Apply framework in Vanderlande’s context

Step 1: Operationalize responsiveness for the given context

The first step of our framework is to operationalize; define responsiveness and determine how to measure this for the given context. We already performed this step in Chapter 2 by introducing the three KPIs and compute current performance based on six sample projects: orderline fill rate (57%, target ~100%), activity fill rate (90%, target 100%) and activity lead time (10.6 weeks, target 8 weeks). We also mention in Chapter 2 that in a project organization, the critical path determines the total project makespan. Since every orderline could potentially increase this project makespan, we measure KPIs on orderline level.

Step 2: Determine level of required responsiveness

In this second step, we first determine the level of required responsiveness on system level. We consider the four external requirements and match these with the five ETO characteristics of our literature study (Section 3.2). Table 4.1 shows this match. We see that all five ETO characteristics apply in Vanderlande’s context, which results in a high level of required responsiveness on system level.

Table 4.1 – Required responsiveness is high on system level

Vanderlande’s complete system: high level of required responsiveness

Exte rn al r eq u ir emen ts Demand uncertainty

High; Demand is uncertain until SPEC release; the CODP is located at the design stage

since Vanderlande offers tailor-made customer solutions (ETO characteristic 1). From that moment, SCC has eight weeks to deliver all items to the warehouse. Demand can change in the rolling horizon due to SPEC revisions or changes in planning.

Demand variability

High; The workload for the SCC is expressed in orderlines. Due to the project

environment (ETO characteristic 4), this workload is characterized by a high peak and long tail as shown in the Oslo example, appendix D.

Product variety

High; Engineering has the freedom to make every equipment project specific,

resulting in high level of customization (ETO characteristic 2) as we showed in the posisorter example in appendix C. Also NPIs and products with complex underlying BOM structures (ETO characteristic 3) raise uncertainty of this external requirement.

Lead time pressure

High; Vanderlande cannot buffer the customized items in finished good inventory,

which pressures the lead time performance of all echelons involved. Non-fixed site locations all over the globe (ETO characteristic 5) also create lead time pressure. However, as we just mentioned above, we measure KPIs on orderline level since every orderline can potentially increase the project makespan, causing project delay. Thus, responsiveness at Vanderlande applies not just on activities (system) but on orderline (item) level. Vanderlande’s system consists of two type of items: project specific items (specials, one-offs) and more standard items. Both item types have different product characteristics, resulting in different levels of uncertainty. We show this by matching both item types with the external requirements.

Figure 4.1 - Network redesign Table 4.2 – Required responsiveness is high for project specific items but low for standard items

Project specific items: high level of required responsiveness

Standard items: low level of required responsiveness Exte rn al r eq u ir emen ts Demand uncertainty

High; project specific items are

specified in SPEC release, so CODP is located at the design stage.

Low; demand can be aggregated

over multiple projects, enabling a reliable demand forecast.

Demand variability

High; no demand aggregation

possible, so demand variability is high.

Low; demand aggregation creates

a more stable workload, which lowers the demand variability.

Product variety

High; Project specific is

characterized by ‘high mix, low volume’.

Low; Standard items are

characterized by ‘low mix, high volume’.

Lead time pressure

High; High customization hinders a

buffer with finished good inventory, pressuring lead times.

Low; Forecast enables sourcing

before SPEC release and inventory management, lowering the lead time pressure.

This table shows that the level of required responsiveness differs on item level. However, currently all items receive almost the same treatment, irrespective if these items are critical project specific items or standard items used in multiple projects. However, these standard components can require a lower level of responsiveness. Due to the responsiveness trade-off which we explained in Section 2.1, these standard items better fit an efficient strategy. The annual sales increase in this growing organization offers the opportunity to benefit of economies of scale (EOS).

Step 3: Align potential with required responsiveness

We use this insight in the third step to redesign the network. These different levels of required responsiveness ask for two supply chain strategies, which we call ‘item level split’. Project specific items have a high level of required responsiveness and fit a responsive strategy. However, standard items have a low level of required responsiveness. Due to the responsiveness trade-off, an efficient strategy fits these items. We introduce two networks: the project specific network focusing on responsiveness, the EOS network focusing on efficiency by aggregating demand over all projects. This is in line with Chopra & Meindl’s (2016) strategic fit, stating that a tailored supply chain is required to be efficient when implied uncertainty is low and responsive when implied uncertainty is high. The item level split results in two networks:

The project specific network follows a responsive strategy and sources the so called ‘project specific items’. This network serves the ETO environment where the CODP is located at the design stage. The economies of scale (EOS) network follows an efficient strategy and sources what we call ‘EOS items’. The CODP is shifted downstream and demand is aggregated over multiple projects. In Chapter 5, we provide an item classification scheme to identify which items are suitable to become EOS items. We provide an illustrative example which we found in the book of Chopra & Meindl (2016). A company that successfully applied this item level split is fashion retailer Zara. They apply a responsive strategy for new season clothes (‘project specific items’) and an efficient strategy for basic clothes (‘EOS items’) and adjust the supply chain drivers that we showed in Section 2.1 accordingly. For example, Zara produces basic clothes in low-cost countries but new season clothes at local factories to ensure fast shipment to stores. This redesign allocates most uncertainty to the project specific network. Key is that this redesign provides strategic focus: GSC selects per item the best supply chain strategy based on the item characteristics. The goal of the project specific network is to be responsive towards projects: coordination still occurs by the current three SCCs. On the other hand, the goal of the EOS network is to realize economies of scale by aggregating item demand over all projects. They must ensure item availability against lowest costs. Since the EOS network aggregates demand, there is one global virtual stock points which can have different physical locations. EOS items can be sent via separate shipments to site or first be consolidated at the local warehouse.

In our network design, we deliberately make a clear separation of two networks. We believe that if every facility focuses on one strategy that fits item characteristics, Vanderlande can deliver their systems faster ánd more cost efficient while still designing customer-specific solutions. The project specific network enables the customer-specific solutions, whereas the EOS network can apply proper inventory management and realize economies of scale, thereby being faster and more cost efficient. In Chapter 5, we shows this statement with a mathematical model.