Demand Planning Research Articles

A Web-Application for Measuring Spatial Accessibility in Health Planning

Abstract. The planning of health care facilities and the analysis of the medical care situation are complex topics that generally involve geographical and, specifically, settlement topographical, methodological, data-driven aspects. However, formal analyses and planning in the field of healthcare usually only touch on the aspects or do not fully include some of them. As a result, the complex situation is often not accurately. This paper addresses the multidimensional challenges planners and analysts face. Solutions to overcome these hurdles thanks to improved data availability and more advanced technical and methodological possibilities are presented. For this purpose, a Web-Application is proposed that enables planners without GIS knowledge to interact with method components via parameter adjustments. Subsequently, it is examined whether the technical prerequisites for the implementation of a Web-Application for health planning, in particular for outpatient demand planning and analysis, are given and make a proposal for implementation. Finally, the advantages, disadvantages and possible complications of using this Application in health planning and analysis, including critical points of implementation, will be addressed.

Prospective External Validation of a Commercial Model Predicting the Likelihood of Inpatient Admission From the Emergency Department.

Early notification of admissions from the emergency department (ED) may allow hospitals to plan for inpatient bed demand. This study aimed to assess Epic's ED Likelihood to Occupy an Inpatient Bed predictive model and its application in improving hospital bed planning workflows. All ED adult (18 years and older) visits from September 2021 to August 2022 at a large regional health care system were included. The primary outcome was inpatient admission. The predictive model is a random forest algorithm that uses demographic and clinical features. The model was implemented prospectively, with scores generated every 15 minutes. The area under the receiver operator curves (AUROC) and precision-recall curves (AUPRC) were calculated using the maximum score prior to the outcome and for each prediction independently. Test characteristics and lead time were calculated over a range of model score thresholds. Over 11 months, 329,194 encounters were evaluated, with an incidence of inpatient admission of 25.4%. The encounter-level AUROC was 0.849 (95% confidence interval [CI], 0.848 to 0.851), and the AUPRC was 0.643 (95% CI, 0.640 to 0.647). With a prediction horizon of 6 hours, the AUROC was 0.758 (95% CI, 0.758 to 0.759,) and the AUPRC was 0.470 (95% CI, 0.469 to 0.471). At a predictive model threshold of 40, the sensitivity was 0.49, the positive predictive value was 0.65, and the median lead-time warning was 127 minutes before the inpatient bed request. The Epic ED Likelihood to Occupy an Inpatient Bed model may improve hospital bed planning workflows. Further study is needed to determine its operational effect.

Hybrid intelligence in procurement: Disillusionment with AI’s superiority?

Despite the numerous benefits of general artificial intelligence applications, there are challenges in its introduction and implementation. This paper examines the limits of artificial intelligence and the capabilities of so-called hybrid intelligence, the interplay between human and artificial intelligence. We present four in-depth case studies of the combined use of human and artificial intelligence in procurement processes, such as demand planning, supplier selection, order management and procurement analytics. This study identifies the advantages and disadvantages of human and artificial intelligence in different applications and proposes a model process of hybrid intelligence in procurement. The findings are summarized in eight propositions concerning the impact and role of human and artificial intelligence as well as mutual learning. We conclude that the unilateral application of artificial or human intelligence poses risks in numerous process steps, whereas an iterative hybrid intelligent process offsets shortcomings and boosts performance regarding cost, time, and quality. Practical implications include the identification of benefits and risks of human, artificial and hybrid intelligence in typical processes at the buyer-supplier interface and an outlook on the future of artificial and hybrid intelligence in procurement.

Demand planning for the digital supply chain: How to integrate human judgment and predictive analytics

AbstractOur research examines how to integrate human judgment and statistical algorithms for demand planning in an increasingly data‐driven and automated environment. We use a laboratory experiment combined with a field study to compare existing integration methods with a novel approach: Human‐Guided Learning. This new method allows the algorithm to use human judgment to train a model using an iterative linear weighting of human judgment and model predictions. Human‐Guided Learning is more accurate vis‐à‐vis the established integration methods of Judgmental Adjustment, Quantitative Correction of Human Judgment, Forecast Combination, and Judgment as a Model Input. Human‐Guided Learning performs similarly to Integrative Judgment Learning, but under certain circumstances, Human‐Guided Learning can be more accurate. Our studies demonstrate that the benefit of human judgment for demand planning processes depends on the integration method.

