An adaptive framework for aligning (re)planning decisions on supply chain strategy, design, tactics, and operations

Abstract

Over the last decade, a wealth of valuable approaches to supply chain strategic, tactical, and operational planning has been extensively developed. However, conventionally the planning decisions at each of these levels have been considered in isolation from the other levels. Moreover, decisions on supply chain strategy, design, tactics, and operations are interlinked and dispersed over different supply chain structures (functional, organisational, informational, financial, etc.). This study develops a framework to increase the efficiency, consistency, implacability, and sustainability of decisions on how to design, plan, and run supply chains. In this paper it is proposed that comprehensive planning as an adaptive process encapsulates the planning decisions to be interrelated at all the decision-making levels. A conceptual model is described interlinking the supply chain strategy, design, planning, and operations on adaptation principles. Subsequently, a mathematical modelling complex and its realisation in a software environment is presented. This study contributes to developing methodical basics and practical tools to transit from simple open time slots incremental planning to dynamic, feedback-based adaptive supply chain planning. The elaborated framework serves to increase the efficiency, consistency, implacability, and sustainability of decisions on supply chain strategy, design, tactics, and operations. Decisions alignment can help to increase the wealth of organisations by producing demand-corresponding products in the most cost-effective manner through increasing agility, responsiveness, reaction speed to market changes, and continuous comprehensive improvements/adaptation of supply chains.

Keywords:

• supply chain management
• decision-making
• planning
• adaptation

AMS Subject Classification::

• 90B50
• 93C40, 41
• 93B52

Acknowledgements

The research described in this paper is partially supported by grants from the Russian Foundation for Basic Research (grant 09-07-00066) and the Deutsche Forschungsgemeinschaft (German Research Foundation) PAK 196 ‘Competence Cell-based Production Networks’, subproject P6 'Automated Running of Production Networks’. We thank the anonymous referees for their valuable suggestions to improve the paper.