Changes In Product Demand Research Articles

A column generation approach to multiscale capacity planning for power-intensive process networks

Due to the high volatility in electricity prices, power-intensive industrial plants often have to frequently shift load in order to remain cost-competitive. Capacity planning is required for assessing the value of additional operational flexibility and planning for expected changes in product demand. Here, the main challenge lies in the simultaneous consideration of long-term capacity planning and short-term operational decisions. In this work, we extend the multiscale model proposed by Mitra et al. (Comput Chem Eng 65:89–101, 2014a) to a formulation that applies a general process network representation and incorporates inventory handling across seasons. We propose a column generation approach to solve large instances of the resulting mixed-integer linear program (MILP). The algorithm decomposes the original problem into multiple MILP subproblems, while the restricted master problem is an integer program. Computational experiments demonstrate the effectiveness of the column generation algorithm, which clearly outperforms the full-space model, especially with increasing number of years in the planning horizon. Also, the results show that the master problem tends to yield integer solutions within the required optimality gap due to its strong linear programming relaxation, such that no further branching is required. Moreover, the proposed approach is applied to perform capacity planning for a real-world industrial air separation plant.

Quality Study and Improvement of Logistic Processes on the Example of a Chosen Enterprise

Abstract This article presents the issues of quality research and improvement of logistics processes in the area of distribution of one of selected industrial enterprises. The case described concerns the producer of raw chemical materials, who acts as a supplier for industrial customers. The studied company, due to dynamic changes in product demand, very often outsources the distribution of raw materials to external transport companies. This solution results in limited supervision over commissioned processes, which may lead to increased probability of customer dissatisfaction. The article presents the results of the study of the quality of logistic processes implemented or commissioned in the examined enterprise. The assessment was based on the results of the customer satisfaction survey and the results of the analysis of received complaints regarding deliveries. Analysis of the causes of complaints allowed to identify the actions necessary to improve the quality of logistics processes. This article contains, among others, the criteria for the assessment of transport service providers and a set of recommendations to reduce the risks associated with logistics processes of deliveries.

Latent-Function-Based Residual Discrete Grey Model for Short-Term Demand Forecasting

ABSTRACTWhen developing a production plan, accurate forecasting short-term demand is challenging for managers because a short forecast period indicates that the change in product demand exhibits an unsteady trend. Therefore, forecast models generated using a large amount of historical observations do not fully explain the data collected on developing patterns and, consequently, do not robustly forecast outcomes. However, if a low number of samples featuring the most recent information is used for developing a forecast, management efficiency could be enhanced and enterprises could gain a competitive advantage. To solve the problems associated with forecasting short-term demand when small datasets are available, we developed a residual discrete grey model that is based on modeling residual analysis. Specifically, we first applied the discrete grey model to create a forecasting model, and then used the obtained fitting residuals to generate training samples using the Latent Information function to learn the topology of a backpropagation neural network. Finally, the predictive errors obtained using the constructed network for adjusting the forecast to enhance the forecasting performance. We conducted an experiment using the demand data obtained from a thin film transistor liquid crystal display panel, and the results indicated that a highly accurate forecast could be obtained using the proposed modeling procedure. This finding suggests that the model developed in this study is a tool that enables short-term demand to be forecast accurately using small datasets.

Long-Term Electricity Procurement for Large Industrial Consumers under Uncertainty

Due to the need for adjusting plant operations to time-varying electricity prices and changing product demand, managing the procurement of electricity in power-intensive businesses has become a major challenge. Large industrial electricity consumers often enter into long-term contracts with favorable rates. However, such power contracts require the consumers to commit themselves to the amount that they are going to purchase months in advance when future demand is not yet known with certainty. In this work, we simultaneously optimize long-term electricity procurement and production planning while considering uncertainty in product demand. We propose a multiscale multistage stochastic programming model in which a 1-year planning horizon is divided into seasons, with each season represented by two characteristic weeks; also, each season corresponds to a stage at which the demand for that season is revealed. The progressive hedging algorithm is applied to solve industrially relevant large-scale instances of t...

A petri-net model for scalable systems with variable stages based on changing product demands

Swift and sustainable change in production throughput is considered an invaluable attribute for manufacturing systems. Effective models for these scalable systems allows manufacturing enterprises to develop systems that provide upgradability as well as capability to maintain its current throughput. In order to achieve this, the system must be designed to support future expansions in the production capacity as and when needed by the market. Our aim is to provide a Petri-net model that minimizes ramp up requirements and simplifies the system transition after the change in throughput has been proposed. The proposed model is applied to an industrial case study to show the capability and advantage of replacing traditional approach with a more graphical one. The model can act as an aid for designers to plan and cater for the requirements of the emerging manufacturing market.

