Demand Planning Research Articles

Operational planning for public holidays in grocery retailing - managing the grocery retail rush

Public holiday weeks cause specific challenges in grocery retailing as sales are raising and working days for logistics processes are reduced. The paper analyzes the operational planning challenges and solutions for demand planning and disposition as well as for warehouse and transportation management of grocery retailers in public holiday seasons. A total of 22 top managers representing 20 sales lines of 17 of the top 30 grocery retailers in Germany participated in the study. Semi-structured, face-to-face interviews with logistics managers were conducted and analyzed. Uncertainties and missing resources can be identified as the two main challenges of public holiday seasons in grocery retailing. Retailers implement numerous measures that can be summarized in three categories, i.e., the adjustment of workload profiles, the adaptation of resources and modifying processes. Literature has so far considered public holidays only to a limited extent, e.g., as a parameter in forecasting models or for the application of marketing instruments. This study is the first developing a framework and providing insights into operational planning in grocery retailing.

ML2-enabled Condition-based Demand, Production, Inventory, and Maintenance Planning

Production planning and control is pivotal to meeting customer demand and maximizing profit. At the same time, machine breakdowns compromise these goals, which can be tackled with a good maintenance strategy. Here, advances in condition-based maintenance and prognostics and health management allow predicting the health state of production machines through sensor data and prescribing optimal demand, production, inventory, and maintenance plans. Here, machine learning (ML) is promising for accurate health predictions using sensor data and decision-making in complex, highly dynamic production environments. Thus, in this work, two ML algorithms are applied. First, a data-driven regression algorithm predicts the health of a machine. This forecast is forwarded to a reinforcement learning algorithm (i.e. proximal policy optimization, recently made famous by its application within ChatGPT) to optimize demand, production, inventory, and maintenance plans. A computational study shows excellent performances of the ML-based health prediction and planning algorithms, which surpass traditional maintenance strategies.

The Dynamics of Judgemental Adjustments in Demand Planning

The Dynamics of Judgemental Adjustments in Demand Planning

Framework for predictive sales and demand planning in customer-oriented manufacturing systems using data enrichment and machine learning

Companies with Make-to-Order (MTO) manufacturing have always faced the conflict of meeting a large volume of individual customer orders on time while remaining as flexible as possible. Unlike Make-to-Stock (MTS) manufacturing, planning the production requirements for MTO is a challenging task since incoming orders may vary in time and quantity, while also being subject to a number of variables. In some cases, the delivery time allocated by the customer can be less than the required Order Lead Time to fulfil the order. Manufacturers can respond to this with either with approached from production management or from data science. This paper presents a framework to leverage the benefit of both domains. We conduct a literature review and present the results of an expert workshop. We propose criteria for a suitable data enrichment and the application of Machine Learning (ML) methods in sales and demand forecasting. In conclusion, the proposed framework helps to equip manufacturing companies with a structured strategy for data management and to utilize the benefit of ML for sales and demand forecasting.

Организационно-экономический механизм закупочной деятельности предприятий нефтяной отрасли: методический аспект

Current legislation in the field of public procurement focuses on ensuring maximum transparency and openness of procurement at different stages of its implementation. However, at present, the methodology for assessing the effectiveness of procurement by commercial organizations is imperfect. The research topic is relevant. The purpose of the study is to develop the fundamentals of methodological support for the procurement system of oil refineries. The object of the study is the oil company LLC “LUKOIL – PERM” in the “geological exploration, oil and gas production” segment. The theory and practice of the mechanism for carrying out procurement activities of oil producing enterprises is the subject of research. The concepts and types of procurement, legal regulation of procurement activities in commercial organizations are revealed. An analysis of methods for assessing the effectiveness of procurement is provided. Systematization of information and technological support for procurement activities at oil producing enterprises was carried out. The main factors for assessing the effectiveness of the existing procurement system are identified: price, reliability and time. Based on the study of practical aspects, significant shortcomings were identified: procurement planning is unreliable, digital technology is not used enough, there is no system of criteria for assessing demand planning and procurement procedures. In this regard, in order to more accurately plan demand and apply fair prices, the corporate system of dictionaries and reference books (KSSS) of the LUKOIL Group has been improved. A system has been developed for assessing the effectiveness of procurement procedures in an oil producing company, which makes it possible to determine the effectiveness of inventories of material and technical resources (MTR) for all inventory items. A methodology has been developed for assessing the effectiveness of demand planning and budgets according to the data of the integrated management system of oil and gas producing subsidiaries (IMS NGDO). To determine the level of competition of potential counterparties, an original method for assessing the effectiveness of the supplier selection process is proposed. The practical significance lies in the possibility of applying the developed methodology in the practice of organizational and economic support for procurement at enterprises. The study may be useful for specialists involved in procurement activities, for managers and those making management decisions related to achieving the goals of sustainable development of the organization.

