies, and marketing analysts often have strong interests in understanding changes in demand for products. It is often the case, however, that only equilibrium price and quantity data are available for identifying changes in demand. But, such equilibria are the result of both changes in demand and changes in supply – the latter of which causes changes in quantity demanded. Although an existing index-based method is widely used to identify demand shifts, we consider its theoretical foundation and empirical performance against a proposed alternative. We find that when using widely available but highly aggregated annual-level price and quantity data, our alternative better characterizes demand shifts for goods such as beef, pork, poultry, and lamb. For many agribusinesses that require information about market dynamics in their industry, our method is likely to provide a more accurate, low-cost assessment of demand changes over time.