Demand-driven Production Research Articles

Digital Twin-based Demand-Driven Production Planning and Scheduling System: A New Conceptual Framework

In the ever-evolving manufacturing landscape, companies are increasingly confronted with both external and internal uncertainties. These challenges necessitate a responsive and agile production planning and scheduling system. While adopting digital twin (DT) technology has enhanced dynamic scheduling at the operational level, there remains a notable gap in the literature regarding tactical-level production planning. This crucial aspect involves determining what and how much to produce and directly addressing customer demands while efficiently managing inventory. This paper introduces a novel conceptual framework that synergistically integrates Demand-Driven Material Requirements Planning (DDMRP) with DT-based scheduling and optimization. Our framework is developed to execute tactical production planning as well as operational-level scheduling seamlessly. It employs a genetic algorithm to refine the scheduling process, aiming to significantly improve production performance. Moreover, the framework is adept at managing rescheduling, effectively addressing both external and internal disturbances. By bridging the gap between high-level planning and operational execution, this framework presents a holistic approach to modern manufacturing challenges, ensuring effective inventory management, demand fulfillment, and enhanced production performance.

Analysis of a demand-driven production inventory model in a trapezoidal neutrosophic number-ruled decision environment

An Economic Production Quantity (EPQ) model is built with fundamental intuitions that time influences demand pattern and demand can control production in manufacturing process. Also, existence of deterioration of produced items during warehousing is considered. Preservation technology is installed for lowering the deterioration rate as much as possible. The manufacturing-warehousing process includes ambiguities in several pockets of decision-making phenomena. Trapezoidal neutrosophic number describes the imprecise environment for decision phenomena in this paper. Numerical results reveal that the cost reduction goal is impacted negatively for large size of production potential and for much reliance of the production process on demand pattern. On contrary, incorporations of preservation measure and neutrosophic decision phenomena favor the cost minimization objective.

Control design and multimode power management of WECS connected to HVDC transmission line through a Vienna rectifier

This paper deals with nonlinear control and optimal management of wind energy conversion systems connected to HVDC transmission lines. Specifically, the energy system under study consists of a wind turbine driving a permanent magnet synchronous generator, the latter supplies energy to a power grid through a Vienna rectifier and a DC link. To compensate for intermittent nature of wind power and load demand variations, we design a voltage controller as well as a novel energy management strategy that accounts for the available wind power and DC load demands. In the considered management, distinction is made between two operation modes: i) the maximum power point tracking mode using an intelligent adaptive optimizer, where only electrical parameter measurements are used to ensure maximum power extraction; ii) the adaptive power point tracking mode employing demand-driven production. In both modes, the power factor requirement is ensured, regardless the weather conditions and load demand variations, enhancing thus the aerogenerator’s efficiency. One more novelty of the proposed control and management system is the online estimation of the DC voltage, avoiding thus the use of a physical sensor. The performances of the control and management system are highlighted by several simulations, showing the achievement all control objectives.

Performance improvement through nonlinear control design and power management of a grid-connected wind-battery hybrid energy storage system

This paper presents a study on the application of nonlinear control and optimal power management techniques in a grid-connected wind energy conversion system with battery storage. The objective is to efficiently supply power to a load while considering performance constraints. The energy system under investigation comprises a wind turbine, battery, AC-grid, and load. Power is delivered to the load through various converters and a DC-link. However, the intermittent nature of wind energy and frequent load fluctuations can negatively impact the battery's lifetime and charging performance. To address these challenges, a nonlinear controller and a novel energy management system are designed. The energy management system generates multiple energy flow scenarios to achieve equilibrium in energy distribution between the load and energy sources. This equilibrium aims to minimize overall system costs, ensure DC-grid stability, and enhance power quality. Two distinct operation modes are identified: i) MPPT mode, which utilizes an optimizer to maximize power extraction, and ii) adaptive power point tracking (APPT) mode, which considers demand-driven production and the state of charge (SOC) of the storage system. The convergence of the closed-loop control is formally analyzed, and simulation results confirm the improved performance of the proposed multi-criteria design for energy management.

