Demand Planning Research Articles

Extracting insights from big social data for smarter tourism destination management

PurposeThis paper aims to highlight how big social data (BSD) and analytics exploitation may help destination management organisations (DMOs) to understand tourist behaviours and destination experiences and images. Gathering data from two different sources, Flickr and Twitter, textual and visual contents are used to perform different analytics tasks to generate insights on tourist behaviour and the affective aspects of the destination image.Design/methodology/approachThis work adopts a method based on a multimodal approach on BSD and analytics, considering multiple BSD sources, different analytics techniques on heterogeneous data types, to obtain complementary results on the Salento region (Italy) case study.FindingsResults show that the generated insights allow DMOs to acquire new knowledge about discovery of unknown clusters of points of interest, identify trends and seasonal patterns of tourist demand, monitor topic and sentiment and identify attractive places. DMOs can exploit insights to address its needs in terms of decision support for the management and development of the destination, the enhancement of destination attractiveness, the shaping of new marketing and communication strategies and the planning of tourist demand within the destination.Originality/valueThe originality of this work is in the use of BSD and analytics techniques for giving DMOs specific insights on a destination in a deep and wide fashion. Collected data are used with a multimodal analytic approach to build tourist characteristics, images, attitudes and preferred destination attributes, which represent for DMOs a unique mean for problem-solving, decision-making, innovation and prediction.

Suggestion of Japanese nursing staff supply and demand plan policy

Suggestion of Japanese nursing staff supply and demand plan policy

Erik Lindahl’s Pricing Problem in a Two-Period Model

This paper presents a new interpretation of Lindahl’s pricing problem. The methodology introduces a non-monetary economy, where consumer goods prices are determined endogenously, to show how the ex ante/ex post compatibility between supply and demand plans leads to situations of equilibrium and disequilibrium. Using a roundabout method of production where labour is the only input, I develop a model in two stages. In the first stage, labour, wages, and the interest rate are determined endogenously to establish an ex ante equilibrium. In the second stage, consumer goods prices are calculated to distinguish the ex post equilibrium/disequilibrium. This model aims to constitute a simplified reference to the pricing problem in the Stockholm School approach.

A Comprehensive Approach to the Design of a Renewable Energy Microgrid for Rural Ethiopia: The Technical and Social Perspectives

In view of Ethiopia’s significant renewable energy (RE) potential and the dynamic interactions among the components of the Water–Energy–Food (WEF) Nexus, we attempted to incorporate solar and small-scale hydropower into the optimal design of an environmentally friendly microgrid with the primary goal of ensuring the sustainability of irrigation water pumping, while taking advantage of existing infrastructure in various small administrative units (kebele). Any additional generated energy would be made available to the community for other needs, such as lighting and cooking, to support health and food security and improve the general quality of life. The novelty of the study stems from the utilization of in situ social data, retrieved during fieldwork interviews conducted in the kebele of interest, to ascertain the actual needs and habits of the local people. Based on these combined efforts, we were able to formulate a realistic energy demand plan for climatic conditions typical of Sub-Saharan Africa agricultural communities and analyze four different scenarios of the microgrid’s potential functionality and capital cost, given different tolerance levels of scheduled outages. We demonstrated that the RE-based microgrid would be socially and environmentally beneficial and its capital cost sensitive to the incorporation of individual or communal machines and appliances. Ultimately, the social impact investigation revealed the design would be welcomed by the local community, whose members already implement tailor-made solutions to support their agricultural activities. Finally, we argue that extended educational programs and unambiguous policies should be in place before any implementation to ensure the venture’s sustainability and functionality.

Insight into Practical Teaching in Rural Planning in Colleges Based on the \u201cRural Innovation Workshop\u201d

Based on demand and problem-oriented rural planning, this study proposes new requirements for rural planning education in colleges and universities.This study reflects on the problems and challenges of rural planning teaching and practice in colleges in China from the perspectives of experiential teaching and participatory planning theory. It takes an actual workshop case as its object of analysis to examine the effectiveness of a workshop-based experiential rural planning mode of teaching in response to the needs of rural construction.In this study, used Kolb’s analytical framework to classify the tasks and skills required in activities into four different learning processes: accommodation, divergence, assimilation and convergence. The feedback of students on the Rural Innovation Workshop mentions the improvement of all aspects of ability and indicates that individual comprehensive ability is improved in the process of practical participation in the workshop.

