Business Analytics 2

Enabling integrated business planning through big data

analytics: a case study on sales and operations planning

Alexander Schlegel, Hendrik Sebastian Birkel and Evi Hartmann Chair of Supply Chain Management,

Friedrich-Alexander University Erlangen-Nuremberg, Nuremberg, Germany

Abstract

Purpose – The 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/approach – The 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. Findings – The 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/value – This study is the first theoretically grounded, empirical investigation of S&OP implementation journeys under consideration of the impact of BDAC.

Keywords Integrated business planning, Sales and operations planning, Demand and supply planning, Big

data analytics capabilities, Case study, OIPT

Paper type Research paper

Introduction Organizations face the challenge of steadily increasing dynamics in the business environment, while trying to establish competitive advantages to achieve a sustainable business model (Porter and Millar, 1985). Coping with these dynamics which are resulting in increased uncertainty requires advanced and integrated planning activities within and across organizations to be prepared for the future (Barratt and Barratt, 2011; Oliva and Watson, 2011; Kaipia et al., 2017).

Although concepts relating to sales and operations planning (S&OP) have been recognized for three decades in supply chain management (SCM) research (Ling and Goddard, 1988; Wallace and Stahl, 1999), organizations are struggling to implement rigid and mature S&OP forms. Therefore, they often remain in very basic stages. One advanced form of S&OP is integrated business planning (IBP) which combines cross-functional planning activities related to sales, operations, marketing, finance as well as the strategic direction of a company with the integration across organizational boundaries toward customers and suppliers (Pal Singh Toor and Dhir, 2011; Smith et al., 2011; Bower, 2012; Palmatier and Crum, 2013). The integration aspect of business planning is crucial, since only through intra and interorganizational collaboration and information sharing all relevant knowledge about future development can be brought together for making a decision (Barratt and Oliveira, 2001; Barratt and Barratt, 2011; Stank et al., 2012; Goh and Eldridge, 2015).

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The current issue and full text archive of this journal is available on Emerald Insight at:

https://www.emerald.com/insight/0960-0035.htm

Received 29 May 2019 Revised 6 October 2019

7 April 2020 28 June 2020

1 October 2020 2 November 2020

Accepted 3 November 2020

International Journal of Physical Distribution & Logistics

Management Vol. 51 No. 6, 2021

pp. 607-633 © Emerald Publishing Limited

0960-0035 DOI 10.1108/IJPDLM-05-2019-0156

Combining shared information from different sources means working with a large amount of data. Data with high volume, velocity, variety, veracity and value are characterized through these five V’s in academic research as big data (McAfee et al., 2012; Richey et al., 2016; Waller and Fawcett, 2013; Wamba et al., 2015). While data-driven decision-making has been prevalent in SCM research for some years (Chen et al., 2012; Roßmann et al., 2018; Zhu et al., 2018), practitioners are still struggling with the implementation of big data analytics capabilities (BDAC) in the context of S&OP to achieve stages of a higher maturity (Schoenherr and Speier-Pero, 2015; Jonsson and Holmstr€om, 2016; Gunasekaran et al., 2017; Zhu et al., 2018). Previous studies are highlighting that enhanced S&OP capabilities are benefitting from BDAC. BDAC are necessary to be able to utilize the power of internal and external data effectively for business decision-making with optimization and decision tools (Schoenherr and Speier-Pero, 2015; Aryal et al., 2018).

In addition, empirical research on the implementation of advanced forms of S&OP is lacking in general (Grimson and Pyke, 2007; Jonsson and Holmstr€om, 2016; Danese et al., 2018; Kristensen and Jonsson, 2018). While the benefits of BDAC on decision-making in S&OP have been mentioned by scholars (Schoenherr and Speier-Pero, 2015; Aryal et al., 2018), the lack of empirical research on BDAC in S&OP results in unclarity about the utilization and detailed performance outcomes of BDAC in S&OP. This study aims to close the identified research gap of lacking extensive empirical research on advanced S&OP implementations under consideration of BDAC by answering the following research questions:

RQ1. How can BDAC increase the efficiency and effectiveness of S&OP?

RQ2. How can big data analytics (BDA) solutions be utilized in S&OP?

To answer these questions, a case study on the implementation journey of S&OP in an agrochemical multinational corporation (MNC) was conducted by utilizing organizational information processing theory (OIPT) as a theoretical lens (Galbraith, 1974). OIPT postulates that the performance of decision-making within organizations in uncertain and equivocal environments is based on the fit of information processing requirements (IPR) with information processing capacities (IPC) (Galbraith, 1974; Tushman and Nadler, 1978; Daft and Lengel, 1986; Bensaou and Venkatraman, 1995). In the context of S&OP, the transition to a more mature level of data analytics leads to a rising mass of data. As a consequence, IPR are increasing. Accordingly, IPC need to be increased to achieve a fit. With the agrochemical industry, the selected case study replies to the call of scholars for research on S&OP processes in various contexts and industries (Grimson and Pyke, 2007; Singh, 2010; Iyengar and Gupta, 2013; Hulth�en et al., 2016; N€aslund and Williamson, 2017).

The remaining part of this paper is divided into five sections, beginning with a literature review to reveal the status quo of academic research on S&OP and BDA. Section three describes the research methodology by depicting the complete process of sampling, data gathering and analysis. Section four presents the findings of the study followed by section five which includes a critical discussion. The last section contains the conclusion of the study and an outlook for further S&OP research.

Literature review and theoretical framework Integrated business planning and big data analytics Comprehensive literature review studies on S&OP have been published recently (Thom�e et al., 2012; Tuomikangas and Kaipia, 2014; Kristensen and Jonsson 2018). Hence, this section will only focus on relevant publications of the two research streams S&OP and BDA instead of providing a broad overview.

S&OP is defined as a business process which is “balancing supply and demand, and aligning strategic and operational plans, on a tactical planning horizon” (Kristensen and

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Jonsson, 2018, p. 21). Scholars describe different forms and maturity stages of S&OP (Grimson and Pyke, 2007; Danese et al., 2018). IBP – as an advanced form of S&OP – is predominantly a practitioner’s phenomenon. Consequently, studies on this concept are mainly published in practitioners’ journals, consultancy reports or online blogs. Thus, academic definitions do not exist currently (Pal Singh Toor and Dhir, 2011; Smith et al., 2011; Bower, 2012). This study defines IBP as an organization’s unique, cross-functional business planning process, which results in a common set of tactical and strategic goals for profit optimization with the involvement of customers and suppliers.

The main difference between IBP and less advanced forms of S&OP is that standard S&OP originates from supply chain (SC) and therefore primarily possesses the supply– demand balancing character. In contrast to the intra and interorganizational integration characteristic of IBP, standard S&OP often falls short in true cross-functional implementation supported by top management (Ambrose et al., 2018; Piercy and Ellinger, 2015). Therefore, SC collaboration from two perspectives is of utmost importance for IBP: first, intraorganizational collaboration enforced by cross-functional teams to break functional silos and ensure optimization toward one common goal instead of several individual, conflicting targets (Oliva and Watson, 2011; Thom�e et al., 2012) and second, interorganizational collaboration with suppliers and customers as an instrument for reducing supply and demand uncertainty (Cachon and Fisher, 2000; Kouvelis and Milner, 2002; de Leeuw and Fransoo, 2009). IBP as an advanced form of S&OP is characterized by not only a tactical but also a strategic timeframe. Therefore, IBP also covers long-term business decisions such as asset investment by utilizing a strong profitability perspective. IBP is based on a tremendous flow of information. Among others, it results from collaborative planning, forecasting and replenishment (CPFR) and therefore requires certain capabilities to be processed (Smith et al., 2011). By considering information from outside of the focal company and extending the planning scope across several business functions, the volume, variety, velocity, veracity and value (5V baseline) of data to identify influence factors for decision- making increases significantly. SCM scholars reveal promising application areas for BDA in SCM. According to recent studies, planning activities incorporating demand or supply planning are one of the main application fields for BDA (Waller and Fawcett, 2013; Schoenherr and Speier-Pero, 2015; Wang et al., 2016; Roßmann et al., 2018; Zhu et al., 2018). To apply BDA, organizations need to establish corresponding capabilities. As highlighted by Akter et al. (2016), the definition of the BDAC construct can have different perspectives such as competitive advantage, alignment of strategy and capabilities or decision-making ability. In the study at hand, BDAC are defined according to Gupta and George (2016) “as a firm’s ability to assemble, integrate and deploy its big data-based resources.”