An efficient production planning approach based demand driven MRP under resource constraints

Production plans based on Material Requirement Planning (MRP) frequently fall short in reflecting actual customer demand and coping with demand fluctuations, mainly due to the rising complexity of the production environment and the challenge of making precise predictions. At the same time, MRP is deficient in effective adjustment strategies and has inadequate operability in plan optimization. To address material management challenges in a volatile supply-demand environment, this paper creates a make-to-stock (MTS) material production planning model that is based on customer demand and the demand-driven production planning and control framework. The objective of the model is to optimize material planning output under resource constraints (capacity and storage space constraints) to meet the fluctuating demand of customers. To solve constrained optimization problems, the demand-driven material requirements planning (DDMRP) management concept is integrated with the grey wolf optimization (GWO) algorithm and proposed the DDMRP-GWO algorithm. The proposed DDMRP-GWO algorithm is used to optimize the inventory levels, shortage rates, and production line capacity utilization simultaneously. To validate the effectiveness of the proposed approach, two sets of customer demand data with different levels of volatility are used in experiments. The results demonstrate that the DDMRP-GWO algorithm can optimize the production capacity allocation of different types of parts under the resource constraints, enhance the material supply level, reduce the shortage rate, and maintain a stable production process.

Customer Order Scheduling in a Mobile 3D Printing Factory

The problem addressed in this article is motivated by the development of small-scale, mobile factories equipped with 3D printing technologies. As they are embedded in standard containers, it is possible to move them from one location to another to support on-site and demand-driven production. To approach this problem, we developed a mixed-integer program model which defines the location of the mobile factory and assigns customer orders to each location and build, with the objective of minimizing relocation, transport, and tardiness costs. In addition, we proposed a heuristic method which, in an iterative process, builds a feasible solution and then seeks to improve it based on certain predefined rules. Early computational tests show that our algorithm achieves competitive results and can lay the groundwork for more sophisticated algorithms and problems.

Lean and Legacy supply chains for coordinated demand driven production to handle disruptions

The pandemic created a massive disruption and exposed the lack of resilience in supply chains (SCs), bringing resilience to the spotlight. SC practices have become lean to achieve maximum profitability, with very little or no buffer stocks. On the other hand, with disruptions being the new norm in an ever-increasing uncertain and volatile business environment, organizations’ sustainability is being severely tested. A comparison of Legacy and Lean SC practices is carried out in this paper. Legacy practices are costly, inefficient and forgotten but valuable in the context. Lean practices are highly profitable and efficient, but they are insufficient for a firm's survival in a disruption. This paper through the basics of control engineering applications and proposes a methodology to achieve an appropriate dynamic mix of Lean and Legacy SC practices through coordinated demand-driven production. The work presented in this paper will enable SC managers and practitioners struggling to find solutions to deal with disruptions and help strengthen SCs, albeit at the cost of sacrificing some profitability and efficiency.

Digital assistance for mobile 3D-printing of medical supplies

In order to enable fast medical response in times of pandemics or other humanitarian crises, decentralized manufacturing networks based on additive manufacturing technologies are suitable for the demand-driven production of protective equipment and medical supplies. The use of mobile manufacturing units is intended to increase the resilience of isolated and overwhelmed hospitals in developing countries. In this paper, a digital assistance system for enabling untrained staff to handle the mobile manufacturing unit as well as operate and maintain 3D printers on site will be discussed.

Integrating rough set theory with customer satisfaction to construct a novel approach for mining product design rules

When users choose a product, they consider the emotional experience triggered by the product form. In view of the fact that traditional kansei engineering can not effectively reflect the complex and changeable psychological factors of users, and it has not explored the complex relationship between customer satisfaction and perceptual demand characteristics. To address this problem, some uncertainty techniques including rough sets and fuzzy sets are applied to capture more accurate emotion knowledge. Therefore, this research proposes an integrated evaluation gird method (EGM), rough set theory (RST), continuous fuzzy kano model (CFKM), fuzzy weighted association rule mining method to extract the significant relationship between user needs and product morphological features. The EGM is applied to analyze the attractive factor of morphological characteristics of the product, and then the demand items with the highest satisfaction are analyzed through CFKM. The semantic difference method is combined to construct a decision table, and through attribute reduction and importance calculation to obtain the weight of the core product design items. In order to explore the non-linear relationship between design elements and kansei images, the fuzzy weighted association rule mining method was applied to obtain the set of frequent fuzzy weighted association rules based on evidence theory’s reliability indices of minimum support and confidence so as to realize user demand-driven product design. Taking the design of electric bicycle as an example, the experiment results show that the proposed method can help companies or designers develop products to generate good solutions for customer need.