Towards a sophisticated approach to cost oriented automation and intelligent metrology in the advanced manufacturing

Abstract The manufacturing industry faces currently and in the near future due to the information society, very important challenges. New data, network, automation, digital customer interfaces blow up the existing value chain. The production of nanoscale workpieces requires integration of a high level of quality in conformance with manufacturing processes and micro-/nanometrological assessment and evaluation. Nanotechnology is multidisciplinary in nature. Modern production metrology and its industrial application started on the basis of the scientific, technical and organisational work of E. Abbe, William Taylor and Frederick Winslow Taylor and at the end of the twentieth century – the “Quality Control Century” – the development has reached Nanotechnology and is proceeding to Pico- and Femto-technology. To meet high-level demands both from industrial and from private customers in the future, manufacturing enterprises must be flexible and agile enough to respond quickly to product demand changes also according technological developments especially in the field of precision engineering at micro, nano and pico scale production. With support of AI and modern IT, it is possible to realise modern cost-effective customer-driven design and manufacturing.

Drilling reconfigurable machine tool selection and process parameters optimization as a function of product demand

Special purpose machines (SPMs) are customized machine tools that perform specific machining operations in a variety of production contexts, including drilling-related operations. This research investigates the effect of optimal process parameters and SPM configuration on the machine tool selection problem versus product demand changes. A review of previous studies suggests that the application of optimization in the feasibility analysis stage of machine tool selection has received less attention by researchers. In this study, a simulated model using genetic algorithm is proposed to find the optimal process parameters and machine tool configuration. During the decision-making phase of machine tool selection, unit profit is targeted as high as possible and is given by the value of the following variables: SPM configuration selection, machining unit assignment to each operation group, and feed and cutting speed of all operations. The newly developed model generates any random chromosome characterized by feasible values for process parameters. Having shown how the problem is formulated, the research presents a case study which exemplifies the operation of the proposed model. The results show that the optimization results can provide critical information for making logical, accurate, and reliable decisions when selecting SPMs.

Advanced Metrology and Intelligent Quality Automation for Industry 4.0-Based Precision Manufacturing Systems

Intelligence is an essential feature of future development and production systems, and intelligent production is a major component of future business. To meet market demands in present and future global industrial world, manufacturing enterprises of any kind and any size must be flexible and agile enough to respond quickly to product demand changes also according technological developments especially in the field of precision engineering at micro/nanoand pico scale production. With support of AI and modern IT it is possible to realise modern cost-effective customer-driven design and manufacturing taking into account the importance and basic role of modern Integrated Management Systems - IMS and intelligent advanced metrology.This new concept can be developed on the basis of intelligent production technologies and integrated systems as well as extensive use of the IT, AI, simulation, quality autonomation, robotics, advanced metrology and advanced engineering data exchange techniques. Moreover by utilizing advanced information analytics, networked intelligent machines and instruments will be able to perform more efficiently, collaboratively and sustainably, that makes possible an agile and optimal industrial production in any kind of industry and especially in up-to-date SMEs towards Industry 4.0.

A Matheuristic Approach for Solving the Dynamic Facility Layout Problem

The Dynamic Facility Layout Problem (DFLP) is designing a facility over a multi-period planning horizon where the interdepartmental material flows change from one period to the next one due to changes in product demands. The DFLP is used while designing manufacturing and logistics facilities over multiple planning periods; however, it is a very challenging nonlinear optimization problem. In this paper, a zone-based block layout is used to design manufacturing and logistics facilities over multiple planning periods. A zone-based block layout inherently includes possible aisle structures, which can easily be adapted to different material handling systems. The unequal area DFLP is modeled and solved using a zone-based structure where the dimensions of the departments are decision variables, and the departments are assigned to flexible zones with a pre-structured positioning. A matheuristic approach, which combines concepts from Tabu Search (TS) and mathematical programming, is proposed to solve the zone-based DFLP on the continuous plane with unequal area departments. The TS determines the relative locations of departments and their assignments to zones while their exact locations and shapes are calculated by the mathematical programming. Numerical results for a set of test problems from the literature showed that our proposed matheuristic approach is promising.

Life cycle assessment and optimization of an iron making system with a combined cycle power plant: a case study from China

In the steel industry, the iron making system deals with large quantities of materials and energy and so it can play a critical role in reducing emissions and production costs. More specifically, excess by-product gases should be used for electricity generation; otherwise, they lead to pollution. A life cycle analysis is performed to compare the environmental impact of an iron making system with a combined cycle power plant (CCPP), to a system producing the same amount of electricity in a coal power plant. The results for a Chinese steel plant show a 33% reduction in the energy conservation and emission reduction potential for the CCPP system, which is thus more environmentally friendly. A mathematical programming formulation is then proposed for optimal scheduling. It incorporates key technological constraints and is sensitive to hourly changing electricity prices. The outcome is a 19% increase in revenue from electricity sales compared to a schedule that does not dynamically adjust to the price profile. The results also show that emissions from by-product gases can be avoided completely. The paper ends with a sensitivity analysis to evaluate the impact of changes in product demand, gas storage and CCPP capacity, and emission cost.