Examining the diagnostic pathway for lung cancer patients in Wales using discrete event simulation.

BackgroundUK’s National Health Service (NHS) has one of the poorest lung cancer survival rates in Europe. To improve patient outcomes, a single cancer pathway was introduced in the NHS. In this study, a Discrete Event Simulation was developed to understand bottlenecks during lung cancer treatment.MethodsThis study focused on the lung cancer diagnostic pathways at two Welsh hospitals. Discrete Event Simulation is a computer-based method that has been effectively used in demand and capacity planning. In this study, simulation models were developed for the current and proposed single cancer pathways. The validated models were used to provide Key Performance Indicators. Several “what-if” scenarios were considered for the current and proposed pathways.ResultsUnder the current diagnostic pathway, the mean time to treatment for a surgery patient was 68 days at the Royal Glamorgan Hospital and 79 days at Prince Charles Hospital. For chemotherapy patients, the mean time to treatment was 52 days at the Royal Glamorgan Hospital and 57 days at Prince Charles Hospital. For radiotherapy patients, the mean time to treatment was 44 days at Royal Glamorgan Hospital and 54 days at Prince Charles Hospital. Ensuring that the patient attends their first outpatient appointment within 7 days and streamlining the diagnostic tests would have the potential to remove approximately 20 days from the current lung cancer pathway resulting in a 20–25% increase of patients receiving treatment within 62 days. Ensuring that patients begin their treatment within 21 days of diagnosis sees almost all patients comply with the 62-day target.ConclusionsDiscrete Event Simulation coupled with a detailed statistical analysis provides a useful decision support tool which can be used to examine the current and proposed lung cancer pathways in terms of time spent on the pathway.

The Diagnostic-Measurement Method-Resting Energy Expenditure Assessment of Polish Children Practicing Football.

Establishing the amount of energy needed to cover the energy demand of children doing sport training and thus ensuring they achieve an even energy balance requires the resting energy expenditure (REE) to be estimated. One of the methods that measures REE is the indirect calorimetry method, which may be influenced by many factors, including body composition, gender, age, height or blood pressure. The aim of the study was to assess the correlation between the resting energy expenditure of children regularly playing football and selected factors that influence the REE in this group. The study was conducted among 219 children aged 9 to 17 using a calorimeter, a device used to assess body composition by the electrical bioimpedance method by means of segment analyzer and a blood pressure monitor. The results of REE obtained by indirect calorimetry were compared with the results calculated using the ready-to-use formula, the Harris Benedict formula. The results showed a significant correlation of girls’ resting energy expenditure with muscle mass and body height, while boys’ resting energy expenditure was correlated with muscle mass and body water content. The value of the REE was significantly higher (p ≤ 0.001) than the value of the basal metabolic rate calculated by means of Harris Benedict formula. The obtained results can be a worthwhile suggestion for specialists dealing with energy demand planning in children, especially among those who are physically active to achieve optimal sporting successes ensuring proper functioning of their body.

The Effect of Social Preferences on Sales and Operations Planning

Sales and operations planning processes are used to align production quantities and customer demand. Two key activities of these processes are demand planning and production planning, which are often assigned to individuals in different departments. In “The Effect of Social Preferences on Sales and Operations Planning,” Papier and Thonemann analyze the role of social preferences (altruism, inequality aversion, and competitive pressure) and monetary incentives in motivating demand planners to invest effort in forecasting that benefits the production planners. Their results indicate that social preferences can be used to incentivize demand planners to invest effort and that this effect is anticipated by production planners. The resulting more accurate demand forecasts and adapted production quantities result in higher company profit. They also provide an optimization model for optimally allocating investments to financial incentives and social preference building.

