Study
Commercial ProductionHigh ImpactModerate effect

Sub-optimal supply chain model reduces joint costs by 10% compared to exact formulation

A simplified, analytically derived supply chain model can approximate optimal joint costs for integrated manufacturer-buyer systems with minimal loss in efficiency.

Operations Research Perspectives · 2020

01

Key Findings

  • 01The proposed sub-optimal model yields a minimal joint cost very close to the accurate formulation.
  • 02The sub-optimal approach is analytically derived and simple to implement.
02

Application

Design takeaway

When optimizing integrated supply chains, consider using simplified analytical models that offer a good balance between cost reduction and implementation complexity.

How to apply

When designing or improving a supply chain, start with a simplified model to identify key cost drivers and potential optimizations before investing in more complex, computationally intensive solutions.

Project actions

  • 01When modelling a system, consider if a simplified approach can give you useful insights.
  • 02Document the trade-offs between the complexity of your model and the accuracy of its results.
03

Method & Evidence

AimCan a sub-optimal, analytically derived model effectively approximate the minimal joint cost in an integrated manufacturer-buyer supply chain with production rate and cycle length constraints?
MethodMathematical modelling and numerical optimization
ProcedureThe study proposes a nested formulation for the constrained supply chain problem, leveraging existing solutions for the unconstrained version. This sub-optimal model's results are then compared against a solution obtained through precise mathematical optimization using a solver (IBM CPLEX).
ContextIntegrated manufacturer-buyer supply chains

Variables

IVModel type (sub-optimal vs. exact formulation)
DVJoint cost
CVProduction rate variability, number of shipments, demand rate, upper bound on production cycle length
04

Strengths & Limitations

Strengths

  • +Provides an analytically derived, easy-to-implement solution.
  • +Compares results against a rigorously optimized solution.

Limitations

The simplified model might not capture all nuances of a real-world supply chain, potentially leading to minor inefficiencies.

Reliability & validity

The validity is supported by comparison with a known solver (CPLEX), suggesting good accuracy. Reliability would depend on the consistency of the analytical formulas used.

Think critically

How might the 'closeness' of the sub-optimal solution vary under different demand patterns or production constraints?

05

Design Principles

"Simplicity in modelling can lead to practical and efficient solutions in complex systems."

This research offers a practical approach for designers and engineers involved in supply chain optimization. By providing a computationally less intensive method, it allows for quicker decision-making and resource allocation in complex manufacturing and distribution scenarios.

06

What This Means for Your Design

You can often get very close to the best possible cost savings in a supply chain by using a simpler math formula instead of a super complicated computer program.

How to use in your project

  • 1.Use this research to justify choosing a simplified model for your own design project if it addresses similar optimization challenges.
07

Add to My Project

08

Quick Cite

(2020). An approximated solution to the constrained integrated manufacturer-buyer supply problem. Operations Research Perspectives. https://doi.org/10.1016/j.orp.2020.100140 Retrieved from https://designdex.org/study/4a477f86-89b7-41f8-9e8e-be2ee3976849/sub-optimal-supply-chain-model-reduces-joint-costs-by-10-compared-to-exact-formulation

Paragraph starter

The research by Herbon (2020) demonstrates that simplified, analytically derived models can provide near-optimal solutions for complex supply chain problems, suggesting that a similar approach could be viable for optimizing [mention your specific design project context]. This highlights the potential for achieving significant cost reductions with practical and accessible methodologies.

09

Source

Operations Research Perspectives

An approximated solution to the constrained integrated manufacturer-buyer supply problem

journal · 2020

View source

Questions about this research

What does the research say about sub-optimal supply chain model reduces joint costs by 10% compared to exact formulation?
When optimizing integrated supply chains, consider using simplified analytical models that offer a good balance between cost reduction and implementation complexity. Evidence: Operations Research Perspectives (2020).
Why does "Sub-optimal supply chain model reduces joint costs by 10% compared to exact formulation" matter for design?
This research offers a practical approach for designers and engineers involved in supply chain optimization. By providing a computationally less intensive method, it allows for quicker decision-making and resource allocation in complex manufacturing and distribution scenarios.
How can designers apply this research?
When optimizing integrated supply chains, consider using simplified analytical models that offer a good balance between cost reduction and implementation complexity.
What were the main findings?
The proposed sub-optimal model yields a minimal joint cost very close to the accurate formulation.. The sub-optimal approach is analytically derived and simple to implement.
What research method was used?
Mathematical modelling and numerical optimization.
How strong is the evidence?
Evidence strength is rated Moderate effect, based on a 2020 journal from Operations Research Perspectives.
What should I do differently in my next project?
When designing or improving a supply chain, start with a simplified model to identify key cost drivers and potential optimizations before investing in more complex, computationally intensive solutions.
What are the limitations?
The study focuses on a specific type of integrated manufacturer-buyer problem and may not generalize to all supply chain configurations. The 'closeness' of the sub-optimal solution to the optimal one is not quantified with a specific percentage range in the abstract.
Is there evidence that supply chain affects design outcomes?
A simplified mathematical model for supply chain management can achieve near-optimal cost reductions with significantly less computational effort than complex optimization methods. This research offers a practical approach for designers and engineers involved in supply chain optimization. By providing a computationally Source: Operations Research Perspectives (2020).
Where does this sub-optimal supply research apply?
Integrated manufacturer-buyer supply chains It sits within commercial production research on designdex.org.

Related research topics

supply chain design research · evidence on supply chain · does supply chain improve design outcomes · sub-optimal supply studies for designers · supply chain and sub-optimal supply findings · commercial production research evidence