A method for product form design of integrating interactive genetic algorithm with the interval hesitation time and user satisfaction

Due to the continuous release of new products, manufacturers are paying attention to customer-oriented design of products that meet user needs to minimize the risk of their products being rejected by the market. Due to the ambiguity of user cognition, it is difficult to accurately obtain the user's preference for individual productions. To respond to the challenge, we propose an engineering scientific research method of interactive genetic algorithm with the interval arithmetic based on hesitation and fuzzy kano model(FKM) to explore the emotional needs of users for product forms and drive product modeling evolution design. Through expert interviews, the morphological characteristics and perceptual images factors of the products attracting users are investigated. In order to identify the user's satisfaction relationship with the perceptual images, we use FKM to analyze the product image style that meets the user's kansei needs accurately and selects 5 factors which is attractive attributes. Meanwhile, we attempt to transform this 5 factors into evaluation carrier to guide the evolution direction of product styling in HIIF-IGA, and then optimized four electric bikes with scores over 8.8 so that it could realize user demand-driven product evolution design. To handle users' ambiguity, the FAHP method is used to quantify the user's emotional imagery criterion and create a product evolution design system platform, which can automatically generate product styling design scheme in line with user preferences. This experimental results show that the proposed method can help enterprises effectively improve customer satisfaction and reduce the cost and time of product development.

Industrial wearable system: the human-centric empowering technology in Industry 4.0

The Industry 4.0 program and corresponding international initiatives continue to transform the industrial workforce and their work. The service-oriented, customer-centric and demand-driven production is pushing forward the progress of industrial automation. Even though, it does not mean that human can be fully replaced by machines/robots. There is an increasing awareness that human presence is not only one type of manufacturing capability, but also contributes to the overall system’s fault tolerant. How to achieve the seamless integration between human and machines/robots and harness human’s full potential is a critical issue for the success of Industry 4.0. In this research, a human-centric empowering technology: industrial wearable system is proposed. The aim of this system is to establish a human–cyber–physical symbiosis to support real time, trusting, and dynamic interaction among operators, machines and production systems. In order to design a substantial framework, three world-leading R&D groups in this field are investigated. Five design considerations have been identified from real-life pilot projects. The future trends and research opportunities also show great promise of industrial wearable system in the next generation of manufacturing.

Impacts of Demand-Driven Energy Production Concept on the Heat Utilization Efficiency at Biogas Plants: Heat Waste and Flexible Heat Production

Conventionally, biogas plants—small and large—produce more heat than what they use for their internal operations. Typically, the amount of heat produced and the proportion wasted was directly proportional to the plant sizes and their hydraulic loading rates. Meanwhile, prevailing climate in a plant location can also be a determining factor of the amount of heat wasted in the biogas production process. This study utilizes data from 10 agricultural biogas plants located in the Midwest of Germany, which generate different amounts of biogas from the digestion of variety of substrates, to achieve two principal objectives. First, the trends of waste heat production and the extent to which critical factors such as climate and total produced thermal energy affect them are analyzed. Secondly, the impact of demand-driven energy production concept on waste heat production is examined. Results indicate that there is a significant interactive effect between amount of produced heat and climatic temperature (p < 0.01) on the amount of wasted heat from biogas plants. The study also finds that demand-driven production can reduce heat waste by up to 20%. If the thermal energy for households were supplied by district heating based on flexible heat production biogas plants, it could save up to 9.8 TWh heat energy and 4,719,315 t CO2 each year in Germany, which would help to reach targets determined by EEWarmeG and Kraft-Warme Kopplung Gesetzt.

Rethinking Macroeconomic Policy within a Simple Dynamic Model

We propose a simple macrodynamic model to rethink some standard policy prescriptions. Our model includes exogenous growth, endogenous investment, demand-driven production and employment, a dynamic Phillips curve, and fiscal and monetary policy instruments. The model has multiple steady states with different stability properties, and it is analytically tractable to a significant extent. Given the nonlinear nature of the model, we complete the analytical results with simulations. The model generates different growth patterns, endogenous fluctuations, and demand-driven level effects even in the long-run. It also reveals saddle-path type instabilities in which seemingly stable paths suddenly give way to increasing volatility. These results lead us to reflect on fiscal and monetary policy standards, and suggest new interpretations for the “Great Recession”.