A Novel Power Measurement Method Using Lock-In Amplifiers with a Frequency-Locked Loop

The extensive use of renewable energy systems with grid-connected inverters (GCIs) causes harmonic injection. Similarly, the imbalance in energy demand and supply causes frequency fluctuations. As a result of the increased harmonics and frequency fluctuations, the accuracy of power measurement using conventional methods continues to decline. Precision in power measurement is an essential factor for the billing and management of power supply and demand. Moreover, it is challenging to build a supply plan for the power demand and to manage the billing for the power consumption. To solve these problems, this paper proposes a novel method based on Lock-in Amplifier (LIA) and Lock-in Amplifier Frequency-Locked Loop (LIA-FLL) to measure the power with high precision and accuracy. The proposed method first tracks the variations in the input signal frequency using LIA-FLL and generates the updated reference signals for LIA. After that, the LIA is used to extract the accurate amplitude of each frequency component. The proposed method results in accurate and precise measurement, even with harmonics and frequency fluctuations. The validity of the proposed method is verified by comparing the power measurement results with the classical method, FFT, and ZERA COM3003 (a commercially available power measurement reference instrument).

Changes in Air Quality, Meteorology and Energy Consumption during the COVID-19 Lockdown and Unlock Periods in India

The increasing population and its associated amenities demand innovative devices, infrastructure, methods, plans and policies. Regional climate has a great role in deciding the air quality and energy demand, and therefore, weather and climate have an indisputable role in its consumption and storage. Here, we present the changes in trace gases and associated regional weather in India during lockdown and unlock periods of COVID-19. We observe a reduction of about 30% in sulphur dioxide (SO2) and 10–20% in aerosols in the Indo-Gangetic Plain (IGP), large cities, industrial sites, mining areas and thermal power plants during lockdown as compared to the same period in the previous year and with respect to its climatology. However, a considerable increase in aerosols is found, particularly over IGP during Unlock 1.0 (1–30 June 2020), because of the relaxation of lockdown restrictions. The analyses also show a decrease in temperature by 1–3 °C during lockdown compared to its climatology for the same period, mainly in IGP and Central India, possibly due to the significant reduction in absorbing aerosols such as black carbon and decrease in humidity during the period. The west coast, northwest and central India show reduced wind speed when compared to its previous year and climatological values, suggesting that there was a change in regional weather due to the lockdown. Energy demand in India decreased by about 25–30% during the first phase of lockdown and about 20% during the complete lockdown period. This study thus suggests that the reduction of pollution could also modify local weather, and these results would be useful for drafting policy decisions on air pollution reduction, urban development, the energy sector, agriculture and water resources.

Estimation Method for Transmission Line Overloads Due to Renewable Energy Source (RES) Output Forecast Errors in Daily Supply and Demand Planning

AbstractWe propose a method to estimate transmission‐line overloads due to forecast errors of power output of renewable energy sources. The proposed method involves particle swarm optimization and optimal power flow calculations. The effectiveness of the method is confirmed via numerical simulations using a modified IEEE RTS 24‐bus system model. The results indicate that the proposed method can determine the forecast errors of the renewable power output that causes thermal overload, and further predict the occurrences of transmission‐line overload under generator activation in supply and demand planning. Furthermore, we explain the occurrence mechanism of transmission‐line overload due to forecast errors, which may contribute to improve the daily supply and demand planning. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

Importance of the implementation of the S and OP system in modern industries

In recent years, there has been a growing demand for Supply Chain Management (SCM) to become more agile, integrated, and data-driven, especially with the advent of Industry 4.0. To meet this demand, companies have focused on implementing Integrated Sales and Operations Planning (S and OP) processes, which seek to integrate and align the activities of Sales, Operations, Demand Planning, Supply Planning, and Finance. A qualitative approach was adopted for the development of this research, using a literature review as the investigation procedure. The overall objective of this research is to demonstrate the importance of implementing the S and OP system in modern industries. As a result, an alignment in the company's perception of the level of service expected by the customer is observed, focused on delivery time and commercial conditions, but its process has areas for improvement when viewed in the light of S and OP.