With increasing complexity in a SC, the number of sources for uncertainty is increasing (Wilding, 1998). In this way, the measurement of uncertainty drivers is increasing the extent of big data. This leads to more required BDAC in complex planning environments. Using big data as decision-making support is enfolding its benefits especially in more complex situations where tangible, human and intangible BDAC are required to process and synthesize information from various sources leading to reduced demand and supply uncertainty (Dubey et al., 2019, 2020; Srinivasan and Swink, 2018; Gupta and George, 2016; Chen et al., 2015). In low uncertainty contexts, descriptive analytics and business intelligence are usually sufficient to support decision-making (Janssen et al., 2017). Therefore, BDAC are rather enabling more complex decision-making (Power, 2014). The complexity explains the delineation of traditional business intelligence, which relies on data collection, extraction and analysis of structured data from company internal databases and BDA, which is investigated in this study (Chen et al., 2012). Environments with low planning complexity are characterized by a low number of uncertainty sources. While low uncertainty can be countered by increased transparency through traditional business intelligence, data-rich

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environments with high planning complexity require more advanced analytics based on structured and unstructured data with high volume, variety, velocity, veracity and value (5V baseline) (Yu et al., 2018). Cao et al. (2015) have investigated that business analytics have a direct and positive effect on an organization’s IPC and an indirect positive effect on decision- making effectiveness. Their study highlights the relevancy of business analytics in relation to decision-making in general but is not linked to the S&OP context. In addition, Zhu et al. summarized the tremendous potential of BDAC applied to planning processes: “In particular, improving analytics capability in the plan process might provide the greatest return on investment because this [. . .] helps to improve analytics capabilities in other processes” (2018, p. 60).

Although research on BDA is increasing, studies on its capabilities are limited (Mikalef et al., 2018). Following these findings and considering the scarcity of empirical studies on BDAC in specific contexts (Mikalef et al., 2019), more academic research on BDAC is needed.

Theoretical lens According to OIPT, organizations are processing information under uncertain and equivocal circumstances to make business decisions about, e.g. organizational design, allocation of resources, relationships with business partners or sales and operations. Increases of IPR without adjustments of an organizations’ IPC are causing a mismatch between IPR and IPC, leading to ineffective decision-making. Consequently, organizations have the choice of implementing information processing mechanisms (IPM), which increase IPC, or structural mechanisms, which reduce IPR, to finally achieve a fit (Galbraith, 1974; Tushman and Nadler, 1978; Daft and Lengel, 1986; Bensaou and Venkatraman, 1995; Busse et al., 2017).

When organizations are transitioning from one S&OP maturity stage to another, increases in the complexity of decision-making as well as increases in the number of involved stakeholders can be observed (Grimson and Pyke, 2007; Goh and Eldridge, 2015). Thus, the amount and complexity of information that needs to be processed increase, resulting in higher IPR. In line with this, the capability of utilizing BDA represents an organization’s IPC in the S&OP context. It needs to adhere to IPR to avoid a mismatch. Thereby, the study on hand is based on the research model in Figure 1, which describes the constructs and their relationships. It is grounded in OIPT and illustrates that an IBP implementation, as the

Figure 1. Research model

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most mature S&OP stage, increases an organization’s IPR driven by an environment with high uncertainty and equivocality (Daft and Lengel, 1986; Busse et al., 2017). According to OIPT, uncertainty and equivocality are originating from the environment (i.e. supply and demand uncertainty in the business environment), characteristics (i.e. S&OP implementation- related uncertainty and equivocality) and interdependence (i.e. cross-functionality related uncertainty and equivocality) of the task (Tushman and Nadler, 1978; Bensaou and Venkatraman, 1995; Grimson and Pyke, 2007; Dreyer et al., 2018). The S&OP implementation construct, which is considered to be the task, is adapted from Grimson and Pyke (2007) and Dreyer et al. (2018). At the same time, BDAC increase an organization’s IPC to result in a fit by compensating increased IPR. The BDAC construct is adapted from Gupta and George (2016). Ultimately the model outlines BDAC as an enabler for successful IBP implementations measured by S&OP performance. The S&OP performance construct is adapted from Hulth�en et al. (2016) and splits into the two dimension effectiveness and efficiency, as stated in Table 1. While effectiveness consists of factors, such as input data quality, forecast accuracy, resource adherence, trade-off measures, plans adherence as well as actuals vs targets, efficiency contains the factors people, process and organization.

Table 1 describes the construct definitions of this study.

Methodology Research design An embedded single case study approach has been chosen in this research due to several reasons. First, empirical research on advanced forms of S&OP implementations is lacking (Tuomikangas and Kaipia, 2014; Kristensen and Jonsson 2018), wherefore previous studies are calling for case study-based publications (Thom�e et al., 2012). Second, case study research is applicable to investigations of contemporary and complex phenomena (Meredith, 1998), which is the case when analyzing collaborative interactions between several involved parties. Third, the chosen research design is especially suitable for answering “how” and “why” questions as phrased in this investigation (Yin, 2014). Fourth, assessing the depth and width of this study’s research objective (Eisenhardt, 1989; Seuring, 2008), there is a stronger focus on understanding the depth of BDAC in S&OP, which is favored by a single case approach. The studied context requires a deep understanding of industry-specific business planning processes and behaviors. The focus on a single case allows building this detailed knowledge.

Construct Definition Source

S&OP implementation

Implementation of the mechanisms meetings and collaboration, organization, performance measurements and IT to achieve a unique, cross-functional business planning process which results in a common set of tactical and strategic goals for profit optimization with the involvement of customers and suppliers

Grimson and Pyke (2007), Dreyer et al. (2018)

S&OP performance Measurement for the efficiency and effectiveness of S&OP. S&OP effectiveness measures the influence of S&OP on corporate effectiveness and efficiency. S&OP efficiency measures how well the S&OP process is managed

Hulth�en et al. (2016)

BDAC Capability based on tangible, human and intangible resources which an organization need to possess to reap benefits from big data

Gupta and George (2016)

BDA solutions in S&OP

Solutions and applications which use big data to assist decision-making

Table 1. Construct definitions

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Finally, the availability of companies which represent the required maturity in S&OP and are willing to share in-depth information for academia is low. Therefore, it is very difficult to extend the number of case companies and apply a multiple case study approach for the juvenile research stream on BDAC in S&OP.

The research design of the case study has been evaluated for transferability, truth-value and traceability according to da Mota Pedrosa et al. (2012), as outlined in Table 2.