Hydrograph variances over different timescales in hydropower production networks

AbstractThe operation of water reservoirs involves a spectrum of timescales based on the distribution of stream flow travel times between reservoirs, as well as the technical, environmental, and social constraints imposed on the operation. In this research, a hydrodynamically based description of the flow between hydropower stations was implemented to study the relative importance of wave diffusion on the spectrum of hydrograph variance in a regulated watershed. Using spectral decomposition of the effluence hydrograph of a watershed, an exact expression of the variance in the outflow response was derived, as a function of the trends of hydraulic and geomorphologic dispersion and management of production and reservoirs. We show that the power spectra of involved time‐series follow nearly fractal patterns, which facilitates examination of the relative importance of wave diffusion and possible changes in production demand on the outflow spectrum. The exact spectral solution can also identify statistical bounds of future demand patterns due to limitations in storage capacity. The impact of the hydraulic description of the stream flow on the reservoir discharge was examined for a given power demand in River Dalälven, Sweden, as function of a stream flow Peclet number. The regulation of hydropower production on the River Dalälven generally increased the short‐term variance in the effluence hydrograph, whereas wave diffusion decreased the short‐term variance over periods of <1 week, depending on the Peclet number (Pe) of the stream reach. This implies that flow variance becomes more erratic (closer to white noise) as a result of current production objectives.

Supply Chain's Market Entering Strategy with Unbalanced Power

In this Internet age, global economic integration process increases fierce market competition. In addition to dealing with issues such as geography, culture, technology, knowledge, and close relations of cooperation between enterprises, the enterprises also expanded the scope and space of the competition among enterprises. Market becomes more crowded. At the same time, customer demand over the past simple quality, cost-effective, and after-sales service is quite different; companies need to provide timely quality assurance while meeting the special needs of customers personalized products and services. Therefore, businesses have to cost reduction even more responsive to changes in product demand, distribution and more rapid, more comprehensive service. View the extensive literature on the basis on the powers of the supply chain not including retailers accounted for the dominant and supplier account issues such as the dominant narrative. The introduction of the binary value of the structure, functions can be summarized in the market acceptance of products in use function and enjoyment features. Use features can be known as the basic value, determined by the substitution effect of satisfaction; enjoyment from beyond the performance of an enjoyment features, can be described as beyond the value of the product to meet consumer satisfaction of the fashion and goods function. Market access that is, supply chain ranging from power problems, functions and enjoy the functionality requirements, constraints, where quality and service, and is proportional to the level of product quality and service, the same external coefficient between products, in order to determine the market of product differentiation positioning, companies need to consider the results of price competition, the use of backward induction, first obtain the equilibrium solution of the price competition in the Stackelberg equilibrium, and then get the enterprise product differentiation and positioning strategy.

Novel Trends in the Development of Autonomation and Robotics in Metrology for Production in the Future

Abstract: Today's manufacturing industry is going through a period of transition to the novel production technology down to micro/nanoscale of complex shapes and increasing efficiency and functionality. To meet high-level demands both from industrial and from private customers in the future, manufacturing enterprises must be high flexible and agile enough to respond quickly to product demand changes also according technological developments especially in the field of precision engineering at micro, nano and pico scale production. New levels of manufacturing precision are the key requirements to enable advanced machining processes that demands improved techniques of metrology with high flexibility, universality and high accuracy. New models for alternative configurations of future industrial organisations in general which are usually applied and especially for small and medium sized enterprises (SMEs) need to be investigated. Those new models can be developed on the basis of intelligent production technologies and integrated management systems as well as extensive use of the IT technologies, automation, robotics, parallel-processing computing and advanced engineering data exchange techniques.

A reactive optimization strategy for the simultaneous planning, scheduling and control of short-period continuous reactors

The efficient and economic operation of processing systems ideally requires a simultaneous planning, scheduling and control framework. Even when the optimal simultaneous solution of this problem can result in large scale optimization problems, such a solution can represent economic advantages making feasible its computation using optimization decomposition and/or few operating scenarios. After reducing the complexity of the optimal simultaneous deterministic solution, it becomes feasible to take into account the effect of model and process uncertainties on the quality of the solution. In this work we consider those changes in product demands that take place once the process is already under continuous operation. Therefore, a reactive strategy is proposed to meet the new product demands. Based on an optimization formulation for handling the simultaneous planning, scheduling, and control problem of continuous reactors, we propose a heuristic strategy for dealing with unexpected events that may appear during operation of a plant. Such a strategy consists of the rescheduling of the products that remain to be manufactured after the given disturbance hits the process. Such reactive strategy for dealing with planning, scheduling and control problems under unforeseen events is tested using two continuous chemical reaction systems.

