Hafez Shurrab

Manufacturer's planning guide to balancing demand and supply

​Matching customer demand with supply capacity is crucial for high manufacturing business performance. In his doctoral thesis, Hafez Shurrab highlights tactical planning and the challenges encountered in complex manufacturing environments in the quest of demand-supply balancing.

What challenges do you focus on in your research?

"Failing to balance demand and supply in manufacturing processes entails frequent swings between over- and undercapacity and consequently generates considerable financial losses. This is true for both engineer-to-order operations (ETO, for products requiring highly customized engineering such as ships, cranes or industrial facilities) and configured-to-order operations (CTO, for products consisting of countless combinations of standard systems, subsystems, and components, such as cars or electronic devices).  The reason is the constant pressure of substantial complexity, such as volatility, uncertainty and ambiguity. Manufacturers respond to such complexity by using planning processes that address the business’s needs and risks at various medium-term horizons, ranging from 3 months to 3 years."
 
"Because the importance of decision-making increases exponentially as the horizon shrinks, understanding the interaction between complexity and demand-supply balancing requires extending findings reported in the literature on operations and supply chain planning and control. Therefore, my thesis addresses complexity’s impact on planning medium-term demand-supply balancing on three horizons: the strategic– tactical interface, the tactical level, and the tactical–operational interface."

How do you address the problem with your research?

"To explore complexity’s impact on demand–supply balancing in planning processes, the thesis draws on five studies, the first two of which addressed customer order fulfillment in ETO operations. In the first study, I examined relevant tactical-level decisions, planning activities, and their interface with the complexity affecting demand–supply balancing at the strategic–tactical interface. The second study revealed the cross-functional mechanisms of integration affecting those decisions and activities and their impact on complexity. Next, the third study investigated areas of uncertainty, information-processing needs (IPNs), and information-processing mechanisms (IPMs) within sales and operations planning in ETO operations. By contrast, the studies that followed addressed material delivery schedules (MDSs) in CTO operations; whereas the fourth study identified complexity interactions causing MDS instability at the tactical–operational interface, the final study quantitatively explained how several factors affect MDS instability."

What are the main findings?

"The thesis contributes to theory and practice by extending knowledge about relationships between complexity and demand–supply balancing within a medium-term horizon. Its theoretical contributions, in building upon and supporting the limited knowledge on tactical planning in complex manufacturing operations, consist of a detailed tactical-level planning framework, identifying IPNs generated by uncertainty, pinpointing causal and moderating factors of MDS instability, and balancing complexity-reducing and complexity-absorbing strategies, cross-functional integrative mechanisms, IPMs, and dimensions of planning process quality."

"Meanwhile, its practical contributions consist of concise yet holistic descriptions of relationships between complexity in context and in demand-supply balancing. Manufacturers can readily capitalize on those descriptions to develop and implement context-appropriate tactical-level planning processes that enable efficient, informed, and effective decision-making."

What do you hope your research will lead to?

"I hope my results lead to increased research and industrial interest in developing tactical planning and related tools in a more integrated way so that decision-makers at various levels encounter fewer uncertainties about resource allocation and higher flexibility for problem-solving. Such conditions facilitate better use of resources and more effective investments. Examples of expected investments can be initiatives for improving the scalability of capacity downstream and flexibility in supply chain order fulfillment upstream and initiatives for developing predictive machine learning models for identifying and managing demand- and supply-related uncertainties."
 
 
Text compilation: Daniel Karlsson
 
 
The author will defend the thesis on 21 March 2022 at 13:15, see link on the thesis' page
 
More about Hafez Shurrab
 

Page manager Published: Mon 08 Aug 2022.