An interpretive analysis of influential drivers for control tower adoption in supply chains

A Supply Chain Control Tower (SCCT) provides real-time information, analytics, and decision support for supply chain management, helping organizations manage disruptions and inefficiencies before they occur. The complexity of contemporary supply chains is characterized by various influential factors that significantly affect the performance of Supply Chain Control Towers (SCCT). Interpreting the interactions among these factors is the key for supply chain managers in their efforts to improve decision quality and performance. Factor analysis is used to identify, prioritize, and rank the influential success factors that help accomplish SCCT effectiveness. This study investigates the influential drivers that shape SCCT adoption by applying Total Interpretive Structural Modeling (TISM) to evaluate how they relate and MICMAC (Matrice d’Impacts Croisés Multiplication Appliquée à un Classement) analysis to determine their relative importance. The results illustrate that SC visibility and transparency are the principal factors, while the sustainable growth strategy is the least important factor influencing SCCT. This study delivers valuable practical understanding to supply chain managers regarding expediting efforts and effectively applying SCCT, ultimately boosting supply chain performance. • Identify key success factors for control tower implementation in supply chains. • Analyze factor relationships using interpretive structural modeling methods. • Highlight visibility and transparency as the most influential control tower driver. • Redevelop a hierarchical model to guide control tower deployment strategies. • Provide practical insights for enhancing decision-making in supply chain operations.

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Publication Details

Journal
Supply Chain Analytics
Published
2025-11-20
DOI
https://doi.org/10.1016/j.sca.2025.100178
Primary Topic
Collaboration in agile enterprises
Type
article
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article

An interpretive analysis of influential drivers for control tower adoption in supply chains

Manikam Michael Nadar, Angappa Gunasekaran, Vaibhav S. Narwane
Supply Chain Analytics
Collaboration in agile enterprises
article

An interpretive analysis of influential drivers for control tower adoption in supply chains

Manikam Michael Nadar, Angappa Gunasekaran, Vaibhav S. Narwane
article en

Abstract

A Supply Chain Control Tower (SCCT) provides real-time information, analytics, and decision support for supply chain management, helping organizations manage disruptions and inefficiencies before they occur. The complexity of contemporary supply chains is characterized by various influential factors that significantly affect the performance of Supply Chain Control Towers (SCCT). Interpreting the interactions among these factors is the key for supply chain managers in their efforts to improve decision quality and performance. Factor analysis is used to identify, prioritize, and rank the influential success factors that help accomplish SCCT effectiveness. This study investigates the influential drivers that shape SCCT adoption by applying Total Interpretive Structural Modeling (TISM) to evaluate how they relate and MICMAC (Matrice d’Impacts Croisés Multiplication Appliquée à un Classement) analysis to determine their relative importance. The results illustrate that SC visibility and transparency are the principal factors, while the sustainable growth strategy is the least important factor influencing SCCT. This study delivers valuable practical understanding to supply chain managers regarding expediting efforts and effectively applying SCCT, ultimately boosting supply chain performance. • Identify key success factors for control tower implementation in supply chains. • Analyze factor relationships using interpretive structural modeling methods. • Highlight visibility and transparency as the most influential control tower driver. • Redevelop a hierarchical model to guide control tower deployment strategies. • Provide practical insights for enhancing decision-making in supply chain operations.

Supply Chain AnalyticsVol. 13
Pennsylvania Department of Agriculture (US), K. J. Somaiya Hospital & Research Centre (IN), K J Somaiya Medical College (IN)
Openalex Percentile: Top 52%
Collaboration in agile enterprises
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An interpretive analysis of influential drivers for control tower adoption in supply chains — Manikam Michael Nadar, Angappa Gunasekaran, et al. · Supply Chain Analytics (2025) | TGRS Research Map | TGRS