To ensure reliability of this study, the conducted research steps are rigorously described in the following sections. A case study protocol, detailed transcripts of interviews and notes from observations and workshops are available, so that the study could be repeated but would exceed the length of this manuscript. Due to the applied single case research methodology, the validity of this study is limited to the used constructs and the developed relationship between them.

Sampling The search for a purposeful case for this research was based on several factors, which can be subsumed under two industry-related and two company-related sampling criteria. The industry-related sampling criteria were (1) level of uncertainty in business planning and (2) number of published S&OP and IBP studies. Additionally, company-related sampling criteria were (3) experience with BDAC and advanced forms of S&OP and (4) willingness to share experience on the transformation journey from standard S&OP to IBP with academia.

The selected agrochemical industry is characterized by high uncertainty in demand and supply, which means high relevancy of planning activities. Demand uncertainties originate from weather-related seasonality and outstanding registration approvals by authorities. Supply uncertainties are based on the availability of raw materials and active ingredients as

Indicator Addressed in investigation

Transferability Theoretical aim of the study

Explain success of S&OP implementation with BDAC und application of OIPT

Unit of analysis MNC in agrochemical industry with 11 countries embedded as sub-units of analysis

Justification of case selection

Agrochemical industry is characterized by high uncertainty for decision-making, and case company has deep and broad knowledge on S&OP

Number of cases used in study

Single case with 11 embedded sub-units of analysis

Truth-value Coding Two coding cycles according to Salda~na (2015) Comparison Analysis within and across sub-units to search for patterns Iteration Data gathering and analysis overlaps, first results were

considered for pending interviews, workshops and observations during participation in IBP meetings

Refutation Raw data, results and conclusions of data analysis were reviewed by and discussed with informants

Traceability Protocol or database Case study protocol Data collection guideline

Semistructured interviews, workshops and direct observation of S&OP meetings

Informant selection Experts from different countries and different business functions who are directly involved in S&OP meetings or responsible for preparation of decision-making

Number of informants 23

Source(s): Adapted from da Mota Pedrosa et al. (2012)

Table 2. Quality criteria for case study-based research

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well as the high variety of country-specific end products driven by regulatory product registrations.

According to the recent literature reviews on S&OP studies, research on the agrochemical industry has not yet been published (Tuomikangas and Kaipia, 2014; Kristensen and Jonsson, 2018). While empirical evidence from a case study on the chemical industry exists (Ivert and Jonsson, 2010), the specific characteristics of agrochemistry related to seasonal demand, cross-contamination risks in production or strict country-specific end product regulations require a particular S&OP setup. A company within the agrochemical industry, which shows a high maturity in S&OP, was selected to be able to provide experience in advanced S&OP implementations. The 11 embedded sub-units of analysis of the selected MNC are structured by sales countries since this represents the structuring of S&OP processes within the case company. This structure emerges from the characteristic of the analyzed industry related to higher demand than supply uncertainty. In total, the four geographic regions North America, South America, Asia/Pacific and Europe/Middle East/ Africa were covered by 11 different countries: Argentina, Australia, Brazil, Bulgaria, Canada, China, Germany, Great Britain, Russia, South Africa and the United States. The manufacturing strategy in the agrochemical industry is make-to-stock based on monthly updated forecasts with a time horizon of two to three years. Typically, multiple sales countries share multiple sources of supply, leading to dependencies across countries. S&OP is managed on a country level with executive review meetings on an aggregated regional level. Figure A1 illustrates the SC of the case company.

Data collection The collection of data started with four regional IBP workshops involving cross-functional, regional representatives as participants (see Table 3). Each workshop with a duration of two days resulted in a comprehensive understanding of the as-is situation of S&OP in the corresponding geographic region. The content of the discussions was related to detailed explanations of each step in the planning cycle, achievements of previous improvement projects regarding S&OP and a collection of the current problems related to planning.

After finalizing the regional workshops, the data collection with individual informants was started. The choice for semistructured interviews as main data collection methodology is based on the work of Brinkmann and Kvale (2015). Semi-structured interviews allow on the one hand to follow a predefined structure when guiding the interviewee through a dialogue. On the other hand, they ensure a sufficient degree of freedom to capture unforeseeable directions of the interviewees. The selection of interviewees was focusing on directly or indirectly involved S&OP participants. Since the meetings are of cross-functional nature and usually are conducted on a country level in the case study company, representatives from controlling, SC, marketing and sales were interviewed.

In addition to standard questions, which were asked in all interviews, further questions were discussed depending on the progress of each individual interview. All interviews had a length of 60–90 minutes and were conducted in person (18 out of 23 interviews) or via telephone in combination with video conference due to the distance between the geographical locations of interviewer and interviewee (5 out of 23). The interviews were recorded whenever possible. In interviews where recording was not permitted due to confidentiality concerns, detailed notes were taken. After the interviews were conducted, the created case study protocol was reviewed and validated by the informants to avoid misunderstandings and ambiguities. In addition to collecting data from workshops and individual interviews, participation in monthly S&OP/IBP meetings in five different countries (Brazil, Germany, Great Britain, South Africa and the United States) ensured the possibility of directly observing behavioral dynamics of all contributing individuals.

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Additionally, data were collected via company internal documents (i.e. presentations, guidebooks and data exports from company business warehouses) as well as publicly available documents (i.e. consultancy reports and studies) and complemented with S&OP research in academic journals.

Data analysis The data analysis is based on a coding process, which consists of two cycles with two iterations, respectively, (Corbin and Strauss, 2014; Yin, 2014; Salda~na, 2015): one initial coding cycle which is rather straightforward and a second cycle which is based on more advanced methods, as outlined in Table 4. In coding cycle 1, descriptive coding was applied to the results of the workshops as well as notes and transcripts of the individual interviews. In coding cycle 2, pattern coding and then elaborative coding was applied to the basic categories from the first coding cycle. The final deductive step of elaborative coding used theoretical constructs from previous studies to align with the codes developed for this study (Grimson and Pyke, 2007; Gupta and George, 2016; Hulth�en et al., 2016; Dreyer et al., 2018).

To be able to analyze the constructs IPC, IPR, fit and performance according to OIPT, as stated in Figure 1, the data have been assessed on a five-point scale as in previous OIPT- based case study research (Foerstl et al., 2018). The scale for S&OP implementation, BDAC and S&OP performance constructs is structured as follows: 1 5 very low, 2 5 low, 3 5 medium, 4 5 high and 5 5 very high. Uncertainty and equivocality, as driver for IPR, were measured and analyzed through dimensions of the S&OP implementation, which are increasing IPR during the transformation process. Since environmental uncertainty, which is comprising complexity and dynamism, is difficult to quantify (Duncan, 1972; Busse et al., 2017), the sample of the agrochemical industry reflects an uncertain environment in general. Individual uncertainty per sub-unit of analysis was assessed for task characteristics and task interdependence.

Thescalefor measuringthe fitand comparing S&OPperformancewithS&OP perfExpected is built on the following elements: 0 5 excellent, 1 5 good, 2 5 acceptable, 3 5 poor and 4 5 not fitting. The fit represents the difference between S&OP implementation and BDAC. The fit has been compared to the difference between S&OP performance and S&OP perfExpected. While the actual performance is derived from the data collected for each sub-unit of analysis, the expected performance equals the level of S&OP implementation. The underlying assumption according to OIPT is that optimal performance could be achieved once IPR and IPC are available at an equal level. To measure S&OP performance, the framework of Hulth�en et al. (2016) was adopted. The grading of the constructs has been conducted by the researchers according to collected data and conducted observations. Afterwards, it was validated by the respective interviewee. This approach has been used to quantify and structure the large amount of information which has been gathered during the study. The next step of analysis consisted of different comparisons. On the one side, factors and dimensions were compared within a sub- unit of analysis. On the other side, comparisons across different sub-units were conducted. In order to show whether the countries’ IPC and IPR levels are in accordance with S&OP maturity stages, fuzzy C-means clustering was applied.

