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The purpose of this research paper is to offer a solution to differentiate supply chain planning for products with different demand features and in different life‐cycle phases.

A normative framework for selecting a planning approach was developed based on a literature review of supply chain differentiation and supply chain planning. Explorative mini‐cases from three companies – Vaisala, Mattel, Inc. and Zara – were investigated to identify the features of their innovative planning solutions. The selection framework was applied to the case company's new business unit dealing with a product portfolio of highly innovative products as well as commodity items.

The need for planning differentiation is essential for companies with large product portfolios operating in volatile markets. The complexity of market, channel and supply networks makes supply chain planning more intricate. The case company provides an example of using the framework for rough segmentation to differentiate planning.

The paper widens Fisher's supply chain selection framework to consider the aspects of planning.

Despite substantial resources being used, planning results are often not reliable or consistent enough to ensure cost efficiency and adequate customer service. Therefore there is a need for management to critically consider current planning solutions.

The procedure outlined in this paper is a first illustrative example of the type of processes needed to monitor and select the right planning approach.

Planning processes along a demand supply network in an environment characterized by rapid market fluctuations and product changes are studied. The relationship between demand planning and the bullwhip effect is investigated by comparing planning accuracy in different demand supply network echelons and locating where there is most nervousness.

The current demand supply planning process flow was described based on interviews with key decision‐makers throughout the demand‐supply network from retailers to second tier suppliers. A data analysis of the quality of plans for demand and supply was generated in each decision‐making point by collecting planning and actual data of two products.

The results show that planning accuracy varies between the parties in the supply chain. The connection between planning nervousness and the bullwhip was investigated in detail through a vendor‐managed inventory (VMI) model in the chain. Planning nervousness causes bullwhip, as the changes in demand were amplified in the used information sharing process in VMI. In product introduction phase, the phenomenon was emphasized.

To stabilize and simplify planning the process should be differentiated according to product life‐cycle phases. One proposal is to improve communication practices with suppliers, especially to stabilize demand information sharing with VMI‐suppliers.

The structure of the electronics supply chain makes planning processes challenging. In this research we were able to follow the data flow and planning process throughout the supply chain, which is not often the case.

The purpose of this paper is to explore and propose how product-in-use data can be used in, and improve the performance of, the demand planning process for automotive aftermarket services.

A literature review and a single case study investigate the underlying reasons for the demand for spare parts by conducting in-depth interviews, observing actual demand-generating activities, and studying the demand planning process.

This study identifies the relevant product-in-use data and divides them into five main categories. The authors have analysed how product-in-use data are best utilised in planning spare parts with different attributes, e.g. different life cycle phases and demand frequencies. Furthermore, the authors identify eight potentially relevant areas of application of product-in-use data in the demand planning process, and elaborate on their performance effects.

This study details the understanding of what impact context has on the potential performance effects of using product-in-use data in aftermarket demand planning. Propositions generate several strands for future research.

This study shows the potential impact of using product-in-use data, using eight different types of interventions for spare parts, in the aftermarket demand planning.

The literature focusses on single applications of product-in-use data, but would benefit from considering the context of application. This study presents interventions and explores how these enable improved demand planning by analysing usage and effects.

The purpose of this paper is to describe how demand planning can increase agility in supply chains. The paper builds on a case study from mobile infrastructure industry with explicit focus on project business environment.

The paper contains a short theoretical review on supply chain agility, different planning and forecasting concepts and explores the linkages between them. Empiric evidence is collected from Nokia Networks as a case study. Main lessons are primarily taken from integrated project management program that is to implement a truly customer‐focused delivery process in the case company.

Suppliers should pay more attention on effectively utilizing customer's project plans for aligning their supply chain. Supply chain agility does not just happen but requires continuous planning.

Common project planning is the most natural way for customers to share future demand information between the supply chain players. Instead separate and often laborious demand forecasting process, suppliers should utilize customer's project plans in building agility in their supply chains.