Demand characteristics of component parts of Korean nuclear power plants depending on reactor type at the operation stage

The demand characteristics of nuclear power plant (NPP) component parts were analyzed to provide basic data for stable operation of an NPP. KHNP’s material supply and demand plan from 2017 to 2020 was used to obtain the demand characteristics of NPP component parts for various reactor types in Korea. The reactor types include OPR1000, APR1400, CANDU, Westinghouse-design PWR, and Framatome-design PWR. After classifying NPP component parts with Q-class and A-class into 344 items, the annual average order quantity per reactor was obtained, and 10 items with large numbers of order counts were selected. They were gaskets, O-rings, bearings, valves, control cards, relays, seals, rings, packing, and switches in order of orders. It was found that most of the demand for the 10 component parts occurred for the planned maintenance period. It was also seen that component parts required for maintenance varied according to each reactor type.

A Model for Demand Planning in Supply Chains with Congestion Effects

This paper is concerned with demand planning for internal supply chains consisting of workstations, production facilities, warehouses, and transportation links. We address the issue of how to help a supplier firmly accept orders and subsequently plan to fulfill demand. We first formulate a linear aggregate planning model for demand management that incorporates elements of order promising, recipe run constraints, and capacity limitations. Using several scenarios, we discuss the use of the model in demand planning and capacity planning to help a supplier firmly respond to requests for quotations. We extend the model to incorporate congestion effects at assembly and blending nodes using clearing functions; the resulting model is nonlinear. We develop and test two algorithms to solve the nonlinear model: one based on inner approximation and the other on outer approximation.

Sales and operations planning: learnings from 15 Brazilian companies

ABSTRACT
 Goal: The goal of this paper is to investigate 15 Brazilian companies, which execute S&OP cycles periodically, in order to characterize the processes implemented and discuss challenges and improvement opportunities.
 Methodology: Initially, a multiple-case study approach is applied embracing 15 Brazilian companies. Then, a survey is conducted in the same organizations to deepen the investigation. Two recognized S&OP frameworks from the literature are used to structure the research.
 Results: Some of the main research findings include: some companies consider “Data Gathering” a normal task and no longer a S&OP cycle step; some organizations include a new step one named “Portfolio Management”, preceding “Demand Planning” to leverage step two’s outcomes; there are improvement opportunities identified in “Pre-meeting” and “Executive Meeting” steps regarding capacity to simulate different scenarios from a financial perspective; and most of the studied companies do not adopt a S&OP software facing limitations to manage information and perform what-if analysis.
 Research Limitations: Only companies located in Brazil are investigated.
 Practical implications: The study provides useful information for practitioners on the characterization of the S&OP process, implementation challenges, and improvement opportunities.
 Originality / Value: The paper applies different research methods (multiple-case study and survey) and two recognized frameworks from the literature in the study of the S&OP process performed by 15 companies, providing a broad characterization of the processes implemented and valuable findings about challenges and improvement opportunities. Although all the researched companies are Brazilian, evidences indicate the results are generalizable.

The dynamics of judgemental adjustments in demand planning

The dynamics of judgemental adjustments in demand planning

Probabilistic Estimation of Wind Generating Resources Based on the Spatio-Temporal Penetration Scenarios for Power Grid Expansions

The proportion of renewable energy generation is expanding worldwide with the goal of reducing greenhouse gas. According to the 8th Basic Plan for Long-term Electricity Supply and Demand in South Korea, South Korea reduces traditional energy generation such as nuclear and coal plants and achieves 20% (58.5GW) of renewable energy generation by 2030. Wind Generating Resources (WGRs) are affected by meteorological variables such as temperature, wind speed and wind direction. Specifically, WGRs have uncertainty and variability issues depending on temporal and spatial characteristics. In this paper, we propose the probabilistic estimation of wind generating resources based on the spatiotemporal penetration scenarios for power grid expansion. The data of WGRs are analyzed based on clustering method considering the spatiotemporal penetration scenarios, and the potential scenarios are estimated using Monte Carlo simulation by selecting a representative power distribution probability for each cluster. The proposed estimation model of WGRs will play a key role to develop the hedging strategies of investment decision on power grid expansion planning with high wind power penetrations.

