Increase redesign potential

Based on the formulas and numerical results of the model, we provide recommendations how to increase potential of the EOS network in terms of cost reductions and responsiveness performance. We explain why we expect increased potential and how to realize this. The recommendations relate to supplier collaborations, standardization efforts and warehouse consolidation.

Supplier collaborations

As shown in the sensitivity analysis of Section 6.2, δ is an important parameter. Supplier collaborations can realize this discount. Let us recall why we expect supplier benefits, as explained in Section 4.4. Demand aggregation allows Vanderlande to provide suppliers with a reliable and stable workload. They can optimize their production process and capacity planning accordingly. Also, bulk transport results in handling and transportation advantages. But Vanderlande can also ask suppliers to lower replenishment lead times when possible. Faster replenishment implies lower safety stock, and ultimately move more candidate items to class 5. We expect that it is realistic that more items can move to class 5, having a replenishment lead time lower than 7 weeks, since our model determines replenishment lead times based on historic performance. In this time period, suppliers were given 8 weeks to deliver their materials to the warehouse. We cannot see the capability of a supplier to deliver faster. We already described in Section 6.1 that this explains why only 9 of the 725 candidate items are class 5 items. Redesign is needed to obtain these benefits for suppliers: this allows Vanderlande to identify and select EOS items and offer the reliable forecast and baseload to suppliers. However, the inventory costs are now over 1 million euro. These costs can be reduced to a minimum when Vanderlande manages to shift candidate items to class 5. When SCC EOS selects their suppliers, they should therefore not only consider who provides the lowest discount, but also value their ability to deliver within 7 weeks.

Standardization efforts

The sensitivity analysis on item classification showed that more input of candidate items logically results in more savings. But when we fix the cut-off value on 52 orderlines per year, Vanderlande can increase the number of candidate items by standardization efforts. In our dataset, there were no such standardization efforts and we only evaluated project items of Vanderlande Equipment. A product that currently exists with different item numbers can be excluded from our model. For example: an item that currently exists in 6 different item numbers, each ordered 15 times a year for multiple projects, becomes class 2 items. However, if we standardize this to one item number, this item has 90 orderlines, making it a candidate item. Standardization efforts can be realized by collaboration with Engineering and R&D. Supply chain can provide Engineering insight in their drawing program in which items are EOS items, for example by colours. Introducing the EOS network and the EOS item terminology can help to create awareness. Another way to increase the number of candidate items is to include subcomponents of assemblies that Vanderlande’s factories purchase at second tier suppliers. We cannot see these subcomponents in our dataset since we only look at SCCs orderlines. Component commonality in assemblies provides the opportunity of product postponement. Postponement is the ability of a supply chain to delay product differentiation or customization until closer to the time the product is sold (Chopra & Meindl, 2016). Since second tier suppliers also influence the activity lead time (Figure 2.8), this suggest that lead times can be reduced. We include this as suggestion for further research.

Warehouse consolidation

In our model, EOS items are consolidated to a project activity in the warehouse (Figure 4.3), resulting in extra handling efforts of €6.25 per orderline. This consolidation was a requirement set by management. As showed in Section 6.1, this explains why savings or orderline reduction is less than expected. However, these savings can be increased with a different approach regarding shipment of EOS items to site to lower handling efforts. For example, this inventory can also be held and managed at the supplier, applying Vendor Managed Inventory (VMI). Or Vanderlande can ask suppliers to deliver in separate collies, which results in less handling efforts for the warehouse. However, suppliers do not benefit of transportation and handling advantages as mentioned in Section 4.4, which can imply a lower discount. Another approach is to not consolidate EOS items to activities, but just send one container with EOS items to site. This approach was suggested during an interview with a former site manager. As inspiration, we refer to the paper of Montreuil et al. (2012), who provide the breakthrough concept ‘Physical internet’. This is a metaphor of the digital internet, reshaping the real world where “physical objects are currently being moved, stored, realized, supplied and used in inefficient and unsustainable ways”. In Section 8.3 we provide suggestions for further research. Here, we also recommend to investigate which approach is best to store, consolidate and ship EOS items to site.

6.5. Conclusion

This chapter allows us to answer the fifth research question: “How does this redesign perform compared to the current network design?”

The numerical results of the model show that 633 of the 725 items provide positive savings, of which most savings occur due to savings in material cost. When we compare the current network with this redesign, we see that it reduces costs with 4 million euros and improves responsiveness in terms of orderline fill rate (raising from 57% to 78%) and activity fill rate (raising from 90% to 94%). However, average activity lead time remains 10.6 weeks since project specific items still increase the critical path. The model thereby quantified the impact of cost reductions and responsiveness performance.

Sensitivity analysis shows that the number of candidate items can be increased by include items of class 2 which are ordered more than once per two weeks. We include twice as much items in the model, but savings only rise with 1.3 million, showing that our model already selects most promising items. Sensitivity analysis also shows that discount has most impact on total savings. We emphasize that supplier collaborations are important, not only to obtain discount and thereby raise savings of material cost, but also to lower replenishment lead times to reduce inventory costs. This could further increase the potential of this redesign.