Focuses on the importance of the ability to adapt to rapid and unexpected changes and asserts that a continuous, customer driven planning process is a pre‐requirement for being agile in supply chains.

The goal of the paper is twofold. First, it aims to empirically conceptualize whether a wide array of fragmented demand planning activities, performed in supply chains, can be logically categorized into actionable sets of practices, which then form a broader conceptualization of the demand planning process. Second, regarding certain contextual factors, our research seeks to investigate the contribution of demand planning, as a higher-order construct, to mitigating disruptions induced by operational risks in supply chains.

In this study, PLS-SEM was used to estimate the reflective-formative nature of the model. The results of PLS-SEM were additionally complemented by the assessment of the predictive power of our model. Finally, to reveal possible contingency effects, the multigroup analysis (MGA) was conducted.

The study suggests that demand planning process (DPP) is a second-order construct that is composed of four sets of practices, including goal setting, data gathering, demand forecasting, communicating the demand predictions and synchronizing supply with demand. The study also reveals that the demand planning practices, only when considered together, as a higher-order factor, significantly contribute to mitigating disruptions driven by operational risks. Finally, the research shows that the strength of the impact of demand planning on disruptions is contextually dependent.

While the study makes some important contributions, the obtained findings ought to be considered within the context of limitations. First, the study only investigates disruptions driven by operational risks, ignoring the negative consequences of environmental risks (terrorist attacks, natural disasters, etc.), which may have a far more negative impact on supply chains. Second, the sample is mostly composed of medium and large companies, not necessarily representative of demand planning performed by the entire spectrum of companies operating in the market.

The study shows that to effectively mitigate disruptions induced by operational risks, the demand planning practices should be integrated into a higher-order construct. Likewise, our research demonstrates that the intensity of demand planning process is contingent upon a number of contextual factors, including firm size, demand variability and demand volume.

The study indicates that to mitigate disruptions of operational risk, demand planning as a higher-order dynamic capability can be referred to the concept of organizational learning, which contributes to forming a critical common ground, ensuring the balance between formal and informal dynamic routines.

The paper depicts that to fully deal with disruptions, the demand planning practices need to be integrated and categorized into the dedicated higher-order. This may lead to forming demand planning as a higher-order dynamic capability that provides a more rapid and efficient rebuttal to any disruptions triggered by operational risks.

The purpose of this paper is to explore the effects of demand planning practices on the disruptions induced by operational risk. The study reveals whether the negative consequences of operational risk factors (covering demand, supply, control and process risks) can be absorbed or amplified through the application of specific demand planning practices in supply chains.

The study involves the partial least squares path model procedure. Likewise, the items of the constructs in the outer model were subjected to a purification process by principal component analysis with the orthogonal (varimax) and oblique (Promax) methods of rotation.

The findings suggest that although one may not observe uniformity and standardization in the role of demand planning in alleviating the negative effects of operational risks, still some regularities can be obtained. Having said that some demand planning practices tend to mitigate or reinforce disruptions driven by operational risk, whereas the other practices simultaneously absorb and amplify disruptions driven by operational risk.

The study shows that different managerial instruments, which are not inherently dedicated to risk management, when appropriately applied, may have an indirect impact on the mitigation of supply chain risk. In particular, the concept of demand planning might be very helpful for managers when dealing with demand and control risks.

The study simultaneously examines a more detailed bundle of practices forming the demand planning process. The research attempts to investigate the link between the demand planning process and operational risk consequences, derived from all sources (supply, demand, process and control). The paper shows that risk management is not a sole tool to mitigate disruptions. Among the concepts, which contribute to decrease risks is the demand planning process. The study demonstrates that the demand planning process when applied as a component of supply chain management, may contribute to mitigate certain operational risks.

The purpose of this paper is to develop and empirically validate a model for assessing demand planning maturity in organisations.

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.

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.

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.

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.