Paradoxes & Mysteries in Virus-Infected Supply Chains: Hidden Bottlenecks, Changing Consumer Behaviors, and Other Non-Usual Suspects

In the early onset of the pandemic, COVID-19, in the U.S., consumers experienced surprising shortages of essential, and unrelated at a first look to the pandemic, goods such as toilet paper, yeast and flour, and some of their favorite meat cuts. The “usual” explanations of “demand spikes,” foreign suppliers, trade tariffs, and “hoarding behavior” of panicked consumers, often were failing to explain their persistence. The shortages ended up being real “supply chain struggles,” with their true causes going beyond the “usual” ones, and revealing a set of deeper and “unusual causes.” Our detailed analysis of the affected chains identifies these overlooked failure factors and hidden causes. Such causes include demand shifts among market segments, siloed demand planning, bottlenecks in shared resources of transportation and distribution, and inflexible supply chain structures. We conclude on the lessons from the pandemic crisis on supply chains, and the implied challenges for building resilient supply chains for the future. We need to rethink what is the relevant “system” we plan and optimize (e.g., firm, industry, region, global factor resources, etc.). Building firm-specific redundancy of assets and operational flexibility might be prohibitive in the level of investment required for any one firm, or their financial stakeholders.

Demonstration of Weapon System Requirements Based on System Engineering

Based on the thoughts and methods of system engineering, this paper explore a new model of weapon equipment system demand demonstration. A research framework for the demonstration of weapon equipment system requirements is proposed, including military background analysis, mission field requirements analysis, capability field requirements analysis, demand plan analysis, and system structure optimization design, and the specification process, theoretical principles, and technology of weapon equipment system requirements demonstration are clarified. The method and related system structure products clarify its practicability and effectiveness, and provide theoretical method support for the demonstration of weapon equipment system requirements and top-level design.

Supply chain analysis of import fresh fruit business in Thailand using the supply chain operations reference (scor) model

According to the increasing trend of online shopping through social media and reduced international trade barriers and tariffs, there is an opportunity for entrepreneurs to import fresh fruit. However, operations throughout the supply chain have suffered from product quality control and cold chain management. The research aimed to analyze the supply chain of import chilled fresh fruit based on the Supply Chain Operations Reference (SCOR) Model and suggest optimising cold chain management. The data are collected from case study executives and operators using in-depth interviews and observation. The main problems are price markdown due to the overestimation of slow-moving products and food loss and low productivity, no standard operations in receiving and distribution, and lack of proper metrics to measure the supply chain performance. Finally, the suggestions of improvement include establish demand planning and forecasting, implement supply chain optimization and inventory monitoring, develop a lot tracking system, standard operating procedures for receiving process, enhance worker efficiency, use the sensor with the Internet of Things (IoT) solution, implement supply chain risk monitoring, and online marketing. In summary, this research will be useful for other traders to reduce food loss throughout the supply chain.

Development of GHG Emission Factors for the Life Cycle of the Animal Manure Treatment Systems