Demand planning of a nearly zero energy building in a PV/grid-connected system

The energy consumption planning of smart homes connected to a grid with flexible power pricing, and complemented with a local PV installation, is studied. Advanced energy management is necessary to implement the Nearly Zero Energy Building (nZEB) concept in these smart homes. To do so, in the Energy Management System, it is essential to consider the flexibility provided by power pricing schemes, the distributed generation from renewable energies and the use of smart household appliances. This paper studies a novel formulation that depends on the comfort index required by the user, the PV power production, and the price variation of the grid energy. The study is performed on two levels: in the first, a metaheuristic multi-start, consisting of two steps, constructive heuristics and refinement heuristics (local search), is proposed to regulate the power demand of household appliances. The second level consists of determining the optimum PV installation that ensures the economic viability of the plant. The novelty of the paper is that the user defines a desired comfort level as input data, and the algorithm defines a demand plan that meets this comfort at the lowest possible cost. Six real case studies were carried out, considering the insertion of photovoltaic generation, to evaluate the performance of the proposed model from a technical and economic point of view. The results show that the proposed approach allows user requirements to be fulfilled and the energy production cost to be minimized. Without needing to reduce user comfort, the proposed demand planning algorithm can reduce the energy cost by 48.07 %. In this same scenario, combining the proposed method and the insertion of a photovoltaic system composed of 15 modules (Scenario 6), the total energy cost is reduced by 73.90 %.

Food poverty: a scale unimagined

Abstract When Dan Clarkson volunteered to help at a UK food bank, he discovered how statistics and data analysis can help organisations assess need, plan for demand, and better understand the extent of food poverty

역세권 공공임대주택에 대한 선호도 및 수요특성 연구

The government and the Seoul Metropolitan Government are continuously increasing the supply of public rental housing mainly in large quantities, but vacancies are increasing. The reason is that it was supplied mainly in large quantities without considering the small equilibrium-oriented supply outside the city center and the characteristics of demand for policy targets. Public rental housing to achieve the purpose of stabilizing housing for the common people should be supplied according to the characteristics of consumers, such as location and accessibility. Therefore, this study analyzed the preference and demand characteristics by class for long-term rental houses in the station influence area of Seoul. Classification by stratum was divided into age, income, occupation, and living area. A survey on the preference of policy consumers by stratum was conducted to analyze supply policies suitable for demand characteristics. In addition, the purpose of this study is to analyze customized demand and supply plans based on the analysis of consumers' preferences and characteristics by class according to the location of the station influence area. In order to expand the supply of public rental housing in the station area, policy implications and improvement directions were presented so that customized supply based on demand characteristics by stratum.

Sales Forcasting and Organizational Performance in Bakery Industry in Nigeria

Planning is an integral part of a manager’s job. If uncertainties cloud the planning horizon, it can be quite difficult for a manager to plan effectively. Sales forecasts play such an important role in the planning process because they enable managers to anticipate the future and to plan accordingly. Firms must anticipate and plan for future demand so that they can react immediately to customers’ orders as they are made. The ability to accurately make sales forecast enables firms to control costs by leveling their production quantities, rationalizing their transportation and planning for efficient logistics operations. A great number of organizations have failed in their sales forecast planning and process that provide them the required and accurate sales forecasting result. The problems associated with sales forecasting planning and process, and the activities of the sales people in enhancing the sales forecasting decisions of the organization have been difficult for some organizations to identify. It is on this background that this work is laid. Therefore, this study examines the impact of forecasting on organizational performance in the bread making industry. Both qualitative and quantitative research methodologies were used to analyze data gathered from the field. From the result obtained, conclusions were made to the fact that all the dimensions of sales forecasting have a positive and significant effect on the dimensions of organizational performance. The study thus concluded generally that sales forecasting is a viable and veritable tool in measuring and improving organizational performance in the bread making industry in Nigeria. The study therefore recommended that organizations should have a good sales plan in place as it will not only help them to formulate a strategic plan but it will also put them in control by helping them determine their product’s current status, where they want to take it, and how they will get there. Also, organizations should develop effective and efficient sales process that provides them with data on what is happening in the sales pipeline.