Finally, the results were shared with the case company to verify the correctness of the researcher’s interpretation and the final conclusions. The iterative approach to share the raw data, analysis results and final conclusions at different stages with the informants nurtured the truth-value of the investigation.

Findings The results of the data analysis are summarized in Table 5, which allows the comparison of data within as well as across sub-units of analysis according to the underlying research

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Region Country Business functiona Informant Job titlea

Working experience in years

Type of data collection

Europe, Middle East and Africa

Multiple SC, C M and S

W1 Regional representatives from SC, C, M and S

– Workshop

E1 SC I1 Demand Planning Manager

10–14 Personal interview

C I2 Controller 5–9 Personal interview M I3 Marketing

Manager 5–9 Personal interview

S I4 Sales Excellence Expert

10–14 Personal interview

E2 C I5 Controller 5–9 Personal interview SC I6 Demand Planning

Manager 15–19 Personal interview

S I7 Head of Sales 15–19 Personal interview E3 C I8 Business

Controller 10–14 Personal interview

SC I9 Demand Planning Manager

10–14 Personal interview

E4 C I10 Head of Controlling

10–14 Personal interview

SC I11 Head of Supply Chain

10–14 Personal interview

E5 SC and C I12 Head of Controlling and Supply Chain

10–14 Personal interview

North America

Multiple SC, C M and S

W2 Regional representatives from SC, C, M and S

– Workshop

N1 SC I13 Team lead Supply Chain

10–14 Personal interview

C I14 Controller 5–9 Personal interview M I15 Marketing

Manager 5–9 Personal interview

N2 C I16 Head of Controlling

15–19 Telephone þ video conference

South America

Multiple SC, C M, S

W3 Regional representatives from SC, C, M and S

– Workshop

S1 SC I17 Team lead Supply Chain

10–14 Personal interview

C I18 Head of Controlling

10–14 Personal interview

S2 SC and C I19 Controller and Demand Planner

15–19 Telephone þ video conference

(continued)

Table 3. Overview of case

countries and informants

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model of this study (Figure 1). The relations between IPC, IPR, S&OP performance as well as the fit were analyzed for each sub-unit.

S&OP performance The results show that the fit between IPR and IPC correlates with S&OP performance

S&OP perfExpected

(see Figure A2). The tendency across all sub-units is that IPR is higher than IPC. Figure 2 shows the analysis outcome across the sub-units of analysis which resulted in the identification of three clusters based on fuzzy C-means algorithm. The degrees of cluster memberships are listed in Table A1. The clusters are in accordance with the sub-units’ S&OP maturity stages derived from the framework of Danese et al. (2018). While sub-units of developed economies in cluster 3 are already implementing and optimizing IBP, other sub- units of clusters 1 and 2, especially in emerging economies, are still working in less mature S&OP stages. With one exception (S2), the data of all sub-units show the tendency that a high fit of IPC and IPR (good or excellent) also means a low difference between S&OP performance and S&OP perfExpected. In contrast, sub-units with a lower fit show a high difference between performance and perfExpected. This confirms the underlying assumptions of the OIPT- grounded research model, as stated in Figure 1 and leads to proposition P1a.

P1a. The greater the fit between IPR and IPC in the context of S&OP, the smaller the deviation between the expected S&OP performance and the actual S&OP performance.

Cluster 1 consists of the countries A2, E5 and S2 which operate in a low maturity stage. They are characterized by a reactive way of S&OP and are typically facing low to medium uncertainty. Decisions are primarily taken based on previous experience. Participation of different departments in regular meetings is ensured while the contribution of participants is focused on their responsibility silo. Cross-functional consequences of decisions are not fully transparent. The planning process is based on manually prepared information without utilization of standardized software. IPR of these sub-units are low/medium, and they are close to the desired fit. An investment in higher BDAC, which leads to higher IPC, would only marginally increase S&OP performance and result in slack afterwards.

Cluster 2 represents standard S&OP maturity in combination with a lack of BDAC. These countries have established standard S&OP while some of them started the transition to IBP.

Region Country Business functiona Informant Job titlea

Working experience in years

Type of data collection

Asia/ Pacific

Multiple SC, C M and S

W4 Regional representatives from SC, C, M and S

– Workshop

A1 SC I20 Demand Planner 5–9 Telephone þ video conference

C I21 Controller 5–9 Telephone þ video conference

A2 SC and C I22 Controller and Demand Planner

15–19 Telephone þ video conference

Multiple M I23 Vice President Marketing

15–19 Personal interview

Note(s): a SC 5 Supply Chain; C 5 Controlling; M 5 Marketing; S 5 SalesTable 3.

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Coding cycle Iteration Data source Approach Result Example

1 1 Informants W1, W2, W3 and W4 Internal documents

Descriptive Coding

Basic level categories

(1) SC participation (2) Forecast Manager

participation (3) Demand Planner

participation (4) Sales rep

participation (5) Sales Manager

participation (6) Product Manager

participation (7) Controller

participation (8) Commercial

excellence participation

(9) Country Head participation

(10) Management participation

(11) Customer participation

(12) Supplier participation

2 Informants: I1–I23 Internal documents

Descriptive coding

Basic level categories per country

(1) E1: Participation of registration department

(2) E1: SC participation (3) E1: Controlling

participation (4) E2: SC participation . . .

2 1 Results of cycle 1

Pattern Coding Miles and Huberman (1994)

Distinct categories for whole case

(1) SC participation (2) Sales participation (3) Registration

participation (4) Marketing

participation (5) Controlling

participation (6) Leadership

participation (7) Customer

participation (8) Supplier

participation 2 Results of

iteration 1 of cycle 2

Elaborative coding Auerbach and Silverstein (2003)

Final factors and dimensions according to constructs from the literature

Involvement in cross- functional/cross- company planning meeting Table 4.