Objectives:A whole process greenhouse gas emission factor was developed considering the direct greenhouse gas emission from the decomposition of livestock manure provided by the IPCC guidelines and the energy consumption of manure management systems.Methods:Greenhouse gas generated by animal manure management is divided into direct greenhouse gas emission by decomposition of manure and greenhouse gas effect in the entire process due to energy use by operating manure management systems. By obtaining and summing them, the whole process greenhouse gas emission factor for the livestock manure treatment system was calculated.Results and Discussion:Among the pig manure management systems, the greenhouse gas emission factors for composting, purification and liquefaction were calculated as 128 kgCO2-eq./ton, 123 kgCO2-eq./ton, 119 kgCO2-eq./ton, respectively. It was analyzed that 20.7% to 24.1% of greenhouse gas emissions generated in the process of managing manure were due to electricity use. As a result of analyzing the change in the emission factor according to the change in GHG emissions of the national electric power according to the 8th Basic Plan for Electricity Supply and Demand, a change in emission of about 6% was confirmed. Based on the results of this study and analysis of direct GHG emissions from manure management in three major Western European countries, France, Germany, and the Netherlands, based on the manure management emission factor in 2017, GHG emissions of 48.9% to 70% compared to this study in all countries.Conclusions:In the greenhouse gas emission factor for the pig manure management system, the greenhouse gas emission from energy used in the manure management system operation represents a contribution of more than 20%, so improvement of energy efficiency of the manure management system in the future can contribute to the reduction of greenhouse gas emission. As the GHG emissions of the pig manure management system are expected to change substantially according to the change in the power grid composition ratio according to the 8th Basic Plan for Electricity Supply and Demand, it is necessary to study the application plan in preparation for the implementation of product environmental footprint certification for livestock products in the future. As a result of comparing direct GHG emissions by manure management with major Western European countries, the difference in emissions was found to be large, suggesting the need to develop a Tier 2 emission factor suitable for the situation in Korea.

국내 단기 전력수요예측을 위한 AI 예측 모델 성능 비교분석

Uncertainty in the output of renewable energy can lead to an imbalance in power supply and demand. It can lead to disruptions in power supply and demand plans such as a decrease in the supply reserve ratio and an increase in the system marginal price. Therefore, for stable power supply and demand, accurate power demand forecasting is essential. In this paper, the accuracy of nationwide short-term power demand forecasting was compared using artificial intelligence based forecasting model. In order to evaluate the prediction error, MAPE and RMSE error functions were used, and the power demand prediction was performed according to weekdays and weekends. Also input variables were selected through correlation analysis with meteorological factors. Data from 2010 to 2018 were used for model training, and data from 2019 were used as test data. Finally the performance of each prediction method was compared through a case study.

Combining biased regression with machine learning to conduct supply chain forecasting and analytics for printing circuit board

Demand planning (DP) and sales forecasting (SF) are classical, yet, important techniques in supply chain management. In practice, demand planning is usually treated as the optimisation of internal resources (mathematical programming and game-theoretic modelling) while sales forecasting is related to the impacts of external markets (statistical regression, time series, machine learning). To help printing-circuit-board (PCB) manufacturers conduct effective DP and SF, a demand-driven framework is presented to accomplish the following goals. First, least absolute shrinkage and selection operator (LASSO) is employed to identify significant consumer products (smartphone, tablet, panel display, desktop, laptop, server, printer) affecting PCB sales volumes and revenues. Second, Holt-Winter’s smoothing (HWS) is constructed to predict global shipments of consumer products. Finally, statistical regression and ensemble learning are adopted to conduct forecasting and sensitivity analyses. In summary, the presented framework helps PCB manufacturers incorporate seasonal variations of consumer products into demand planning for PCB shipments and sales forecasting for PCB revenues. Sensitivity analyses identify panel display and laptop significantly as key products for PCB shipments while smartphone and server are critical to PCB revenues.

Cross-Functional Alignment for Sales and Operations Planning in a Cement Company in Indonesia

This paper aims to propose a conceptual framework of the Sales and Operations Planning (S&OP) process in a cement company in Indonesia. The company encountered several obstacles in their S&OP process, including asymmetric data information for planning purposes, conflicts between supply and demand planning, use of a manual spreadsheet to consolidate the supply planning, and integrating 2 (two) Enterprise Resource Planning (ERP) systems. A literature study regarding S&OP and its several aspects are performed parallels with a case study method to explore S&OP implementation in the company. The research strategy performed using focus group discussion with S&OP actors, observation on the meeting activities, and interviews with the key person in the selected functional area. The authors propose a conceptual framework of the S&OP process to resolve the problems adapted to company conditions. This paper’s contribution is to provide an overview of S&OP implementation and its problem in the enterprise-level to provide a better understanding for practitioners in other industries and also academic performers.