Approaching a dynamic view on the availability of mineral resources: What we may learn from the case of phosphorus?

This paper elaborates in what way a dynamic perspective on reserves, resources and geopotential is necessary to provide robust estimates on resource availability. We introduce concepts of essentiality, criticality and economic scarcity and discuss for the case of phosphorus (P) how they are defined and may be measured. The case of P is considered in detail as P an essential element for global food security with a highly dissipative use and is geographically unevenly distributed across the globe. We distinguish and relate the complementarity between physical and economic scarcity and discuss limits and potential of static indicators such as static lifetime, Hubbert curve applications, and the Herfindahl–Hirschman-Index of P for predicting future availability of these resources. We reveal that these static indicators are – in general – not valid approaches to predict physical scarcity of resources. Geological data show that though the P reserves have not been systematically and completely assessed on a global scale, the static lifetime of P is high. When acknowledging socio-economic and technological dynamics, and available geological facts, statements predicting physical scarcity or a peak in P production within a few decades are unlikely to be accurate or valid. We elaborate that some simplified indicators such as static lifetime or the Hubbert curve based prediction of peaks may serve as screening indicators preceding early warning research, which may induce increased mining activities, technology innovation or other actions. However, in general, these simplified indicators are not valid approaches to predict physical scarcity of resources. Although one day there may be a supply-driven P production peak, demand-driven production plateaus and multiple peaks are probable in the near future. Given its geopotential, essentiality, and the learning curve of efficient fertilizer use, P is subject to demand-driven market dynamics. Thus, a symmetric decline and unavoidable shortage of P in the next decades are unlikely. This insight does not refute the need to close the anthropogenic P loop. Activities associated with P production and consumption use has a significant pollution potential in part because of the dissipative nature. The paper reveals the necessity to mitigate risks (such as economic scarcity, especially for poor farmers) of both short-term price peaks and longer lasting step-changes in price, e.g. due to knowledge gaps of technological adaptation in energy and water management or other reasons of insufficient supply-demand dynamics management. The complexity of this task necessitates a transdisciplinary approach.

Diagnosing constraints to market participation of small ruminant producers in northern Ghana: An innovation systems analysis

This paper assesses why participation in markets for small ruminants is relatively low in northern Ghana by analysing the technical and institutional constraints to innovation in smallholder small ruminant production and marketing in Lawra and Nadowli Districts. The results show that the limitations experienced by smallholders, i.e., water shortages during the dry season, high mortality and theft of livestock, persist because of institutional constraints. These include structural limitations related to availability of arable lands, weak support systems for animal production and health services delivery, community values that are skewed towards crop production more than animal husbandry, ineffective traditional and formal structures for justice delivery, and gaps in the interaction between communities and district and national level organizations such as the Ministry of Food and Agriculture, district assemblies, rural banks, and non-governmental organizations as well as traders and butchers. Confronted with such constraints, the strategies that most smallholders have adopted to be resilient entail diversified sources of livelihood, low input use in small ruminant production, and maintaining the herd as a capital stock and insurance. Only a few smallholders (i.e., ‘positive deviants’) engage in market or demand-driven production or exhibit successful strategies in small ruminant husbandry. It is argued in this paper that for the majority of smallholders, market production, which requires high levels of external inputs or intensification of resource use, is not a viable option. The main implications of the study are (1) that other institutional constraints than market access constraints should be addressed, (2) that commercial livestock production should not be idealized as the best or only option (as is being done in many contemporary interventions that aim at incorporating smallholders into commodity value chains), and (3) that different types of small ruminant system innovation pathways should be explored by making use of local positive deviants.

