Search

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Efficient demand planning holds critical significance for businesses. In this research, the authors investigate the applicability of the Temporal Fusion Transformer, a neural network-based model, to address demand planning challenges. Specifically, they explore the potential benefits of incorporating additional information related to product characteristics and sales channel types. The primary objective of this study is to assess the advantages gained by incorporating these supplementary variables. The dataset utilized in this analysis originates from a company predominantly engaged in the sale of building engineering products. The authors initially focus on static attributes such as product groupings and time-varying attributes such as sales channel variations. This paper’s contribution lies in its comprehensive case study, which applies the Temporal Fusion Transformer model to a real-world demand planning problem of the company, including all its specifications and customizations. [ABSTRACT FROM AUTHOR]

Background: Technological advancements, particularly in Artificial Intelligence (AI), are revolutionizing operations management, especially in the domain of supply chain management. This paper delves into the application of AI in demand planning processes within the supply chain context. Drawing upon a comprehensive review of the existing literature, the main objective of this study is to analyze how AI is being applied and adopted in the demand planning process, identifying the resources needed to build the capacity of AI in the demand process, as well as the mechanisms and practices contributing to AI capability's advancement and formation. Methodology: The approach was qualitative, and case studies of three different companies were conducted. Results: This study identified crucial resources necessary for fostering AI capabilities in demand planning. Our study extends the literature on AI capability in several ways. First, we identify the resources that are important in the formation of the capacity to implement AI in the context of demand planning. Conclusions: This study's practical contributions underscore the multifaceted nature of AI implementation for demand planning, emphasizing the importance of resource allocation, human capital development, collaborative relationships, organizational alignment, and relational capital and AI. [ABSTRACT FROM AUTHOR]

There is a dearth of rigorous empirical studies that directly examine the contextual variables that drive sales and operations processes in entrepreneurial firms operating in the apparel value chain. This study aimed to identify and understand the influential variables that impact S&OP efficiency in apparel firms in sub-Saharan Africa. The study adopted a cross-sectional quantitative approach utilizing a structured questionnaire to collect data from 398 S&OP experts working in entrepreneurial firms operating along apparel value chains in six countries in the Sub-Saharan Africa region. The findings revealed moderate, positive, and statistically significant correlations between S&OP effectiveness and various factors, including organizational culture and personnel. Technological readiness, organizational alignment, demand planning practices, performance metrics, time/planning horizons, and data integrity. However, factors such as the S&OP structure, supply chain planning practices, organizational systems, and S&OP governance showed statistically insignificant correlations with S&OP effectiveness. This finding suggests that their influence may be less pertinent in this context. The study concludes that enhancing S&OP effectiveness in firms within sub-Saharan Africa's apparel industry can be achieved by dedicating resources to improve variables that have a significant impact. This study underlines the necessity for companies to understand locally relevant factors rather than relying on a standardized approach to S&OP implementation. This study contributes to the literature by shedding light on S&OP determinants within this under-researched context and suggests areas for further research across different industries and markets

Demand planning is a significant factor that affects the performance of each stage of supply chain management in fast-moving consumer goods. An unavoidable problem is the inaccuracy of demand planning which has an impact on production planning for the procurement of personal care products. The more products, the more the accuracy of demand forecasting varies, so it is necessary to make efforts to improve the quality of demand planning to have an impact on effective, efficient demand planning and increase customer satisfaction. This research is a mixed method with a concurrent embedded strategy by providing a simulated rolling forecast process (to be) of the current rolling forecast process (as is). The analysis uses the method of rolling forecast, double moving average, double exponential smoothing, and forecast error calculation with Mean Absolute Percentage Error (MAPE). The purposive sampling method is used to calculate demand forecasting for 2022 focusing on product categories with the 80/20 Pareto principle, where 20% of products generate 80% of the company's revenue. This research improves the business process by implementing a planning time horizon that is dissected into 3 months consisting of frozen, slushy, and liquid periods in a rolling forecast and setting safety stock to be obeyed by distributors to get the smallest forecast error rate. The appropriate demand forecasting method for forecasting product demand based on test results shows that each product requires a different forecasting method, depending on the characteristics of each product's historical data. [ABSTRACT FROM AUTHOR]