국내 전력계통의 운영조건을 고려한 변동성 재생발전원의 정상상태 수용한계 평가방법

In this paper, considering the electric power generation characteristics of variable renewable sources(VRS) due to the dependence on the weather conditions the factors that would limit the increasing penetration level of VRS in the power system are analyzed, and then the method for evaluating the steady-state penetration limit of VRS is presented for each factor. In addition, through study cases employing the planning data developed from the 9th Basic Plan for Long-term Electricity Supply and Demand and historical operation data of Korean power system, the effectiveness of the proposed method is verified.

Prediction of NOx Emission Based on Data of LHD On-Board Monitoring System in a Deep Underground Mine

The underground mining industry is at the forefront when it comes to unsafe conditions at workplaces. As mining depths continue to increase and the mining fronts move away from the ventilation shafts, gas hazards are increasing. In this article, the authors developed a statistical polynomial model for nitrogen oxide (NOx) emission prediction of the LHD vehicle with a diesel engine. The best-achieved prediction accuracy by the 4th order polynomial model for 11 and 10 input variables is about 8% and 13%, respectively. It is comparable with the sensors’ accuracy of 10% at a stable regime of loading and 20% in the transient periods of operation. The obtained results allow planning of ventilation system capacity and power demand for the large fleet of vehicles in the deep underground mines.

Supporting of manufacturer’s demand plans as an element of logistics coordination in the distribution network

Abstract The paper deals with the concept of centralized demand forecasting and logistical coordination in distribution networks. The aim of the paper is to relate the results provided by the forecasting tools to the basic aspects of logistical coordination. The case of 29 distribution networks in which a logistics operator (3PL) operates and provides contract logistics services to a manufacturing company is analysed. The paper partially confirms the hypothesis of better testability of forecasts based on machine learning algorithms and artificial neural networks for demand planning by the logistics operator to the manufacturer in the framework of logistics coordination in the distribution network. These algorithms perform better for networks with high specificity of flows and food networks. Traditional algorithms, on the other hand, have their better share in creating forecasts for more standard distribution networks. Additionally, the second hypothesis regarding the positive influence of modern technological solutions (such as the use of cloud technologies, EDI and flow tracking standards) was confirmed. Additionally, a number of factors that did not have a direct impact on forecasting errors were detailed.

Primary Care Aspects in the German Coalition Agreement 2021

Despite statewide rising numbers of family physicians during the last legislative period, their overall health care providing capacity has declined, mainly due to their working part-time. In the legislative period starting in 2021, this trend appears likely to continue. The German Health System Advisory Council foresees the number of general practitioners (GPs) who will have to be replaced by the next federal election in 2025 as exceeding 26,000. This results in a need for political action to sustain the primary care workforce. We conducted a content analysis of the coalition agreement 2021 between SPD, Greens and FDP with regard to explicit and related topics of GP care. A mixed inductive-deductive classification of the included text passages was used to thematically structure the results. A total of 34 relevant text passages were identified between pages 8 to 139 of the coalition agreement. Family physician care was explicitly addressed only once, when the planned abolition of budgeting was discussed. Other proposals which affect family physicians work included reduction of bureaucracy, prevention, or easier access to medical care for disadvantaged groups. Other passages of the coalition agreement regarding security of medical supply, included demand planning, innovative healthcare models, and cross-sectoral care. One focus of the coalition agreement was on digitalization. It mentions e. g., a digitization strategy for the healthcare sector, telemedicine services, and the electronic medical record, including opt-out possibility. Other assessed passages included projects to facilitate research or climate change and health. Despite the lack of mention of family physicians in the coalition agreement, many projects relate to the work of family physicians. Some of the ideas mentioned have also already been written down by the German Society for General Practice and Family Medicine (DEGAM) in its positions on the future of general practice, digitization or climate change and health. The planned projects in the coalition agreement have the potential to strengthen health care by family doctors and also make it more attractive for young doctors. However, further measures may be necessary to maintain GP care in the short and long term.

