Impacts of adaptive collaboration on demand forecasting accuracy of different product categories throughout the product life cycle

Abstract

Purpose – This paper aims to empirically analyze how adaptive collaboration in supply chain management impacts demand forecast accuracy in short life-cycle products, depending on collaboration intensity, product life-cycle stage, retailer type and product category. Design/methodology/approach – The authors assembled a data set of forecasts and sales of 169 still-camera models, made by the same manufacturer and sold by three different retailers in France over five years. Collaboration intensity, coded by collaborative planning forecasting and replenishment level, was used to analyze the main effects and specific interaction effects of all variables using ANOVA and ordered feature evaluation analysis (OFEA). Findings – The findings lend empirical support to the long-standing assumption that supply chain collaboration intensity increases demand forecast accuracy and that product maturation also increases forecast accuracy even in short life-cycle products. Furthermore, the findings show that it is particularly the lack of collaboration that causes negative effects on forecast accuracy, while positive interaction effects are only found for life cycle stage and product category. Practical implications – Investment in adaptive supply chain collaboration is shown to increase demand forecast accuracy. However, the choice of collaboration intensity should account for life cycle stage, retailer type and product category. Originality/value – This paper provides empirical support for the adaptive collaboration concept, exploring not only the actual benefits but also the way it is achieved in the context of innovative products with short life cycles. The authors used a real-world data set and pushed its statistical analysis to a new level of detail using OFEA.

<b>The importance of trust and collaboration between companies to mitigate the bullwhip effect in supply chain management

“…Bray & Mendelson (2012), Trapero, Korentzes, & Fildes (2012), Li (2013), Sari (2015), Barkataki & Zeineddine (2015) 2. Improved demand forecasting Wright & Yuan (2008), Ali, Boylan, & Syntetos (2012), Trapero et al (2012), Dong et al (2014), Sari (2015), Xu, Dong, &Xia (2015), Nagashima et al (2015) 3. Replenishment policy Lee & Wu (2006), Jaksic & Rusjan (2008), Su & Wong (2008), Kelepouris, Miliots, & Pramatari (2008), Dong et al(2014) VMI Disney & Towill (2003) Relationships between trust, collaboration, and BWE Some studies consider trust as a defining characteristic of the presence of collaboration among members of a SC (Vieira et al, 2009).…”

“…The state-of-art of the literature on Supply Chain (SC) shows that trust and collaboration influences the integration of knowledge and processes in a SC (Manu, Ankrah, Chinyio, & Proverbs, 2015;Capaldo & Giannoccaro, 2015;Revilla & Knoppen, 2015;Pomponi, Fratocchi, & Tafuri, 2015); and that trust and collaboration in supply chain management (SCM) is one of the most effective ways to reduce the bullwhip effect (BWE) (Sari, 2015;Nagashima, Wehrle, Kerbache, & Lassagne, 2015;Cao, Baker, & Schniederjans, 2014, Dominguez, Cannella & Framinan, 2014 The findings of Revilla and Knoppen (2015) support a relationship between knowledge integration and performance, after they have examined the key antecedents that might facilitate knowledge integration: strategic supply management and trust. According to Dong, Huang, Sinha, & Xu (2014), central to Collaborative Planning, Forecasting, and Replenishment (CPFR) is Collaborative Demand Forecasting (CDF) that allows SC partners to share private demand information, and incorporate the jointly derived demand forecast into production planning and product replenishment decisions.…”

Análises E Perspectivas De Estudos Empíricos Em Collaborative Planning, Forecasting and Replenishment (Cpfr)

“…A principal razão para essa lenta aceitação é o reconhecimento geral de que o sucesso da implementação do CPFR não é uma tarefa trivial e que sua prática inflige mudanças significantes na maneira "usual" de fazer negócios (Panahifar et al, 2015b). Embora conceitualmente simples, as implementações do CPFR são complexas na prática, pois exigem trocas de grande quantidade de dados, levando um longo tempo para se consolidar, além de envolver diversas áreas funcionais de vá-rias empresas (Nagashima et al, 2015). O CPFR possui complexidade na implementação por envolver fatores técnicos e fatores não técnicos e, ainda, por ser um processo em que a implementação passa por vários ciclos de funcionamento até estabilizar o sistema (Gomes e Kliemann Neto, 2015;Panahifar et al, 2015a).…”

“…Muitos estudos se concentram em apresentar o método de forma analiticamente ou por esquemas, poucos estudos forneceram validações empíricas dos resultados analíticos (Yao et al, 2013;Nagashima et al, 2015). Além disso, a obtenção de dados relacionados a cadeias de suprimentos é particularmente difícil, o que torna ainda mais árduo a ocorrência de estudos empíricos relacionados ao tema (Nagashima et al, 2015). …”

Tactics at the category level of purchasing and supply management: sourcing levers, contingencies and performance

“…The category management concept developed in a retail context, where similar consumer goods were grouped for joint marketing, product offerings and the optimisation of the overall product portfolio, crosssales and shelf management (Leeflang and Parreño-Selva, 2012;Lindblom et al, 2009;Nagashima et al, 2015;O'Keeffe and Fearne, 2002). In a purchasing context, category management mainly builds on portfolio models that place purchased goods into a limited number of four or nine portfolio cells (Gelderman and Semeijn, 2006;Nellore and Söderquist, 2000).…”