Leading nations have re-emphasised the importance of semiconductor manufacturing to supply chain safety and resilience. Limitations of the existing studies can be traced in part to the lack of a framework within which the interrelated decisions for corporate strategic planning can be coordinated while integrating the associated actions. This research aims to review related studies involved in the decision elements of the proposed Pricing-Demand-Capacity-Capital Expenditure-Cost-Return (PDCCCR) framework and discuss the influence relationships among pricing, demand planning, capacity portfolio and cost structure for the overall financial return to derive the implications for smart manufacturing. Thus, a decision framework is proposed to integrate collaborative decisions for international interests and practical implications. As computational capability is increasing, the interrelated strategic decisions for pricing, demand planning, capacity portfolio and cost structure can be considered simultaneously via the proposed framework at the digital decision platform to coordinate flexible decisions for an agile and sustainable supply chain. [ABSTRACT FROM AUTHOR]

Background: Technological advancements, particularly in Artificial Intelligence (AI), are revolutionizing operations management, especially in the domain of supply chain management. This paper delves into the application of AI in demand planning processes within the supply chain context. Drawing upon a comprehensive review of the existing literature, the main objective of this study is to analyze how AI is being applied and adopted in the demand planning process, identifying the resources needed to build the capacity of AI in the demand process, as well as the mechanisms and practices contributing to AI capability’s advancement and formation. Methodology: The approach was qualitative, and case studies of three different companies were conducted. Results: This study identified crucial resources necessary for fostering AI capabilities in demand planning. Our study extends the literature on AI capability in several ways. First, we identify the resources that are important in the formation of the capacity to implement AI in the context of demand planning. Conclusions: This study’s practical contributions underscore the multifaceted nature of AI implementation for demand planning, emphasizing the importance of resource allocation, human capital development, collaborative relationships, organizational alignment, and relational capital and AI.

Our 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. Highlights: Human judgment is an essential element of demand forecasting but requires integration into the forecasting process.Giving people too much influence may introduce more noise than signal. Our research examines different ways of integrating human judgment into a forecasting process and shows that an effortless way of doing so‐‐by allowing people to indicate that a special event is affecting a forecast and an algorithm to estimate the impact of that event‐‐performs remarkably well in comparison to other methods. [ABSTRACT FROM AUTHOR]

• Metaheuristic Multi-Start algorithm for the demand consumption planning. • Determining the optimum PV installation sizing. • Nearly Zero Energy Building using photovoltaic energy. 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 %. [ABSTRACT FROM AUTHOR]

Combining independent judgmental and statistical forecasts is one of the prevalent methods to increase forecast accuracy in the supply chain. Several strategies for combining them have been proposed in the literature. However, little guidance is available on selecting the proper strategy given various forecasting conditions. This study investigated the performance of four forecast combination strategies, namely (i) judgmental adjustment, (ii) statistical correction, (iii) mechanical combination, and (iv) judgmental input, under four product-market configurations as in Ansoff's matrix. We used archival data from a global pharmaceutical company as the basis for evaluation. The result suggests that combining independent statistical and judgmental forecasts can be beneficial for organizations. However, not all combinations strategies are efficient across the product-market configurations. Amongst the strategies, only the judgmental input and mechanical combination strategies can give consistent accuracy improvement over the baseline. In contrast, judgmental adjustment and statistical correction strategies only work in the environment where the forecasters have high experience with the products and markets. The empirical findings from the study provide some managerial implications for operational forecasting. [ABSTRACT FROM AUTHOR]