Coding procedure

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D im

en si o n

F a ct o r

C o u n tr y

E 1

E 2

E 3

E 4

E 5

N 1

N 2

S 1

S 2

A 1

A 2

IP R

S & O P

im p le m en ta ti o n

re la te d

u n ce rt a in ty

a n d

eq u iv o ca li ty

M ee ti n g s a n d

co ll a b o ra ti o n

D a ta

a cc es s

V er y

h ig h

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V er y

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V er y h ig h

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O rg a n iz a ti o n

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M ed iu m

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V er y

h ig h

H ig h

H ig h

E m p o w er m en t

H ig h

V er y

h ig h

V er y h ig h

V er y h ig h

L o w

H ig h

H ig h

V er y h ig h

M ed iu m

M ed iu m

L o w

P er fo rm

a n ce

m ea su re m en t

E ff ec ti v en es s

m ea su re m en t

H ig h

V er y

h ig h

V er y h ig h

V er y h ig h

M ed iu m

V er y h ig h

V er y

h ig h

H ig h

M ed iu m

V er y h ig h

H ig h

In fo rm

a ti o n

te ch n o lo g y

O w n er sh ip

o f

in fo rm

a ti o n

V er y

h ig h

V er y

h ig h

H ig h

H ig h

L o w

V er y h ig h

H ig h

V er y h ig h

H ig h

H ig h

M ed iu m

In fo rm

a ti o n

sh a ri n g a n d

co n so li d a ti o n

V er y

h ig h

V er y

h ig h

H ig h

V er y h ig h

L o w

H ig h

H ig h

H ig h

M ed iu m

M ed iu m

H ig h

A d v a n ce m en t in

te ch n o lo g y fo r

d ec is io n -m

a k in g

V er y

h ig h

V er y

h ig h

V er y h ig h

V er y h ig h

V er y

lo w

H ig h

M ed iu m

V er y h ig h

L o w

M ed iu m

V er y

lo w

C ro ss -

fu n ct io n a li ty

re la te d

u n ce rt a in ty

a n d

eq u iv o ca li ty

M ee ti n g s a n d

co ll a b o ra ti o n

In v o lv em

en t in

cr o ss -f u n ct io n a l/

cr o ss -c o m p a n y

p la n n in g

m ee ti n g s

V er y

h ig h

H ig h

H ig h

V er y h ig h

H ig h

V er y h ig h

V er y

h ig h

V er y h ig h

M ed iu m

M ed iu m

M ed iu m

S p a n o f

co ll a b o ra ti o n

H ig h

V er y

h ig h

V er y h ig h

H ig h

M ed iu m

V er y h ig h

V er y

h ig h

M ed iu m

M ed iu m

V er y h ig h

M ed iu m

P er fo rm

a n ce

m ea su re m en t

C ro ss -f u n ct io n a l

m ea su re m en ts

V er y

h ig h

V er y

h ig h

H ig h

V er y h ig h

L o w

H ig h

V er y

h ig h

V er y h ig h

L o w

H ig h

L o w

C ro ss -f u n ct io n a l

a cc o u n ta b il it y

V er y

h ig h

H ig h

H ig h

H ig h

L o w

V er y h ig h

M ed iu m

V er y h ig h

L o w

M ed iu m

L o w

S & O P im

p le m en ta ti o n

V er y

h ig h

V er y

h ig h

V er y h ig h

V er y h ig h

L o w

V er y h ig h

H ig h

V er y h ig h

M ed iu m

H ig h

M ed iu m

(c o n ti n u ed

)

Table 5. Assessment of IPR, IPC, fit and performance

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D im

en si o n

F a ct o r

C o u n tr y

E 1

E 2

E 3

E 4

E 5

N 1

N 2

S 1

S 2

A 1

A 2

IP C

B D A C

T a n g ib le

D a ta

V er y

h ig h

M ed iu m

L o w

L o w

M ed iu m

M ed iu m

M ed iu m

H ig h

M ed iu m

M ed iu m

L o w

T ec h n o lo g y

V er y

h ig h

V er y

h ig h

M ed iu m

M ed iu m

V er y

lo w

H ig h

V er y

h ig h

M ed iu m

L o w

L o w

L o w

B a si c re so u rc es

H ig h

H ig h

M ed iu m

M ed iu m

M ed iu m

H ig h

M ed iu m

M ed iu m

L o w

M ed iu m

M ed iu m

H u m a n

M a n a g er ia l

S k il ls

V er y

h ig h

M ed iu m

M ed iu m

H ig h

M ed iu m

M ed iu m

H ig h

M ed iu m

V er y

lo w

L o w

H ig h

T ec h n ic a l S k il ls

H ig h

H ig h

L o w

M ed iu m

V er y

lo w

L o w

V er y

h ig h

M ed iu m

V er y

lo w

M ed iu m

L o w

In ta n g ib le

D a ta -d ri v en

cu lt u re

V er y

h ig h

H ig h

H ig h

M ed iu m

V er y

lo w

M ed iu m

M ed iu m

H ig h

V er y

lo w

M ed iu m

L o w

O rg a n iz a ti o n a l

le a rn in g

H ig h

H ig h

M ed iu m

M ed iu m

V er y

lo w

M ed iu m

H ig h

M ed iu m

H ig h

M ed iu m

M ed iu m

B D A C

V er y

h ig h

H ig h

M ed iu m

M ed iu m

L o w

M ed iu m

H ig h

M ed iu m

L o w

M ed iu m

M ed iu m

F it

E x ce lle n t

G o o d

A cc ep ta b le

A cc ep ta b le

E x ce lle n t

A cc ep ta b le

E x ce lle n t

A cc ep ta b le

G o o d

G o o d

E x ce lle n t

P er fo rm

a n ce

S & O P

p er fo rm

a n ce

S & O P

ef fe ct iv en es s

In p u t d a ta

q u a li ty

H ig h

H ig h

M ed iu m

M ed iu m

V er y

lo w

L o w

H ig h

H ig h

V er y

lo w

M ed iu m

L o w

F o re ca st

a cc u ra cy

M ed iu m

H ig h

H ig h

H ig h

M ed iu m

L o w

M ed iu m

M ed iu m

M ed iu m

L o w

L o w

R es o u rc e

a d h er en ce

H ig h

M ed iu m

M ed iu m

M ed iu m

L o w

M ed iu m

M ed iu m

L o w

V er y

lo w

L o w

L o w

T ra d e- o ff

m ea su re s

H ig h

H ig h

L o w

V er y lo w

L o w

M ed iu m

H ig h

M ed iu m

L o w

M ed iu m

M ed iu m

P la n s a d h er en ce

H ig h

M ed iu m

M ed iu m

V er y h ig h

L o w

L o w

M ed iu m

L o w

M ed iu m

L o w

M ed iu m

A ct u a l v s ta rg et

H ig h

H ig h

M ed iu m

V er y h ig h

M ed iu m

M ed iu m

M ed iu m

L o w

L o w

L o w

H ig h

S & O P

ef fi ci en cy

P ro ce ss

H ig h

H ig h

H ig h

L o w

L o w

L o w

M ed iu m

L o w

L o w

M ed iu m

M ed iu m

O rg a n iz a ti o n

H ig h

H ig h

V er y h ig h

V er y lo w

L o w

M ed iu m

H ig h

M ed iu m

M ed iu m

L o w

M ed iu m

P eo p le

H ig h

M ed iu m

L o w

V er y lo w

L o w

M ed iu m

M ed iu m

L o w

V er y

lo w

L o w

M ed iu m

S & O P p er fo rm

a n ce

H ig h

H ig h

M ed iu m

M ed iu m

L o w

M ed iu m

M ed iu m

M ed iu m

L o w

L o w

M ed iu m

S & O P p er f E

x p e ct e d

V er y

h ig h

V er y

h ig h

V er y h ig h

V er y h ig h

L o w

V er y h ig h

H ig h

V er y h ig h

M ed iu m

H ig h

M ed iu m

S & O P p er fo rm

a n ce

v s IS & O P

p er f E

x p e ct e d

G o o d

G o o d

A cc ep ta b le

A cc ep ta b le

E x ce lle n t

A cc ep ta b le

G o o d

A cc ep ta b le

G o o d

A cc ep ta b le

E x ce lle n t

Table 5.

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The business environment reflects high uncertainty and equivocality while the S&OP maturity level and BDA solutions usage in S&OP is low to medium. Critical decisions and their trade-off effects are prepared prior to the meetings. Structured data are available but preparations require significant effort for participating functions, which is partially supported by a variety of advanced planning, optimization, analytics, reporting or customer relationship management software. The automation level of data processing is considered technical bottleneck. The cluster consists of the countries A1, E3, E4, N1 and S1. The mismatch between IPR and IPC of these countries is high. To achieve the S&OP performance according to the implemented S&OP stage, higher BDAC would contribute significantly. For countries which are at a medium S&OP stage, BDAC would enable the transition into IBP as mentioned by cluster 2 informants (e.g. I10: “Right now our controllers are not experienced in using these new tools. We would need to conduct some training to fully utilize all functionalities”). In countries which have already started to implement IBP but have not developed proper BDAC yet, the desired level of S&OP performance would benefit from BDAC. As stated in Table 5, the sub-units of analysis with higher maturity stages are representing higher IPR. Consequently, the second proposition is as follows:

P1b. Greater maturity in S&OP processes will lead to superior BDAC, thereby resulting in enhanced S&OP performance.

