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How To Ensure the Optimal Supplier Mix for Feedstock Cost, Quality and Reliability

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How To Ensure the Optimal Supplier Mix for Feedstock Cost, Quality and Reliability?

a.k.a “A decision support system for supplier selection and order allocation in stochastic, multi-stakeholder and multi-criteria environments”.

Recommendation

Feedstock procurement practices can make or break a biomass or biofuel project. Good procurement managers buy feedstock from a mix of suppliers, juggling economics, quality, and supplier reliability. However, the mix of suppliers is often done at the procurement manager’s discretion relying on “gut instinct”.

The “gut” approach can lead to sub-optimal results in terms of cost, quality and reliability. For example, cost may be low but there may be large variances in feedstock quality or “gaps” in reliability. The optimal balance of lower feedstock cost, better quality and greater reliability may be achieved by following the methodology proposed in this paper.

This is a technical paper, however it would be useful to feedstock procurement managers, bio-industry executives, as well as bio-industry investors.

In this summary, you will learn

• A new advanced computer-based methodology to select suppliers and optimally allocate orders between them.

Summary

Integrated supplier selection and order allocation is an important decision for both designing and operating supply chains. As firms continue to seek competitive advantage through supply chain design and operations they aim to create optimized supply chains. These are called “blending problems” and there is a great deal of research helping companies deal with them.

This paper helps solve supplier selection problems where multiple suppliers need to be selected each having different costs, quantities, qualities, and reliability. How do we get to the optimal solution by balancing cost, reliability, quality and risk?

The methodology uses a combined Analytic Hierarchy Process – Quality Function Deployment (AHP-QFD) and chance constrained optimization algorithm approach that selects appropriate suppliers and allocates orders optimally between them. The results can be validated by a Monte Carlo Analysis enabling bio-projects to rapidly understand and impact of different supplier mixes in the supply portfolio. The model gives an accurate delivered price & risk for different feedstock “blends”.

The effectiveness of the proposed decision support system has been demonstrated through application and validation in the bioenergy industry.

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