Innovative flexibility-oriented business models and system configuration approaches: An industrial application

In the current competitive scenario, industrial companies experience frequent unexpected changes in production demand. To cope with this, they often opt for investments in manufacturing technologies which embed extra-flexibility that is rarely utilized and, consequently, affects financial and operational performance. Innovative flexibility-oriented business models based on innovative service value propositions could increase manufacturers competitiveness in turbulent environment and could represent a competitive factor for systems suppliers. Their industrial implementation requires the optimization of manufacturing flexibility over the lifecycle of a system and the quantification of economic performance for customers and suppliers in order to mage risks and to shape sustainable contractual agreements. In this paper, stochastic configuration methods to design focused-flexibility production systems are presented as a promising methodological enabler of new flexibility-oriented business models. An industrial case demonstrating the potential value of these methods is discussed. Future research developments are finally outlined.

Price Dispersion and Loss Leader Pricing: Evidence from the Online Book Industry

In this paper, we develop a theoretical model to analyze the pricing strategies of competing retailers with asymmetric cross-selling capabilities when product demand changes. Our results suggest that retailers with better opportunities for cross-selling have higher incentives to adopt loss leader pricing on high demand products than retailers with low cross-selling capabilities. As a result, price dispersion of a product across retailers rises when its demand increases. The predictions of our model are consistent with the empirical evidence from the online book retailing industry. Using product breadth as a proxy for cross-selling capability, we find that retailers with high cross-selling capabilities reduce prices on bestsellers more aggressively than retailers with low cross-selling capabilities. As a result, price dispersion increases when a book makes to the bestseller list, and the increase is mainly driven by the difference in pricing behavior between retailers with different cross-selling capabilities. Our empirical results are robust against a number of alternative explanations.

Developing assembly line layout for delayed product differentiation using phylogenetic networks

Effective formation of product platforms helps adapt to product demand changes and decrease time-to-market and lead time. The product platform groups the core elements of product family members into a common module used to derive different product variants by combining it with different components. A new delayed product differentiation (DPD) platform network model, which applies median-joining phylogenetic networks (MJPN), is proposed. It is used for forming product platforms and determining the assembly line layout of modular product families. The MJPN is traditionally used for DNA sequences’ mapping, analysis, clustering and tracing evolutionary trends. The concept of assembly/disassembly modular platforms, whereby both assembly and disassembly of components are used to derive the final product variants from the platform, is utilised. The proposed model determines the required number and composition of a product platform and defines the DPD points. The developed dynamic assembly/disassembly platforms enhance routing and product mix flexibility due to having different platforms that can be used to produce the same product variant. A family of household kettles is used to demonstrate the application of the proposed model. A metric is presented for determining the effectiveness of a given platform in delaying the product differentiation, hence increasing the efficiency of mass customisation. The proposed metric, applied to the case study, demonstrated that the proposed platform formation model using MJPN is more capable of postponing the product differentiation point.

MANAGING BOTTLENECKS IN MANUAL AUTOMOBILE ASSEMBLY SYSTEMS USING DISCRETE EVENT SIMULATION

Batch model lines are quite handy when the demand for each product is moderate. However, they are characterised by high work-in-progress inventories, lost production time when changing over models, and reduced flexibility when it comes to altering production rates as product demand changes. On the other hand, mixed model lines can offer reduced work-in-progress inventory and increased flexibility. The object of this paper is to illustrate that a manual automobile assembling system can be optimised through managing bottlenecks by ensuring high workstation utilisation, reducing queue lengths before stations and reducing station downtime. A case study from the automobile industry is used for data collection. A model is developed through the use of simulation software. The model is then verified and validated before a detailed bottleneck analysis is conducted. An operational strategy is then proposed for optimal bottleneck management. Although the paper focuses on improving automobile assembly systems in batch mode, the methodology can also be applied in single model manual and automated production lines.

Optimal design of multi-echelon supply chain networks under normally distributed demand

This paper addresses the optimal design of a multiproduct, multi-echelon supply network under uncertainty of demand. The network consists of multiproduct production sites, warehouses and distribution centers and decisions about the selection of facilities and their capacity are taken. Furthermore, information about the flows of products transferred and the safety stock at each distribution center is derived. The lead time of an order to a customer is computed, using the probabilities of overstocking and understocking. All these decisions are incorporated into a single period mixed integer non-linear programming problem (MINLP) which minimizes cost. Linearization techniques for selected highly non-linear terms of the models are explored in order to reduce the computational effort for the solution of the model. Finally, a sensitivity analysis is performed by changing product demand parameters and assessing their effect on the supply chain structure.

Management Insights

Management Insights