This proposition is in line with available S&OP maturity models of previous studies (e.g. Danese et al., 2018), which highlight the increasing impact of the mechanism information technology (IT) on S&OP.

Cluster 3 represents the countries N2, E1 and E2 which are applying IBP successfully. All three countries face high uncertainty and equivocality, have implemented IBP and are therefore in an advanced S&OP maturity stage. While these countries show higher IPR than IPC, they are closer to the fit than countries of cluster 2. Higher developed BDAC which are facilitating S&OP performance can be observed in these countries. Further increases in BDAC would only marginally increase S&OP performance, if at all.

A more precise evaluation of the effects of BDAC on S&OP performance can be outlined by splitting the construct into the dimensions efficiency and effectiveness. Numerous examples of S&OP efficiency improvements were observed in cluster 3. Process improvements required a combination of tangible, human and intangible BDAC. Meeting efficiency, in particular, directly benefitted after the majority of manual decision-making procedures had been systematically automated by utilizing predictive and prescriptive analytics. The consequence was that meetings were mainly related to exception-based

Figure 2. Fuzzy C-means clusters for sub-units of analysis

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decision-making. Additionally, prescriptive analytics increased meeting efficiency even further by calculating cross-functional conditions and influences of discussed decisions instantly during the meetings. While increases in managerial and technical skills related to big data led to increased awareness of S&OP from executives, it indirectly resulted in increased participation in planning meetings. Employees with a data-driven mindset represented higher skills and competencies and induced higher efficiencies for all remaining meeting participants. Table A1 shows evidence in the data for improvements in S&OP efficiency in the form of supportive quotes.

Different best-practices in cluster 3 countries were collected and offer transparency on S&OP effectiveness improvements through the usage of BDAC. The input data quality was improved significantly by two measures. First, the amount of manual data input was reduced by processing data automatically and transferring data input activities from human to system whenever it was possible by utilizing descriptive, predictive and prescriptive analytics. Second, data entry related to planning information was assisted by automated predefined plausibility checks to identify obvious input errors during the execution of the input. Forecast accuracy improvements could be achieved by automating forecasting activities partially through machine learning techniques. Accuracy increases through the switch from manual to statistical forecasting were noticed, especially in the mid- to long-term horizon through bias reduction. Furthermore, BDA contributed to achieving higher integration since trade-off measures were embedded in software, which allowed users to directly see cross-functional impacts of decisions. With this measure, no manual effort is required by individuals to calculate the impact of what-if scenarios. By embedding all trade- off effects and constraints into one IT system, profitability-based business planning could finally be achieved. The data in Table 5, an analysis of the time span between first S&OP activities and the execution of the study as well as the conducted observations of cluster 3 countries, which have already implemented IBP and developed BDAC, show that countries with a high S&OP performance developed BDAC across all dimensions and did not only focus on individual capabilities. This results in the following proposition (e.g. I4: “The software is implemented and the users and management are trained but to sustain, we need to ensure that decisions are always taken based on the data and not on politics or emotions”).

P1c. The longer a high level of S&OP performance is maintained, the higher the relevancy of developing a mutual combination of tangible, human and intangible BDAC.

This finding confirms the validity of the formative construct, which was conceptualized by Gupta and George (2016). Table A3 shows evidence in the data of improvements in S&OP effectiveness due to BDA in the form of supportive quotes.

Big data analytics capabilities Different tangible, human and intangible BDAC have been reviewed in the case company. The results of the analysis on BDAC are mainly driven by informants of cluster 3, which have developed BDAC already intensively and could therefore share their experience. In addition, informants of cluster 2 listed missing capabilities in their countries which are aimed to be developed and are necessary for the transition from standard S&OP to IBP.

Related to tangible BDAC, it could be observed that highly mature countries implemented IT solutions which combine advanced analytics with an advanced planning system. In addition, new corporate technologies have been established to create a data lake for (1) merging internal and external data and (2) combining structured with unstructured data. The necessity of technology to combine multiple sources of data becomes obvious when analyzing decision-making relevant data. Previous studies have already listed data sources or influence

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factors for decision-making in SCM, especially demand planning (Hofmann and Rutschmann, 2018). However, within the specific context of the case study, all relevant S&OP decision- making factors were collected and categorized. While, in the past, internal data were primarily used for decision-making during S&OP, external data were added step-by-step. The entirety of the relevant decision-making data confirmed the 5V baseline of BDA representing high volume, variety, velocity, veracity and value. Case study countries that considered additional data sources compared to traditional decision-making data grew in data volume as well as data variety where sources with unstructured data from outside the company had been incorporated. The velocity aspect of BDA was one of the first evolution steps in the case company. Data which, in the past, had been considered monthly according to the planning cycle were afterwards refreshed daily, hourly or even transmitted in real time, which allowed faster reaction to changes and ignited the idea of event-based planning.

The development of tangible capabilities resulted in the establishment of a cross- functional platform with all relevant data homogenized for decision-making in S&OP for each business function’s perspective. Heterogenous data formats were combined from different data sources and refreshed in multiple frequencies, ensuring the possibility of analyzing, reporting and planning in one single system. It allowed the breakup of functional silos and changed behavior from previous local optimization of individual business functions according to their target setting to global optimization under consideration of corporate business targets and its conflicting sub-targets on a functional level. The creation of an optimal business plan is only possible by combining short-term aspects of sales with long- term aspects of marketing as well as quantity aspects of SC with price and foreign exchange- rate aspects of controlling.

When collecting developed capabilities, all informants reported examples for tangible capabilities at first. In all case countries, the journey of capability development started with the sequential enhancement of tangible capabilities, which afterwards were followed by human and intangible capabilities (e.g. I2: “We noticed the biggest change since the technical go-live of the software”). While a mutual combination of all BDAC is required, as stated in P1c, organizations need to decide where to start with capability development. A comparison of BDAC in Table 5 across the sub-units of analysis as well as an analysis on the time spans between capability development, S&OP implementation and the study leads to the following proposition:

P1d. The more tangible BDAC are developed within an organization, the faster improvements in S&OP performance can be realized.

Human-related capabilities mainly focus on managerial and technical skills. Executives were signing-off updated versions of the plans, which are created in S&OP cycles in the case company. To finally use the results of BDA, it needs to be ensured that executives understand and trust the developed insights. Informants mentioned IBP implementation barriers where the automatically developed plan was questioned conceptually every time, which results in a switch back to a more manual planning approach.

In addition to this, technical skills across all employees who are involved in S&OP need to be developed. The job profile of a typical supply planner, demand planner, controller, marketing manager or sales manager needs to be extended by a data science aspect which became visible when comparing the profiles across case countries. This does not mean that every job is now performed by a data scientist. Data scientists are important as experts for the establishment of BDA, while each employee who is involved in S&OP needs to develop a basic understanding. Without any technical skill, there will be no trust in the outcomes of BDA, leading to resistance against a data-driven culture.

