This study examined the reciprocal influence of demand learning and preference matching in the context of store brand customization. The demand-learning effect refers to the…
Abstract
Purpose
This study examined the reciprocal influence of demand learning and preference matching in the context of store brand customization. The demand-learning effect refers to the collection of market demand information through production, based on pre-order demands, enabling retailers to accurately predict and allocate product quantities, thus improving inventory management. The preference-matching effect involves engaging consumers in the production and design processes of store brands to align fully with their preferences, thereby increasing the purchase impact of store brand products and promoting consumption.
Design/methodology/approach
We employ game-theoretic models to analyze a two-echelon supply chain consisting of a manufacturer and a retailer. The retailer offers both national brands, manufactured by the supplier and in-house store brands. To enhance their competitive edge, the retailer can adopt a customized strategy targeting the store brand to attract a wider consumer base.
Findings
The analysis reveals that, under low commission fees, the manufacturer consistently opts for high production quantities, irrespective of the level of demand uncertainty. However, when the perceived value of a store brand is low and demand uncertainty is either low or high, the retailer should choose a minimal or zero production quantity. The decision-making process is influenced by the customization process, wherein the effects of demand learning and preference matching occasionally mutually reinforce each other. Specifically, when the perceived value of a store brand is low, or the product cost is high, along with high customization costs, the interplay between demand learning and preference matching becomes mutually inhibiting. Consequently, the significance of store brand customization diminishes.
Originality/value
This study enhances the current body of knowledge by providing a deeper understanding of the theoretical value of store brand customization. In addition, it offers valuable decision-making support to enterprises by assisting them in selecting appropriate inventory and customization strategies.
Details
Keywords
Sijie Tong, Qingchen Liu, Qichao Ma and Jiahu Qin
This paper aims to address the safety concerns of path-planning algorithms in dynamic obstacle warehouse environments. It proposes a method that uses improved artificial potential…
Abstract
Purpose
This paper aims to address the safety concerns of path-planning algorithms in dynamic obstacle warehouse environments. It proposes a method that uses improved artificial potential fields (IAPF) as expert knowledge for an improved deep deterministic policy gradient (IDDPG) and designs a hierarchical strategy for robots through obstacle detection methods.
Design/methodology/approach
The IAPF algorithm is used as the expert experience of reinforcement learning (RL) to reduce the useless exploration in the early stage of RL training. A strategy-switching mechanism is introduced during training to adapt to various scenarios and overcome challenges related to sparse rewards. Sensor inputs, including light detection and ranging data, are integrated to detect obstacles around waypoints, guiding the robot toward the target point.
Findings
Simulation experiments demonstrate that the integrated use of IDDPG and the IAPF method significantly enhances the safety and training efficiency of path planning for mobile robots.
Originality/value
This method enhances safety by applying safety domain judgment rules to improve APF’s security and designing an obstacle detection method for better danger anticipation. It also boosts training efficiency through using IAPF as expert experience for DDPG and the classification storage and sampling design for the RL experience pool. Additionally, adjustments to the actor network’s update frequency expedite convergence.
Details
Keywords
This chapter critically evaluates whether football can attain recognition as a national sport in China. Article No. 11, released by the Chinese government in 2015, aimed to…
Abstract
This chapter critically evaluates whether football can attain recognition as a national sport in China. Article No. 11, released by the Chinese government in 2015, aimed to develop a new national strategy centralised on the sport of football to foster consumption and enhance national soft power. Consequently, this also means encouraging Chinese football fans to support the national football team. Comparing the significance of local football clubs and the national football team to Chinese football fans is deemed meaningless and unable to generate useful information to comprehend Chinese people's attitudes towards local and national communities. Through literature comparisons with established Chinese national sports such as Chinese martial arts, badminton and table tennis, the discussion reveals that football currently falls short of meeting the general criteria of invention and popularity to be considered a Chinese national sport. In the specific Chinese context, it also proves that football fails to meet the criterion of politics, hindering its identification as a national sport. Consequently, the chapter rebuts the assumption and advocates for the validity of comparing how fans assess their fandom for local and national football teams.