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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 article is to propose a planning process that takes into account that manufacturers of original equipment have products at different stages of the product‐life cycle, and utilizes sales and inventory information collected from distributors and retailers.

The research paper describes the construction and testing of a planning process.

Trials in a case company indicate that supply chain responsiveness can be improved in product launches using the proposed process. Supply chain efficiency in the maturity phase can also be improved.

The usefulness and effectiveness of the proposed process depend on the assumption that product mix changes can be modeled and point‐of‐sales and channel sell‐through data are available regularly and reliably.

Modeling and monitoring the variant mix on the total market level can be used to improve supply chain responsiveness to mix changes in product launches. The introduction of this planning process reduces the need for planning in the sales units.

The paper shows how the quality of variant forecasting for an original equipment manufacturer can be improved with access to channel visibility in the market introduction phase.

Using AI to strengthen creativity and problem-solving capabilities of professionals involved in innovation management holds huge potential for improving organizational decision-making. However, there is a lack of research on the use of AI technologies by innovation managers. The study uses the theory of appropriation to explore how specific factors – agile leadership (AL), innovation orientation (IO) and individual creativity (IC) – impact innovation managers' use of generative AI tools, such as ChatGPT (CGA).

The research model is tested through a large-scale survey of 222 Italian innovation managers. Data have been analyzed using structural equation modeling following a two-step approach. First, the measurement model was assessed to ensure the constructs reliability. Subsequently, the structural model was analyzed to draw the conclusions on theorized model relationships and their statistical significance.

The research findings reveal positive associations between IO and IC with CGA, demonstrating that innovation managers who exhibit strong innovation orientations and higher Individual Creativity are more likely to adopt and personalize ChatGPT. However, the study did not confirm a significant association between AL and CGA.

Our findings have important implications for organizations seeking to maximize the potential of generative AI in innovation management. Understanding the factors that drive the adoption and customization of generative AI tools can inform strategies for better integration into the innovation process, thereby leading to enhanced innovation outcomes and improved decision-making processes.