Analyzing the supply chain sustainability of an internet service provider in Thailand

This paper aims to comprehensively analyze the supply chain activities of an Internet Service Provider company in Thailand, focusing on sustainability. The company utilizes the SCOR model and IDEF0 methodology to analyze the supply chain and offer rental equipment services. The major issues identified include inventory management, insufficient data in demand planning, supply chain planning, renewal contact planning, the role of suppliers, and last-minute changes during the installation process. To address these challenges, the paper suggests improvements such as optimizing equipment flow, improving communication with teams, implementing demand forecasting models, considering alternative suppliers, and clarifying technical requirements with customers. By implementing these recommendations, ISPs can enhance the sustainability of their supply chains, reducing environmental impacts, optimizing resource utilization, and meeting customer expectations for sustainable practices. This study highlights the importance of sustainable supply chain practices in the telecommunications industry and offers valuable insights for companies seeking to improve their operations in a more environmentally responsible manner.

Forecast Value Added in Demand Planning

Forecast Value Added in Demand Planning

Justification of Plants Water Demand in Conditions of Drip Irrigation

In the current work, a comprehensive methodology for the soil and climatic conditions of the Republic of Armenia has been developed to justify the water requirement/demand of plants in the case of drip irrigation. The proposed methodology enables to calculate the values of the individual components of the drip irrigation regime, and in practical terms, it was used to determine the water requirement of perennial plantations in the soil and climatic conditions of the Armavir and Kotayk marzes. Based on the obtained results, the crops drip irrigation norms/rates were mapped in the GIS environment. They can serve as a background for practical planning and justification of the drip irrigation water demand for various crops and contribute to the development of introduction rates of water-saving technologies in this field.

Influence of Selected Environmental Factors on the Business Type of Dentist's Practice in Germany: A Multi-Criteria Decision-Making Process With an Analytical Hierarchy Process.

In the interests of satisfying the dental services demands of German citizens area-wide, constant, and thoughtful planning of supply and demand is essential. With an anonymous online survey of 375 dentists a pairwise comparison of 9 factors extracted as relevant from the existing scientific literature were analyzed with an analytic hierarchy process (AHP) and ranked considering the various business types. In general, 5 local environmental factors have a dominant impact on founders' decision in German dentistry. In order: environment for the family, quality of life in private environment, real income, location of the practice, infrastructure. Real income is in first place (p = 0.287) for dentists who want to start a new single practice. For preferring a new community practice, it is on third place (p = 0.177) and for dentists who favor a takeover a single practice (p = 0.130) or joining a community practice (p = 0.096) or employment (p = 0.111) it is fourth place. For this purpose, the location of the practice is of greater priority than the real income for dentists who prefer not to start a new practice. The AHP method is a way to picture a priority list out of all relevant factors for setting up of a dental practice.

Optimization of tertiary building passive parameters by forecasting energy consumption based on artificial intelligence models and using ANOVA variance analysis method

<abstract> <p>Energy consumption in the tertial sector is largely attributed to cooling/heating energy consumption. Thus, forecasting the building's energy consumption has become a key factor in long-term decision-making, reducing the huge energy demand and future planning. This manuscript outlines to use of the variance analysis method (ANOVA) to study the building's passive parameters' effect, such as the orientation, insulation, and its thickness plus the glazing on energy savings through the forecasting of the heating/cooling energy consumption by applying the Seasonal Auto-Regressive Integrated Moving Average (SARIMA) and the Long Short-Term Memory (LSTM) models. The presented methodology compares the predicted consumed energy of a baseline building with another efficient building which includes all the passive parameters selected by the ANOVA approach. The results show that the improvement of passive parameters leads to a reduction of heating energy consumption by 1,739,640 kWh from 2021 to 2029, which is equivalent to a monthly energy consumption of 181.2 kWh for an administrative building with an area of 415 m<sup>2</sup>. While the cooling energy consumption is diminished by 893,246 kWh from 2021 to 2029, which leads to save a monthly value of 93.05 kWh. Consequently, the passive parameters optimization efficiently reduces the consumed energy and minimizes its costs. This positively impacts our environment due to the reduction of gas emissions, air and soil pollution.</p> </abstract>

Airport and Air Route Demand Planning: A Connectivity-Based Methodology for New Route Identification

Airport and Air Route Demand Planning: A Connectivity-Based Methodology for New Route Identification

