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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]

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]

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]

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]

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.

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Purpose The purpose of this paper is to develop and empirically validate a model for assessing demand planning maturity in organisations.Design/methodology/approach The authors developed a maturity assessment model for demand planning through iterations of theoretical and empirical work, combining insights from literature and practitioners. An online survey is developed to validate the model using data from different industries.Findings The authors identify six dimensions of demand planning maturity: data management, the use of forecasting methods, the forecasting system, performance management, the organisation and people management. The empirical study indicates that demand data are well managed and organisation readiness is high, yet improvements in the forecasting system and the management of forecast performance are needed. The results show a positive relationship between the size of an organisation and its demand planning maturity.Practical implications The contribution of this work is to propose an assessment model and survey instrument for demand planning maturity. This will help the practitioner to understand the current level of maturity of the demand planning process, reflect on the desired level and develop action plans to close the gap.Originality/value There is broad literature on process maturity assessment in general and on sales and operations planning (S&OP) maturity in particular. However, there is no comprehensive model for assessing the maturity of demand planning, which is a specific and critical process within the overall S&OP process. The authors fill this gap by offering a demand planning maturity model. [ABSTRACT FROM AUTHOR]

The demand planning is one of the most frequented topics in logistics planning within the supply chain management. The prediction of future demand is a very important phase in the enterprise resource planning in point of view of supporting other planning functions (e.g. production planning, material requirements planning etc.). The statistical forecasting is the first step in this very complex process. The effectiveness of the traditional forecasting methods is very variable in the real economic environment. The dyadic harmonic analysis is a new stream and alternative solution to recognize and understand the behavior of the economical time series. [ABSTRACT FROM AUTHOR]

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Supply management in enterprises is a vital strategy for productivity and market competitiveness. Logistics and supply chains should result in competitive advantages from the strategy. Supply chain management presents a starting point which is unavoidable if we are to create value, the anticipation and the anticipation comes first. The paper presents results of conclusive studies conducted by state where the need is strong evidence of a planning application in Colombian businesses. There is evidence that demand planning Colombian companies is necessary. Additionally, it presents an overview of the most important models used to manage demand and use forecasts for it. The purpose is to study how the models should be chosen depending on the behavior analyzed time series and the dynamics of different markets. [ABSTRACT FROM AUTHOR]

This article presents an optimisation-based multi-agent decision support platform (DSOPP) for integrated order promising and production planning in a multi-enterprise supply network environment. The DSOPP platform may be run either in real time or in simulation mode. Its goal is to demonstrate the viability of collaborative decision making. The DSOPP framework is built around a scalable multi-period optimisation model that may be used across enterprises. We first discuss the optimisation model at the core of the planning function called the 'advanced order planning' model. We then present the DSOPP platform architecture, which consists of a distributed network control centre (DNCC) and individual supply chain control centres. Intelligent agents are embedded in both types of centres. The role of the DNCC is to coordinate the planning cycle clock and facilitate the transfer of information and inventory through a central mailbox mechanism. An individual supply chain centre comprises agents for demand management, planning and production execution. Results from an experimental case indicate that the ideas presented in this article can be used effectively to coordinate the planning activities across supply chain networks by providing the ability to analyse and understand the effects of various supply chain parameters. [ABSTRACT FROM AUTHOR]

The article explains the logistics strategies applied by PYMES the manufacturing sector of the Municipality of Itagüí in providing inputs and resources for input and output operations in regional markets, national and international in order to plan strategies and alternatives applicable to local realities in the sector. In conducting the analysis from the SCM (Supply Chian Management), assessing the problems in the supply chain and even within the production unit must be concluded that address four strategic measures: Inventory Management, SOP (Sales and Operations Planning); Demand Planning and Certified Delivery to improve competitiveness and productivity of the sector. [ABSTRACT FROM AUTHOR]