Demand planning related to making purchases of SKUs to maintain the SLA given by the company's strategy and thus avoid stock breaks has an important role in the operation of the supply chain and the company's operation. Demand forecasts based on qualitative methods and manual methods based on historical data obtained impact on production planning, consequently, the fulfillment of the products the customer requires. The objective of defining and implementing a purchasing recommender has been raised based on the machine learning model that more effectively adapts to variations in demand for products classified under an ABC model. Scikit-learn libraries are used to implement demand prediction models trained with historical product information. The result is a proposed prediction model with a better confidence level than the company's current prediction model. [ABSTRACT FROM AUTHOR]

This paper presents a digital material planning approach, utilizing simulation-based optimization to select and parametrize article specific demand forecasting methods. Demand forecasts are the basis of material requirements planning in consumption-based material planning, and are an essential lever for efficient inventory and order calculation. Despite their acknowledged potential, digital tools for optimized demand calculation are still lacking in practice. Thus, the goal of the presented approach to provide an application-oriented method to optimally select and parametrize state-of-the-art forecasting methods, based on product-specific demand data. In this approach, a rule-based selection heuristic is combined with static simulation of demand time-series and a metaheuristics-based optimization of forecasting parameters, to provide automatically optimized article-specific demand forecasts. Case studies for two companies in the capital goods industry evaluate and quantify the application potential. The results point to significantly improved, item-specific demand planning.

Demand planning is a supply chain management planning that enables an organization to forecast the future demand for its products or services to be produced efficiently and delivered effectively to the satisfaction of its customers. And supply planning is the management of the inventory supply to meet the demand forecast targets macro, industry and firm levels during distribution, manufacturing, and procurement operations within the capacity constraints and material availability of the organization. Therefore, the key performing indices (KPIs) are designed to provide up-to-date market intelligence about activities that are critical to planning, particularly in gauging the mean absolute percentage error results of demand planning. This study attempts to establish the extent of key job scopes in demand planner and how true the subject matter experts in demand planning function follow the job scopes as defined. The qualitative design approach, limiting the population to selected countries by choice, employs three categories of data sources: themes synthesizing, semi-structured interview and three ways of LinkedIn profiles review. The triangulated interview findings, confirming the key job scopes practiced by demand planners in the organizations, show the overlapping of demand planner job scopes by a mixture of other roles other than that of the demand planning role. In conclusion, 227 LinkedIn profiles of subject matter experts from 30 countries were found having the demand planning working experience mixed with supply planning or other supply chain functions. [ABSTRACT FROM AUTHOR]

This paper presents a digital material planning approach, utilizing simulation-based optimization to select and parametrize article specific demand forecasting methods. Demand forecasts are the basis of material requirements planning in consumption-based material planning, and are an essential lever for efficient inventory and order calculation. Despite their acknowledged potential, digital tools for optimized demand calculation are still lacking in practice. Thus, the goal of the presented approach to provide an application-oriented method to optimally select and parametrize state-of-the-art forecasting methods, based on product-specific demand data. In this approach, a rule-based selection heuristic is combined with static simulation of demand time-series and a metaheuristics-based optimization of forecasting parameters, to provide automatically optimized article-specific demand forecasts. Case studies for two companies in the capital goods industry evaluate and quantify the application potential. The results point to significantly improved, item-specific demand planning. [ABSTRACT FROM AUTHOR]

Demand planning enables organizations to manage their and their partner supply chain processes. We introduce and evaluate the demand planning process, including the aspects of goal formulation, forecasting techniques, information source, forecasting and decision making, and synchronizing supply with demand. We, especially, show its applicability and evaluation in a Vietnamese coffee supply chain context, which also provides insights for general supply chain and organizational practices, especially in emerging economies. We find that the demand planning relates strongly to cost and delivery performance. There is greater evidence that demand planning is not only valuable but has multiple important dimensions that managers and organizations need to be concerned within its implementation. We provide some practical insights into the design of demand planning as well as issues that organizations may face. [ABSTRACT FROM AUTHOR]

