Customer Demand Management Research Articles

Dynamic customer demand management: A reinforcement learning model based on real-time pricing and incentives

The demand response model proposed in this work offers a game-changing solution to the challenges posed by the unpredictability of renewable energy sources. By combining both pricing and incentives, this model significantly improves the accuracy of demand response strategies, leading to more effective modulation of customer demand. The real-time and time-of-use pricing options presented to customers incentivize them to actively increase or decrease their energy consumption, thereby contributing to the stability of the energy grid. This work also sheds light on the crucial role that characteristic parameters such as the internal or external coincidence factor play in the classification of customers using the k-means algorithm. The reinforcement learning method used in the model not only optimizes prices and incentives, but also ensures that both customers and energy distribution companies benefit equally. A sensitivity analysis of customer elasticity highlights the dynamic interplay between clustering and reinforcement learning algorithms and customer behavior, demonstrating the power and effectiveness of this model. With its innovative approach and cutting-edge techniques, this work sets a new model for demand response and makes a compelling case for the inclusion of prices and incentives in future models.

Residential Load Forecasting Using Modified Federated Learning Algorithm

The feasibility of utilizing electricity consumption data is increasing with the widespread use of advanced metering infrastructure among consumers. In addition, the need for consumer demand management and services is likely to increase further as distributed resources are activated. Load forecasting technology is essential for customer demand management. Forecasting technology using deep learning has been applied extensively in recent years. Deep-learning-based load forecasting requires data for training a forecasting model, and the available data may be limited by the Personal Information Protection Act. Federated learning has emerged as a solution to this problem. However, in federated learning, the global model in the central server is trained by aggregating the local model sent from the client without filtration. This may result in the global model overfitting or failure to converge by aggregating the model that is not trained effectively in the client. In this study, we introduce a modified federated learning algorithm to solve this problem and propose a method to forecast residential loads. The proposed method is analyzed and evaluated experimentally using smart meter data. The experimental results reveal that the proposed method improves forecasting performance and convergence. For the global model, when the number of clients was two, three, four, and five, the forecasting performances were 4.891, 5.228, 5.488, and 5.633, respectively, on a mean absolute percentage error (MAPE) basis, showing performance improvements of 28.4%, 10.0%, 16.3%, and 5.8%.

Enablers of post-COVID-19 customer demand resilience: evidence for fast-fashion MSMEs

PurposeThis study aims to analyse the resilience of customer demand management post-coronavirus disease 2019, using fast fashion as an example. The paper provides insights for potential applications to micro-, small and medium enterprises (MSMEs).Design/methodology/approachBased on the qualitative analysis and an integrated Plan-Do-Check-Act (PDCA)-decision making trial and evaluation laboratory (DEMATEL)-fuzzy technique for order of preference by similarity to the ideal solution (TOPSIS) methodology of fuzzy multi-criteria decision-making, we explored and prioritised the enablers of resilience management for fast-fashion MSMEs.FindingsThe results reveal that the highest priority enabler is maintaining customer loyalty. Other enablers are associated with e-commerce endorsement, a customer-focussed assortment of items and flexible store operations.Research limitations/implicationsThe study findings will enable fast-fashion MSMEs to develop effective actions and priorities in operations efforts to promote post-pandemic recovery.Originality/valueDespite the importance of the resilience project and the changing fast-fashion customer patterns, only a handful of studies have explored how resilience can be managed in this field. Thus, the findings can contribute to closing this gap in the context of operations resilience research as well as MSME operations.

A Robust Strategic Theory based Load Balancing and Resource Allocation in Cloud Environment

An on demand elastic service is cloud computing. At particular time, based on clients requirements, software, information, shared resources and other devices are provided in this. For supporting cost effective usage of computing resources, it is designed using distributed computing and virtualization advances and it enhances resource scalability. Based on requirement, business outcomes can scale up or down its resources. On-demand resource allocation challenges are created by customer demand management. To minimize energy, Dynamic Particle Swarm Optimization (DPSO) model is developed in available research works, where CPU utilization are regulated while operating at maximum frequency. Geographically distributed resources like computers, storage etc. are owned by self interested organizations or agents are available in large-scale computing systems of cloud computing. In their own benefit, this resource allocation algorithm can be manipulated by these agents and severe degradation in performance are produced due to its selfish behaviour and its efficiency is also very poor. To solve this kind of problem, a strategy is designed for developing a resource allocation protocols with load balancer in first phase of this research work. In this agents are forced to follow the rules and tell truth. In heterogeneous distributed systems, to solve load balancing problem, a truthful strategy is designed using this strategic theory. Optimal allocation algorithm based on Improved Elephant Herd Optimization (IEHO) is proposed where a truthful payment scheme is admitted by output function which satisfies voluntary participation. Good performance is exhibited by proposed approach as indicated in experimentation results.