Management Model to Increase Inventory Rotation in SMEs in the Mining Industry

Logistics is an area that determines, in many cases, the prosperity of a company. Regardless of its size, the search for new tools, procedures and management techniques has increased in recent years. In the mining sector, the 49,9% of companies do not have an inventory control mechanism; therefore, a low turnover of this. This can be for three reasons: inefficient purchasing policy, inadequate demand planning, and inadequate storage method. For this reason, a model conformed by the following tools was developed: ABC classification, JIT, MRP, and inventory management. Likewise, the Arena Software was used in an SMEs that imported drill bits for the mining industry to validate the described model. The results were a 62% improvement over the purchasing policy, a 73% decrease in cycle time, and only the use of one worker for the import process, allowing the other worker to be in another area. Finally, with this research is possible to conclude that correct inventory management has a positive impact on the company's economy.

Production Scheduling Based on Smart Forecasting Model of Bottled Mineral Water Products

Optimal production planning is a problem that causes product stock buildup at PT Marina. Factors that affect production planning in the company are the conditions of Demand, Safety Stock, and Production Costs. The results of the demand forecasting method chosen are the Moving Average with N = 3 and the Mean Absolute Percentage Error (MAPE) = 0.05 is 68,084 boxes/period with a production cost of IDR 544,672.00/period. The Safety Stock of bottle products in the ninth forecasting period is 8076 Boxes. Based on the three factors above, an intelligent production planning model was developed using a fuzzy logic approach. The result of defuzzification of demand planning for bottle products for the ninth or three months using the Center of Area (COA) method was 59,917 Boxes. Based on the defuzzification of production planning, the total cost of aggregate production planning for the next three months using the chase strategy method is 617,235,300. 1500 ml = 17,105 boxes. Based on the aggregate planning model above, the company can schedule production and raw materials so that the warehouse’s product stock management is maintained optimally.

Preventing waste in food supply networks - a platform architecture for AI-driven forecasting based on heterogeneous big data

Waste is a major challenge in today´s food supply networks. It induces economic, ecologic, and social implications. Current advanced supply and demand planning frameworks consider waste as an expected lost value in a profit-seeking trade-off. Consequently, existing initiatives for food re-distribution tackle the problem at the end of the supply chain, i.e., at the retailer stage. Advanced forecasting is foreseen as a facilitator of planning towards food waste prevention. However, companies in food production and retail seldomly utilize advanced forecasting methods, i.e., artificial intelligence. Deterrents for inadaptability rank from the differentiation between the role of pure data driven reliance and the human factor. Furthermore, no remarkable endeavor has so far combined heterogeneous external data integration with advanced forecasting techniques for food waste reduction. This paper proposes an architecture for a collaborative digital demand and supply matching platform for perishable food supply chains. Focus lies in the integration of heterogeneous big data (i.e., mobile transaction data and weather data) with AI-based forecasting methods. Building on the Design Science Research methodology, problems and requirements from the perishable food industry form the base for the platform architecture as the core artifact. The validation of the proposed architecture is undertaken by a panel of selected business experts from food retail and wholesale. The designed platform architecture consists of the core modules “data layer”, “logic layer”, “interaction layer”, and “data visualization”, and includes governance rules for sensible knowledge protection and data security. The present work contributes to the research area of food waste prevention by combining collaborative supply chain management approaches with advanced data-driven technologies including artificial intelligence and heterogeneous big data integration. Further research will extend this study by a use-case implementation.

선도 기술 적용을 위한 한국군 무기체계 소요기획 발전방향 연구 : 기술주도, 기술견인, 수요주도, 수요견인을 중심으로

In the era of the 4th Industrial Revolution, the current Korean military's demand planning and weapon system acquisition method was suggest to improve measures for cutting-edge technology applied weapon system. Currently, the problem of the Korean military weapon system acquisition system is the demand-oriented planning and acquiring. The demand-oriented weapon system has the advantage of securing the weapon system that the military needs, but it lacks flexibility considering the shortened life-cycle of technology and changes in the security environment. In particular, more innovative planning is needed to solve the problems of troop reduction and insufficient quantitative and qualitative military power compared to neighbor countries. To do so, it is essential to secure a qualitative advantage by developing leading technologies and securing a weapon system to which leading technologies are applied. In this study, demand-push, demand-pull, technology-push, and technology-pull frameworks, which are methods of market innovation, were introduced to analyze military's planning and acquiring weapon systems. A theoretical framework was presented so that the weapon system planning and acquisition methods could be classified and analyzed in more detail. In addition, an institutions were proposed to develop future weapon system with leading technologies.

