Enabling integrated business planning through big data analytics: a case study on sales and operations planning

IF 5.9 3区 管理学 Q1 MANAGEMENT
Alexa Schlegel, Hendrik Birkel, E. Hartmann
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引用次数: 22

Abstract

PurposeThe purpose of this study is to investigate how big data analytics capabilities (BDAC) enable the implementation of integrated business planning (IBP) – the advanced form of sales and operations planning (S&OP) – by counteracting the increasing information processing requirements.Design/methodology/approachThe research model is grounded in the organizational information processing theory (OIPT). An embedded single case study on a multinational agrochemical company with multiple geographically distinguished sub-units of analysis was conducted. Data were collected in workshops, semistructured interviews as well as direct observations and enriched by secondary data from internal company sources as well as publicly available sources.FindingsThe results show the relevancy of establishing BDAC within an organization to apply IBP by providing empirical evidence of BDA solutions in S&OP. The study highlights how BDAC increase an organization's information processing capacity and consequently enable efficient and effective S&OP. Practical guidance toward the development of tangible, human and intangible BDAC in a particular sequence is given.Originality/valueThis study is the first theoretically grounded, empirical investigation of S&OP implementation journeys under consideration of the impact of BDAC.
通过大数据分析实现综合业务规划:销售和运营规划案例研究
本研究的目的是调查大数据分析能力(BDAC)如何通过抵消日益增长的信息处理需求来实现集成业务计划(IBP)——销售和运营计划(S&OP)的高级形式。设计/方法论/方法研究模型以组织信息处理理论(OIPT)为基础。对一家跨国农化公司进行了嵌入式单一案例研究,该公司具有多个地理上不同的分析子单元。数据通过研讨会、半结构化访谈和直接观察收集,并通过来自公司内部和公开来源的二手数据进行丰富。研究结果表明,通过在S&OP中提供BDA解决方案的经验证据,在组织内部建立BDAC与应用IBP具有相关性。该研究强调了BDAC如何提高组织的信息处理能力,从而实现高效和有效的S&OP。对有形的、人的和无形的BDAC按特定顺序的发展给出了实践指导。原创性/价值本研究是第一个在考虑BDAC影响的情况下对S&OP实施过程进行理论基础的实证调查。
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来源期刊
CiteScore
11.20
自引率
10.40%
发文量
34
期刊介绍: IJPDLM seeks strategically focused, theoretically grounded, empirical and conceptual, quantitative and qualitative, rigorous and relevant, original research studies in logistics, physical distribution and supply chain management operations and associated strategic issues. Quantitatively oriented mathematical and modelling research papers are not suitable for IJPDLM. Desired topics include, but are not limited to: Customer service strategy Omni-channel and multi-channel distribution innovations Order processing and inventory management Implementation of supply chain processes Information and communication technology Sourcing and procurement Risk management and security Personnel recruitment and training Sustainability and environmental Collaboration and integration Global supply chain management and network complexity Information and knowledge management Legal, financial and public policy Retailing, channels and business-to-business management Organizational and human resource development Logistics and SCM education.
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