Intangible capabilities can be developed by fostering the intensity of organizational learning and living a data-driven culture, which ensures the longevity of BDAC (Gupta and

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George, 2016). Informants of cluster 2 countries claimed that without developing a supportive culture for BDA initiatives, developments of tangible capabilities remain in the stage of pilot projects and will never become a real competitive advantage. Due to the repetitive character of S&OP, a data-driven culture is inevitable to drive planning maturity. Otherwise, there is the risk that BDA solutions are technically available but not used. In correspondence to the findings related to technical BDA skills, the intensity of organizational learning is seen as a critical topic in the case company. The real implications of the absence of this capability can only be measured in a longitudinal study. Beside the quick improvements from tangible capabilities, the analysis of human and intangible capabilities across sub-units, as stated in Table 5, are leading to the next proposition which is based on insights into cluster 3 countries with high S&OP performance (e.g. I7: “Although we saw the huge improvements after implementing the software, we need to notch up our performance because the real advantage is in the mindset of our workforce. A tool can be bought by anyone”).

P1e. The more human and intangible BDAC are developed within an organization, the more competitive and sustainable advantages can be realized from BDA in S&OP.

This finding specifies the results of previous research which claims that during BDA-enabled transformations IT capabilities in general have an impact on the competitive advantage of an organization (Wang et al., 2016).

Big data analytics solutions Within the case company, especially cluster 3 provided numerous examples of BDA solutions which allowed the organization to follow the integrated basic principles of IBP and act as IPM. Applications of descriptive analytics have been observed in all steps during planning cycles, from data preparation over the actual planning activity to the alignment meetings with executives. Starting with the first step of data preparation, the integration of decision- making relevant data has been fully automated in cluster 3 countries, while employees in clusters 1 and 2 countries are involved in manual data preparation activities every month. Further, automated dashboards visualize data from different perspectives during meetings and therefore allow the analysts and moderators of S&OP meetings to answer questions of executive participants immediately.

Predictive analytics is applied in the case company to automate forecasting of the elements on the profit and loss statement (e.g. sales quantities, prices and profitability metrics) and SC related information, such as production quantities or capacity requirements for production, transportation and procurement. The solutions are based on different methods (e.g. machine learning, deep learning and textual analytics) and combine internal data sources with sources from SC partners as well as third-party data providers. Table 6 illustrates all data sources which are used in cluster 3. From the perspective of S&OP performance, the implemented BDA solutions mainly influence the forecast accuracy and the meeting efficiency. In particular, the sub-units in cluster 3 have implemented cross-functional IBP software which fosters decision- making in one single solution and enhances the underlying planning process. Financial planning, data analytics, decision-making, reporting and highlighting trade-off effects are available in one solution. BDA solutions encompassing industry-specific peculiarities such as political sentiment analytics about product registration decisions, weather forecasts analytics, satellite imagery analytics for short-term in-season demand anticipation, dynamic pricing and marginanalytics or profitability-based multi-echelon network optimizationsfor production and transportation decisions are reviewed systematically to mitigate demand and supply uncertainty. Participation in cross-functional meetings is not perceived as root cause for high resource consumption due to preparation activities but rather as a possibility to discuss and decide trade-offs and dilemma scenarios.

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The cross-sub-unit comparison of utilized data sources for S&OP results in the following proposition (e.g. I3: “In the past we just focused on new product launches and POS data. But since we incorporated satellite data, external indicators and search trends, our Forecast Accuracy increased steadily”).

P2a. The more data sources are combined from inside the organization, SC partners and third parties for predictive analytics, the higher the efficiency and effectiveness of S&OP.

Prescriptive analytics solutions have been observed only in cluster 3 countries and in total less frequently than applications of descriptive or predictive analytics. A few examples of software solutions which assisted planners with decision recommendations were implemented. Predictive analytics is used to automate forecasting while prescriptive analytics assist planners and executives to highlight the cross-functional impact of taken

Data source Data owner IBP decision-making data

Internal data Supply chain department Packaging and labelling Information Production capacity Delivery reliability of logistics service provider Bill of materials Lead times Inventory Product segmentation Availability of preproducts and components

Controlling department Financial targets (e.g. sales target and EBIT target) Currency exchange rates Cost of goods Profit margins Prices

Marketing department Market share New product launches New business strategy Competitor data Product segmentation

Sales department Customer agreements Payment terms Customer segmentation EDI open orders Customer prices

Supply chain partners Supplier Availability of raw material Availability of preproducts and components

Customer Downstream channel inventory POS data Seasonality End customer consumption (product on ground)

Third party data Data provider Special events (e.g. bank holiday, strikes, etc.) Satellite data (farmland images) Weather Agrochemical/ agronomic expert studies Web logs on product specific websites Macro-economic indicator (oil price, industrial price index, consumer index, etc.) Sentiment data (social media and expert boards) Search trends (online search engines)

Table 6. IBP decision-making relevant data

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decisions. As stated in Table 5, high maturity countries with high performance, such as E1, E2 or N2, reported very high technology-related, cross-functional requirements (i.e. data access or cross-functional measurements) while medium performance countries, such as E3, E4 or N1, reported either very high data access or very high cross-functional measurements. Due to the fact that only cluster 3 countries reported on all three types of BDA solutions and demonstrated formally linked cross-functional processes (e.g. I16: “We continue to check our dashboards at the beginning of each meeting, but especially the reduced effort for manual forecasting and the decision support is very appreciated by our controllers”), the next proposition is as follows:

P2b. Greater maturity in S&OP processes requires more comprehensive BDA solutions to improve S&OP performance.

Figure 3 embeds all propositions into the research model.

Discussion The findings of the study have shown that to establish S&OP as a strategically important process in a company’s planning landscape and to ensure the recognition of S&OP by executives as IBP, the development of BDAC is inevitable. However, BDAC are not seen as a pure mechanism to improve S&OP performance but rather as an enabler which allows S&OP dimensions, such as meetings and collaboration, organization, performance measurement and IT, to evolve according to the prevailing maturity stage. This view corresponds to the S&OP study in a grocery retail context of Dreyer et al. (2018). They argue that IT solutions alone are not a sufficient driver to stimulate S&OP maturation. The study on hand confirms this finding but also argues vice versa that S&OP maturation reaches its limits at advanced stages if BDAC are not developed. BDAC as an enabler is necessary for IBP to have the ability to interact, while S&OP dimensions (e.g. meetings and collaboration) are reflecting processes in which emergent S&OP properties arise (Someh et al., 2019). Although previous studies have highlighted the relevancy of IT in general for advancing in S&OP (Jonsson and Holmstr€om, 2016; Danese et al., 2018; Kristensen and Jonsson, 2018), there was no emphasis on associated capabilities, which need to be developed. In naming

Figure 3. Research model with

propositions

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IT-related improvements, previous studies were mainly focusing on the tangible capability of activating new software or incorporating additional data for decision-making. While this is truly an important aspect, it misses the remaining human and intangible capabilities which were incorporated in this study. According to this study’s findings, tangible capabilities provide quick benefits for advanced forms of S&OP but do not ensure a sustainable, competitive advantage on their own. Therefore, the main targets of S&OP implementations, as internal cross-functional or external integration, were only achieved in some countries of the case company by emphasizing the mutual development of all three areas of BDAC. An example of this is the discussion in the literature on the importance of investments in information systems, which often raise the comparison of the usage of simple spreadsheets in early stages of S&OP against the utilization of advanced planning systems in mature stages of S&OP (Thom�e et al., 2012). However, even advanced planning systems are lacking in analytics or reporting functionalities for big data. Advanced planning systems are only one example of a BDA solution which is required for high S&OP maturity stages. The desire to have all S&OP-relevant data available and possess all BDA- related capabilities is accompanied by the prerequisite of established collaboration activities with SC partners to ensure trust and information sharing. The challenge of conflicting targets between business functions within an organization as well as between SC partners is one of the barriers for successful IBP implementations. Availability of data and the concomitant possibility of creating intelligence from the data for optimization purposes requires alignment on target setting. Within an organization, typical target conflicts between SC and sales departments about inventory reduction vs sales increases often result in individual optimization without any persuasion to change unless there is an incentive of a superior cross-functional business target. Similar examples exist in interorganizational relationships in the case study’s industry where full visibility of inventory or forecasts within the whole SC results in a negotiation of inventory ownership between customers and suppliers. Since capital costs fall to the inventory owner, full data transparency would lead to inventory optimization for the whole SC but could lead to increased costs for one of the SC partners. This means that, in spite of all of the advantages of IBP for an organization, there might be disadvantages for SC partners, leading to resistance to collaboration and information sharing.