Sectoral electricity consumption modeling with D-vine quantile regression: The US electricity market case

ABSTRACT Efficient electricity demand planning is crucial for energy market actors. However, it is difficult as a consequence of climate change. We aim at investigating how climate variables (heating and cooling degree days) may affect electricity demand. By examining electricity consumption in various US sectors, we explore this relationship using parametric and non-parametric D-vine quantile regression models that exploits the dependence between covariates and allows sequential covariate selection. The results are compared against the classical linear quantile regression. We find a positive effect of the climatic variables on electricity consumption that is as heating and cooling degree days increase electricity demand rises in all sectors, and cooling need has a greater impact than heating need. Evidence suggests that residential and commercial electricity consumptions are affected the most, while industrial and transport sector consumptions are less sensitive. The D-vine quantile regression performs better than the linear quantile regression for almost all sectors.

A hidden anchor: The influence of service levels on demand forecasts

AbstractDemand planning is informed by demand forecasts, service level requirements, replenishment constraints, and revenue projections. “Demand forecasts” differ from “demand plans” in that forecasts only represent the distribution (or the most likely value) of product demand. Motivated by common forecasting practices in industry, our research examines whether forecasters recognize this difference between demand forecasts and demand plans. Based on a lab experiment informed by data from two large FMCG companies, we found that forecasters factor service levels into their demand forecasts, even when they are clearly instructed to predict the most likely demand and incentivized to minimize the forecast error. We establish that this result holds for students and practitioners alike, and show that this behavior is driven by the service level information, and not some other anchor. We use data from a recent industry survey to support the external validity of our key findings.

The impact of different types of service robots usage in hotels on guests’ intention to stay

酒店不同类型服务机器人的使用对客人入住意愿的影响摘要研究目的本研究以技术接受模型 (TAM)为理论, 考察了消费者如何看待使用机器人技术的两个类别下评估的十二项服务的重要性。研究设计/方法/途径本研究使用概念模型分析和具有偏最小二乘估计方法的结构方程建模, 考虑了 638 个响应。研究发现：结果表明“房间划分”和“餐饮和二级服务”下机器人服务交付任务的感知重要性对感知有用性（PU）和感知易用性（PEOU）的影响不同。此外, PEOU 和 PU 显着影响使用机器人酒店的态度。研究局限本研究采用非概率便利抽样方法作为数据收集方法。本文为未来更偏好概率方法的研究将为评估结果开辟不同的视角。研究实践意义本研究的实证结果揭示了客户认为重要的机器人提供服务, 并有助于提高客户满意度和忠诚度。此外, 研究结果还有助于指导后 Covid-19 时代旅游和酒店业的准确需求和投资规划。研究原创性/价值据我们所知, 以前的文献尚未测试或证实与两组机器人服务交付任务相关的感知重要性对功用性变量的影响。本研究通过检查不同的机器人服务交付任务如何与酒店环境中的 TAM 框架相关联, 为文献做出了贡献。

Research on Multi Variety and Small Batch Material Management

The development of the logistics industry has greatly changed people's lifestyle. More and more enterprises have begun to pay attention to internal management. The purpose of material management is to enable enterprises to meet the needs of the user departments at the right time, at the right amount, at the right price and with the right quality, with the lowest cost and the ideal and rapid process, so as to reduce the loss and maximize the efficiency of materials. 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.

Decision Support in Horticultural Supply Chains: A Planning Problem Framework for Small and Medium-Sized Enterprises

This paper investigates and systematizes planning problems along the supply chain of small and medium-sized companies in the horticultural market of ornamental plants, perennials, and cut flowers. The sector faces considerable challenges such as multiple planning uncertainties, product perishability, and considerable lead times. However, decisions in practice are often based on rules of thumb. Data-driven decision support is thus necessary to professionalize supply chain, logistics, and operations planning in the sector. We explore the practical planning problems with the help of expert interviews with people in charge of typical companies active in the market. We structure the planning problems along the supply chain according to their time horizon and highlight the critical elements of the planning tasks and horticultural specifics. We examine the status quo of research on decision support for these planning tasks with the help of a structured literature review, highlight research gaps, and outline promising future research directions. We find that the tactical planning domains of material/product requirement, production, and demand planning are especially critical in practice, and that there is a great need for research to develop practically relevant decision support systems. Such systems are currently available only to a limited extent in literature and are not fully compatible with requirements in the ornamental horticultural sector. By structuring and detailing the relevant decision problems, we contribute to an understanding of planning problems and decision-making in horticultural supply chains, and we provide a first comprehensive overview of planning problems, aligned literature, and research gaps for the horticultural business.