Time Driven Activity-Based Costing : Konsep Akuntansi Manajemen yang Akurat dalam Menghadapi Lingkungan yang Dinamis dan Bisnis Global

This paper describes how the concept of Management Accounting, particularly the time-driven activity-based costing (TD-ABC) can be applied in calculating the cost ) is realistic. Cost becomes a very important element in calculating the earnings and net earnings were reported. The errors in the calculation of cost (cost) would result in a pricing mistake. TD-ABC is a new concept of management accounting. The emergence of TD-ABC is expected to eliminate the vulnerabilities that exist on the concept of the previous concepts such as ABC. TD-ABC concept is usually applied to companies with sophisticated accounting information systems were applicable to the Company as at the Farm Bryon ranch., Corp and Small and Medium Enterprises. Therefore it can be said TD-ABC can be applied in a dynamic environment and globalisasi and shifting corporate objective function in order to satisfy customer demand management accounting to do more

PERANCANGAN SISTEM INFORMASI DAN APLIKASI PEMESANAN MAKANAN DI KAFE BERBASIS ANDROID

The advancement of technology specially in mobile sector gives a lot of advantages and facilities in time-saving and labor-saving. Its scope has already extended to various aspects such as in education, entertainment, up to business sector. Cafe business is increasing in number in Indonesia. Various menu of food becomes the attraction of each cafe compared to food which is served at home. The large number of people visiting cafe demands cafe owners to improve the service so that visitors feel comfortable. One way to do it is by creating a computerized system. In this case, the service given to customers visiting cafes is still done manually, namely during food-ordering process, waitress note down orders using stationary media. Due to many problems which are caused by manual food-ordering, a solution to overcome the problem is needed. Therefore, an aplication which can help waiters in food-ordering process is required so that it can facilitate customer demand management and time and labor efficiency. Since everything will be organized well, at the end the purpose of the cafe can be achieved. This aplication will facilitate users to input customers’ orders. This food-ordering aplication is designed in the form of mobile-based aplication which is addressed to mobile android operation system. Keywords: information system, application, café, smartphone, Android.

The Bullwhip Effect

Supply chain management is a fledgling science which concerned with synchronization of both material flow and information flow by integrating companies for a common objective to meet the requirements of the end customer. Bullwhip effect is an important research topic of the supply chain management. The Bullwhip effect is precarious to both short and long run competitive advantage, the dependability sustainability advantage of the chain. This paper proposes to show the impact of the Bullwhip effect on the supply chain using experimentally simulated data from Beer distribution game. The game represents a simple supply chain which consists of factory, distributor, wholesaler and retailer. The paper used empirical models of ANOVA, spectral density estimation, ARMAX and Cochrane- Orcutt autoregression. The result of the study prevails that when we quantify the impact of the Bullwhip effect to different actors of the supply chain with respect of inventory holding cost and stock-out case it is found that different cost implications. When quantifying the impact of the Bullwhip effect to the whole supply chain it is found that at least one of the competitive advantages of the chain is lost. When generalizing it the all the actors in the supply chain will suffer from the Bullwhip effect. The overall evidence from statistical causality analysis suggest that without proper both intra-organizational and inter-organizational coordination of the companies across the supply chain it is difficult to have effective and efficient customer relationship management, customer demand management and inventory management.

Optimal Sizing of BESS for Customer Demand Management

AbstractThe paper proposes an optimal sizing method of a customer's Battery Energy Storage System(BESS) which aims at managing the electricity demand of the customer to minimize electricity cost under the time of use(TOU) pricing. Peak load limit of the customer and charging and discharging schedules of the BESS are optimized on annual basis to minimize annual electricity cost, which consists of peak load related basic cost and actual usage cost(energy cost). The optimal scheduling is used to assess the maximum cost savings for all sets of candidate capacities of BESS. An optimal size of BESS is determined from the cost reduction rate(hereinafter reduction rate) curves via capacity of BESS. Case study uses real data from industry customer and shows how the proposed method can be employed to optimally design the size of BESS for customer demand management.

수용가 수요관리용 전지전력저장시스템의 최적용량 산정방법

The paper proposes an optimal sizing method of a customer"s battery energy storage system (BESS) which aims at managing the electricity demand of the customer to minimize electricity cost under the time of use(TOU) pricing. Peak load limit of the customer and charging and discharging schedules of the BESS are optimized on annual basis to minimize annual electricity cost, which consists of peak load related basic cost and actual usage cost. The optimal scheduling is used to assess the maximum cost savings for all sets of candidate capacities of BESS. An optimal size of BESS is determined from the cost saving curves via capacity of BESS. Case study uses real data from an apartment-type factory customer and shows how the proposed method can be employed to optimally design the size of BESS for customer demand management.

Using utility information to calibrate customer demand management behavior models

In times of stress customers can help a utility by means of voluntary demand management programs if they are offered the right incentives. The incentives offered can be optimized if the utility can estimate the outage or substitution costs of its customers. This paper illustrates how existing utility data can be used to predict customer demand management behavior. More specifically, it shows how estimated customer cost functions can be calibrated to help in designing efficient demand management contracts.

Using Utility Information to Calibrate Customer Demand Management Behavior Models

In times of stress customers can help a utility by means of voluntary demand management programs if they are offered the right incentives. The incentives offered can be optimized if the utility can estimate the outage or substitution costs of its customers. This paper illustrates how existing utility data can be used to predict customer demand management behavior. More specifically, it shows how estimated customer cost functions can be calibrated to help in designing efficient demand management contracts.