The purpose of this paper is to explore tactical planning in grocery retailing and propose how process and integration mechanisms from sales and operations planning (S&OP) can enhance retail tactical planning.

This work follows an explorative design with case studies from the grocery retailing industry in Finland, Norway, and the UK.

The tactical planning process focuses on demand management and securing product availability from suppliers in order to reach sales targets. Less attention is directed toward balancing supply and demand or toward providing a single plan to guide company operations. Planning appeared to be functionally oriented with limited coordination between functional plans, but it did include external integration that improved forecast accuracy.

The study involves grocery retailer cases with variable levels of S&OP maturity. The propositions need to be investigated further through action research or additional case studies to confirm their validity.

The study proposes a design of an S&OP process in retailing and propositions for improving tactical planning integration.

The study complements research on retail tactical planning by taking planning process and integration viewpoints. The research suggests that retailers would benefit from a formal and company-wide S&OP process to unify different market-oriented plans to a single set of numbers, thus better balancing supply and demand without sacrificing the emphasis on demand planning.

Because increasing product variety in retail conflicts with limited shelf space, managing assortment and shelf quantities is a core decision in this sector. A retailer needs to define the assortment size and then assign shelf space to meet consumer demand. However, the current literature lacks not only information on the comprehensive structure of the decision problem, but also a decision support system that can be directly applied to practice in a straightforward manner. The paper aims to discuss these issues.

The findings were developed and evaluated by means of explorative interviews with grocery retail experts. An optimization model is proposed to solve the problem of assortment planning with limited shelf space for data sets of a size relevant in real retail practice.

The author identifies the underlying planning problems based on a qualitative survey of retailers and relates the problems to each other. This paper develops a pragmatic approach to the capacitated assortment problem with stochastic demand and substitution effects. The numerical examples reveal that substitution demand has a significant impact on total profit and solution structure.

The author shows that the model and solution approach are scalable to problem sizes relevant in practice. Furthermore, the planning architecture structures the related planning questions and forms a foundation for further research on decision support systems.

The planning framework structures the associated decision problems in assortment planning. An efficient solution approach for assortment planning is proposed.

The production planning problem with fine-grained information has hardly been considered in practice. The purpose of this study is to investigate the data-driven production planning problem when a manufacturer can observe historical demand data with high-dimensional mixed-frequency features, which provides fine-grained information.

In this study, a two-step data-driven optimization model is proposed to examine production planning with the exploitation of mixed-frequency demand data is proposed. First, an Unrestricted MIxed DAta Sampling approach is proposed, which imposes Group LASSO Penalty (GP-U-MIDAS). The use of high frequency of massive demand information is analytically justified to significantly improve the predictive ability without sacrificing goodness-of-fit. Then, integrated with the GP-U-MIDAS approach, the authors develop a multiperiod production planning model with a rolling cycle. The performance is evaluated by forecasting outcomes, production planning decisions, service levels and total cost.

Numerical results show that the key variables influencing market demand can be completely recognized through the GP-U-MIDAS approach; in particular, the selected accuracy of crucial features exceeds 92%. Furthermore, the proposed approach performs well regarding both in-sample fitting and out-of-sample forecasting throughout most of the horizons. Taking the total cost and service level obtained under the actual demand as the benchmark, the mean values of both the service level and total cost differences are reduced. The mean deviations of the service level and total cost are reduced to less than 2.4%. This indicates that when faced with fluctuating demand, the manufacturer can adopt the proposed model to effectively manage total costs and experience an enhanced service level.

Compared with previous studies, the authors develop a two-step data-driven optimization model by directly incorporating a potentially large number of features; the model can help manufacturers effectively identify the key features of market demand, improve the accuracy of demand estimations and make informed production decisions. Moreover, demand forecasting and optimal production decisions behave robustly with shifting demand and different cost structures, which can provide manufacturers an excellent method for solving production planning problems under demand uncertainty.