Segmenting large supply chains into lean and agile segments has become a powerful strategy allowing companies to manage different market demands effectively. A current stream of research into supply chain segmentation proposes demand volume and variability as the key segmentation criteria. This literature adequately justifies these criteria and analyses the benefits of segmentation. However, current work fails to provide approaches for allocating products to segments which go beyond simple rules of thumb, such as 80-20 Pareto rules. We propose a joint network and safety stock optimisation model which optimally allocates Stock Keeping Units (SKUs) to segments. We use this model, populated both with synthetic data and data from a real case study and demonstrate that this approach significantly improves cost when compared to using simple rules of thumb alone.

The key value proposition of supply chain segmentation is to differentiate supply chains through a reasonable number of segments in order to gain a level of standardisation and avoid managerial complexity incurred in fully customised supply chains. The decision on how products are grouped into segments is at the core of a successful implementation. A fundamental trade-off in this decision-making process is between higher differentiation by having small group sizes and higher standardisation from a smaller number of groups. In this manuscript, we implement segmentation on supply chain configurations and investigate the trade-off by analysing several network scenarios. We use optimisation models for each scenario to align decisions of segment formation and supply chain configurations. We show that divergences in demand characteristics, geographic difference, and cost synergy such as pooling effect have impacts on the balance of standardisation and differentiation.

Aquaculture is identified as one of the critical food supplies in Malaysia. Due to the increasing demand for aquaculture products, the demand for protein sources for fish feed is also increased accordingly. Black soldier fly larvae is identified as one of the main protein sources that can be used in fish feed. Such larvae can be grown using different types of organic materials, such as food waste, agriculture waste, etc. As Malaysia is the second-largest palm oil producer in the world, therefore, a large number of agricultural wastes, also known as palm-based biomass (e.g., empty fruit bunches, mesocarp fibre, decanter cake, etc.) are generated annually. Based on the current industry practise, palm-based biomass can be converted into value-added products. However, using palm-based biomass as feedback to grow black soldier fly larvae is a relatively recent discovery. Thus, a viable supply chain model has yet to be established. In this work, a mathematical optimisation model is developed via commercial optimisation software (Lingo v. 16) to synthesise an optimum black soldier fly-based aquaculture feed supply chain that utilised palm–based biomass as the feedstock. Based on the optimised result, the annual operating cost of the aquaculture feed supply chain is estimated as RM 5.2 million.