Within the agrochemical industry, BDAC in the context of advanced forms of S&OP (i.e. IBP) is leading to uncertainty reduction on both sides, supply and demand. Due to its seasonality in demand, the focus in S&OP meetings is changing dynamically across the year in Northern and Southern Hemisphere geographies. While in-season S&OP meetings are characterized by operational and tactical aspects where BDA solutions are focusing on visibility and trade-off decisions for customer service, finished good production and transportation data, postseason S&OP meetings are characterized by tactical and strategic aspects. In these cases, BDA solutions are focusing on active ingredients supply and demand balancing, trend analysis, market intelligence or registration updates including not only stochastic but also abrupt forms of demand and supply uncertainty.

Conclusion and future research To implement IBP in an organization associated with effective and efficient decision-making, BDAC are indispensable. Based on the study’s findings, it can be concluded that there is no successful IBP implementation without BDAC. BDAC enable IBP implementations by reducing uncertainties originating in the environment through increased IPC. Tangible BDAC in particular are racing up a company’s IPC in an initial stage of S&OP implementation while human and intangible BDAC ensure that capabilities are sustained and a competitive advantage is achieved.

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The theoretical contribution of this investigation is the developed insights into the intersection of the two research streams of S&OP and BDA through an empirical study. Pedroso et al. (2016) reviewed S&OP enablers in the literature. The extensive list of enablers, which is based on numerous studies, does not contain BDAC. Therefore, the findings on hand extend the body of knowledge regarding enablers for implementations of advanced forms of S&OP (i.e. IBP). Moreover, this study is complementary to latest studies on S&OP-related mechanisms, which overlooked technological aspects and rather focused on processual, organizational and behavioral aspects (Goh and Eldridge, 2019). Additionally, the study on hand addresses the call of scholars for more empirical research on S&OP to complement the already available theoretical S&OP frameworks and maturity models with insights into practice in specific contexts (Jonsson and Holmstr€om, 2016).

The managerial contribution of this investigation is the guidance for practitioners about how to establish BDAC for the purpose of achieving successful S&OP. Beside the particular sequence of developing the specific capabilities, the study also provides manifold examples of BDA solutions related to S&OP. Due to the juvenility of BDAC and the associated time, financial resources and effort for its development, the study also delivers one explanation for the lack of studies on successful implementations of advanced forms of S&OP.

Although the study culminated in new insights into academia and practice, it is constrained by numerous limitations. First, the single case study research methodology is associated with a shortcoming of external validity. This calls for future research on quantitative cross-industry studies to examine the extent of the effect of BDAC on S&OP performance or ultimately on firm performance. Especially the impact of S&OP and BDAC on high vs low planning complexity would be of high interest. Second, the study is not covering social implications of S&OP implementation and BDAC development. The researchers noticed that BDAC has an influence. Scholars are encouraged to examine behavioral aspects of cross-functional collaboration driven by technological assistance, the consequences of BDAC development in S&OP on how coworkers interact with each other, changes in required job profiles and challenges or opportunities for training and communication in further studies. Third, empirical evidence regarding implementations of the highest S&OP maturity stages is scarce in academic research. The study sample does not solely demonstrate results of the highest maturity stage either. Instead, only three of the 11 sub-units of analysis have implemented IBP while the others are in an S&OP transition journey related to lower or medium maturity stages. Hence, these three sub-units of analysis within the case study are best practices, which need to be validated by further extreme cases with high S&OP maturity stages. The investigated relation between BDAC and S&OP is explained in one specific context within the agrochemical industry in a limited set of countries. In future, qualitative studies in different contexts as well as quantitative studies could provide beneficial insights.

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Appendix

Country Cluster 1 Cluster 2 Cluster 3

E1 0.0222 0.0879 0.8899 E2 0.0111 0.0995 0.8894 E3 0.0099 0.9676 0.0225 E4 0.0010 0.9957 0.0032 E5 0.9276 0.0457 0.0267 N1 0.0067 0.9615 0.0318 N2 0.0206 0.1589 0.8206 S1 0.0132 0.9011 0.0857 S2 0.9565 0.0300 0.0135 A1 0.2611 0.6055 0.1334 A2 0.9067 0.0616 0.0318

Figure A1. Agrochemical supply chain

Figure A2. Fit vs difference between performance and perfExpected

Table A1. Degree of membership from fuzzy C-means clustering

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Corresponding author Evi Hartmann can be contacted at: [email protected]

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Informant Excerpt of supportive quotes

I1 “Material requirements plans and distribution plans are triggered after every forecast change and show us directly if we reach a bottleneck somewhere.”

I2 “Predicting the top line is already an old story for us. We went further down the P&L and started to utilize predictive analytics for costs of goods sold, distribution costs, gross profit, gross margin, down to EBIT. Now we can do nice simulations.”

I3 “Taking the input from controlling and supply chain and adding market sizes, dose rates of our products and market share developments automatically, allows us to plan our strategic long-term forecast with less manual input.”

I5 “By considering all the different inputs for price management automatically we reduced a huge part of our manual analysis based on customers, products, spring vs autumn season, life cycle status, rebates . . .”

I6 “In the beginning it was a lot of effort to write down every decision for unhealthy batches but when I see that all the historical decisions are now used by the machine to tell us what to do, it’s amazing.”

I13 “In the past the meeting had a duration of one to two days. Since the change, we do it in two hours because we only talk about the important cases. The rest has already been done before .”

I16 “In the past I spent a whole day every month to bring together all data from different systems. Now I have it in one tool and can use this day for value-adding activities.”

. . . . . .

Informant Excerpt of supportive quotes

W1 “In the past we had to upload all data [. . .] every month manually and sometimes there were of course human errors. After IBP implementation we only edit where we have changes.”

I2 “We have less human errors because the tool shows an alert if any thresholds are exceeded as soon as the value is entered.”

W1 “We benchmarked the statistics versus the manual forecasts and saw accuracy increases for specific products especially in year two and three.”

I3 “The tool shows us directly the impact on profitability if we simulate changes of the substance factor or the dose rate. This is basically the communication bridge between marketing and controlling.”

I6 “Simulations on the fly are a big benefit for our executive demand review meetings.” W2 “Our decision-making is becoming more complex because we take into account more and more

influence factors. In the past it was the sales history plus some experience of managers.” I7 “When the distributer shares the channel inventory levels, we can provide much more accurate

forecasts.” I16 “With one platform we can finally talk about the same set of numbers and understand the

perspective and language of the other functions.” . . . . . .

Table A2. Improvements in

S&OP efficiency due to BDA

Table A3. Improvements in

S&OP effectiveness due to BDA

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