A production optimization model of supply-driven chain with quality uncertainty

Supply-driven chain’s production is different from traditional demand-driven production because its supplies must guide the full production flow toward the markets and respond actively to customer demand. According to the control theory, a novel multi-variable operation model of supply-driven chain is discussed here, integrating suppliers, manufacturers, distributors and market demands. Especially the coordination problem between suppliers and manufacturers is discussed where suppliers play more important role than manufacturers. Because defect is common in real production system, the production operation of supply-driven chain with imperfect quality is described on the basis of fuzzy set to model the ambiguity of quality and to provide appropriate supply coordination mechanism. In a designed numerical example, it is apparent that both response and robustness performances of supply-driven production system on demand with imperfect quality are improved by a fuzzy proportional-integral-differential regulator. The proposed model may apply to similar productions with imperfect quality.

Technology product evolution and the diffusion of new product features

The evolution of technology products can be analysed on multiple levels. Product categories go through continuous evolution determined by the cumulative changes in the features of new product models. This is manifested in the diffusion of new product features and in the increasingly vague boundaries between different product generations. This article develops an approach for planning and forecasting technology product evolution and the diffusion of new product features. This is achieved by isolating the phenomena underlying the evolution process, and formulating the process at the product category, product feature, and product model levels. The approach is derived from these formulations combining the primarily demand-driven product category diffusion and product unit replacement behaviour, and the more supply-driven product feature dissemination. The approach enables meaningful sensitivity analysis including the analysis of discontinuities. The developed approach is applied to characterise the evolution of an example product category of mobile handsets and to forecast the diffusion of mobile handset features using extensive longitudinal and cross-sectional data collected from Finland. In consequence, the process of technology product evolution and the phenomenon of product feature dissemination are suggested as extensions to research on product category diffusion and replacement.

Probabilistic assessment of loss in revenue generation in demand-driven production

In Demand-driven Production with Just-in-Time inputs, there are several sources of uncertainty which impact on the manufacturer's ability to meet the required customer's demand within the given time frame. This can result in a loss of revenue and customers, which will have undesirable impacts on the financial aspects and on the viability of the manufacturer. Hence, a key concern for manufacturers in just-in-time production is to determine whether they can meet a specific level of demand within a given time frame, to meet the customers' orders and also to achieve the required revenue target for that period of time. In this paper, we propose a methodology by which a manufacturer can ascertain the probability of not meeting the required demand within a given period by considering the uncertainties in the availability of production units and raw materials, and the loss of financial revenue that it would experience as a result.

Lean planning in the semi-process industry, a case study

The lean approach is an idealizing improvement approach that has an enormous impact in the field of operations management. It started in the automotive industry and has since been widely applied in discrete manufacturing. However, extensions to the (semi-) process industry have been much slower. Resource characteristics of the (semi-) process industry obstruct a straightforward application. The notion of the point of discretization for the (semi-) process industry is helpful here. This notion builds on the fact that in most (semi-) process industries there is a point in production where process production turns into discrete production. Downstream of this point lean principles are applicable in a straightforward manner, while upstream lean needs to be interpreted in a more liberal way. In this article we address this issue by a case study. The study considers how the principles of ‘flow’ and ‘pull’ production – suggesting a regular, demand-driven product flow – may be implemented for the (semi-) process industry by introducing cyclic schedules. The conjectures guiding the case study are: (i) Cyclic schedules fit in a lean improvement approach for the semi-process industry, (ii) Cyclic schedules help to improve production quality and supply-chain coordination and (iii) Discrete event simulation is a useful tool in facilitating a participative design of a cyclic schedule. The case study is extensively described to be able to judge how the context of the changes and the intervention process contribute to the results of the intervention.

Agent-based simulation and analysis of demand-driven production strategies in the timber industry

This paper addresses the generic problem of production planning in a divergent timber production environment. It aims at analysing the performance of various demand-driven production strategies of a timber production system. This analysis is performed using a simulation platform built on an agent-based advanced planning system. Nine production strategies configurations are evaluated under six scenarios in order to carry out a complete mixed level design of 54 simulation runs. Each of these configurations is a combination of a decoupling point position and a level of capacity that is committed to contracts with customers. Accordingly, the six scenarios are designed as a combination of supply type (i.e. log diameter distribution) and timber market prices. Production processes and co-production yields are based on a real manufacturing system from eastern Canada. Performance is evaluated from the logistic and economic points of view. Results demonstrate that demand-driven planning approaches that propagate demand information upstream the supply chain have the potential to improve planned customer service and reduce planned inventories. Results also show that timber companies need to receive a premium from their customers in order to compensate from the loss of potential value resulting from a more constrained planning environment.