An Exploration of the Implications of Human Resources Analytics for Workforce Planning

Workforce is a tangible and talent-driven asset for high-performing organisations in today's global economy. Arguably, if workforce is not adequately planned, it impacts the effective execution of future business strategies. This research uses Standard Chartered and HSBC Holdings as a case study. A sample size of 60 participants is selected and descriptive data analysis employed to examine the online survey questionnaire. The findings indicate that HRA helps organisations to develop and sustain high-quality planning and eliminates the conventional obstacles HRA provides. It aligns strategic planning by head counting and establishing a clear view of talent demand and supply problems. It is a useful tool used to evaluate the impact of their decision and prioritise future investments in the workforce. Key identifiers include: Control, employee production efficiency, and structure. The study recommends organisations combine demand planning and internal supply analysis to identify workforce gaps and plan accordingly.

Workers’ Opinions on Using the Internet of Things to Enhance the Performance of the Olive Oil Industry: A Machine Learning Approach

Today’s global food supply chains are highly dispersed and complex. The adoption and effective utilization of information technology are likely to increase the efficiency of companies. Because of the broad variety of sensors that are currently accessible, the possibilities for Internet of Things (IoT) applications in the olive oil industry are almost limitless. Although previous studies have investigated the impact of the IoT on the performance of industries, this issue has yet to be explored in the olive oil industry. In this study we aimed to develop a new model to investigate the factors influencing supply chain improvement in olive oil companies. The model was used to evaluate the relationship between supply chain improvement and olive oil companies’ performance. Demand planning, manufacturing, transportation, customer service, warehousing, and inventory management were the main factors incorporated into the proposed model. Self-organizing map (SOM) clustering and decision trees were employed in the development of the method. The data were collected from respondents with knowledge related to integrating new technologies into the industry. The results demonstrated that IoT implementation in olive oil companies significantly improved their performance. Moreover, it was found that there was a positive relationship between supply chain improvements via IoT implementation in olive oil companies and their performance.

Production scheduling of small batch materials based on dynamic programming

With the development of society, more and more enterprises begin to pay attention to internal management, such as material production arrangement, inventory management, etc. No matter what type of products or services an enterprise produces or operates in any industry, a certain amount of production preparation time and production time are required to provide products and services required by the market. Therefore, it is necessary for any enterprise to forecast the future demand of the target market and make production plans. This paper selects six kinds of key materials, comprehensively considers the forecast value of demand, demand characteristics, inventory and out of stock, and reasonably arranges and forecasts the number of material production plans. Further analysis and forecast the demand and production plan when the materials planned to be produced in this week can only be used in k (k ≥ 2) weeks.

Multi-objective optimal dispatching of demand response-enabled microgrid considering uncertainty of renewable energy generations based on two-level iterative strategy

In this study, multi-objective optimal dispatching of demand response-enabled microgrid considering uncertainty in renewable energy generations is designed to obtain optimal energy point sets of microgrid. The proposed dispatching model contains upper-level model and lower-level model. The economic and environmental benefits of the microgrid are adopted as the optimization objectives in the upper-level model and the economic benefits under incentive-based demand response is taken as the optimization objective in the lower-level load demand. In the upper-level model, sequence operation theory is designed to deal with the uncertainty in renewable energy generations, and virtual real-time tariffs and electricity credibility of power consumption are utilized to make demand response by shifting load in the lower-level model. To optimally solve the proposed model, improved enhanced multi-objective optimization sparrow search algorithm (EMOSSA) based on sinusoidal search strategy, diversity variant processing by Cauchy variation and external archive updating mechanism is developed to optimize the objective functions of the upper-level model and mixed integer linear programming CPLEX is employed to determine the load demand for the lower-level model. After that, the power demand plan of the lower-level model and the real-time prices of the upper-level model are updated iteratively to obtain the optimal dispatching solutions using the real-time pricing mechanism. Eventually, experimental results of several operation scenarios illustrate the proposed optimal dispatching method can deal with the uncertainty of renewable energy generations and the randomness of load, thus, achieving a well supply and demand balance between microgrid and users.