Copyright of AStA Wirtschafts- und Sozialstatistisches Archiv is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The apparel industry has become highly competitive and complex as consumers’ demands vary, thus pressuring organisations in this sector to invest in better demand forecasting and planning tools. Apparel supply chains are affected by both external and internal factors which impact on the performance of organisations in the market. These dynamics have become challenges in the forecasting and planning of demand in apparel supply chains. This study explores demand forecasting and planning factors influencing the apparel industry. The significance of this study lies in the importance of identifying the factors that influence planning for future demand in order to accurately estimate supply quantities required to meet consumer needs. The study contributes to the existing body of knowledge as it provides insight into the various factors that influence demand planning for a prominent apparel retailer within the South African industry. The study is exploratory and descriptive. Thematic analysis was used to analyse the data. The findings reveal that there are factors that influence how demand forecasting and planning practices are conducted in the apparel industry. These include competition, economic issues, weather, system issues, poor internal collaboration, supplier issues and social media. It is important that organisations in the apparel industry should take these factors into consideration when planning for demand to ensure consumer needs can be met, thus improving the performance of the apparel industry.

Copyright of Inventum. Ingenieria, Tecnologia e Investigacion is the property of Corporacion Universitaria Minuto de Dios and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A configurable product is defined as a combination of predefined product attributes. Identifying the most promising variants of such products in terms of long-term profitability and customer satisfaction has become a central issue for their manufacturers. In theory, configurable products that have numerous attributes with too many levels can be created in millions of different ways. Hence, properly reducing the number of product attributes to be considered in an operational model (e.g., demand forecasting model, inventory model) is therefore worth the effort. This study intends to demonstrate how easier and more accurate predictions can be made with the incorporation of qualitative methods into traditional approaches, which may cause operational models suffer from the "curse of dimensionality." Using customer reviews for a configurable product, the present study discusses how to support the demand planning process by reducing the number of product attributes without losing too much critical information. Various text mining techniques were thus employed, and the results obtained are then compared with those from another study that modeled the demand of the configurable product in consideration using quantitative methods. [ABSTRACT FROM AUTHOR]

Copyright of Revista Ingeniare is the property of Universidad Libre Seccional Barranquilla and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Organizations that efficiently complete a demand planning process gain a competitive advantage. The current demand planning literature mostly studies relatively large organizations. However, SMEs invest a significant amount of resources into improving demand planning practices. Moreover, the context of SMEs in Latin America, and specifically Mexico, has not been explored sufficiently by the current literature. Thus, the purpose of this paper is to understand demand planning in a Mexican SME. The paper involves a case study of a confectionery firm based in Jalisco, Mexico. To make sense of the empirical findings, the Kilger and Wagner (2008) theoretical framework of demand planning was utilized. This paper concludes that Mexican SMEs have a different context compared to larger firms in developed countries. Thus, how Mexican SMEs envision and implement demand planning is unique, specifically in demand planning structures and controlling. [ABSTRACT FROM AUTHOR]

Results of demand planning serve as the basis of every planning activity in a demand-supply network and ultimately determine the effectiveness of manufacturing and logistic planning, such as capacity and safety stock planning, in the network. The uncertainty of demand signals that are propagated and magnified over the network becomes the crucial cause of ineffective operation plans. With the globalization of demand-supply networks and the desire for a more integrated operation plan, demand planning is now one of the greatest challenges facing manufacturers. To manage the demand variability, appropriate demand aggregation and statistical forecasting approaches are known to be effective. This paper will use the bivariate VAR(1) time-series model as a study vehicle to investigate the effects of aggregating two interrelated demands. It is shown that the aggregated time series of two VAR(1) times series is equivalent to the sum of two AR(1) time series. Through theoretical development, the paper further explores the properties of the aggregated time series and provides guidelines for practitioners to determine proper aggregation and forecasting approaches. An important finding of this research is that demand aggregation is far more effective than statistical forecasting in operations planning for any two demands with low positive correlation or negative correlation. [ABSTRACT FROM AUTHOR]