Food Supply Chain

Supply chain integration

Over the last decade, different instruments and approaches to enhance supply chain

integration have been investigated, mainly looking at the impact of supply chain

integration on performance. So far, the influence of information systems (e.g. Vickery

et al., 2003), the influence of operational practices (e.g. Frohlich and Westbrook, 2001),

and the effect of simplifying the materials flow (e.g. Childerhouse and Towill, 2002)

have been investigated. Others stress the development and implementation of specific

tools, such as vendor managed inventory (VMI), collaborative planning, forecasting

and replenishment (CPFR), radio frequency identification (RFID), or bar-coding in a

supply chain context. The above and similar studies add to our knowledge and

understanding of what can be achieved and how performance can be improved. Most

of the published studies fail to address the business conditions or context of a supply

chain (Ho et al., 2002). Ramdas and Spekman (2000) are among the few that

investigated the influence of factors such as availability of substitutes, changes in

market conditions, changes in technology, market maturity, and product life cycle in

order to distinguish between functional and innovative products. As such, they add to

the work of Fisher (1997) who argues that innovative products can be associated with

high levels of uncertainty and need responsive supply chains, while functional

products need efficient supply chains.

So far, specifically the influence of uncertainty in demand on supply chain

management and integration has been explored. To Lee (2002), uncertainty is one of the

drivers for supply chain integration. Empirical evidence also indicates that the level of

uncertainty influences the level of integration (Davis, 1993; Childerhouse and Towill,

2002). Recently, Van Donk and Van der Vaart (2004) measure operational

characteristics that influence supply chain integration, labelled as business

conditions: the decoupling point (MTO/MTS), time window for delivery,

volume-variety characteristics, process type (batch size, set-ups, and routings), and

order-winners. In line with Davis (1993), these factors are important indicators for the

amount of uncertainty manufacturers are facing in their production planning and

delivery schedules. Van Donk and Van der Vaart (2004) distinguish between simple

(high volume, low product variety, large batches, make-to-stock, and costs as a major

order-winner) and complex (low volume, high product variety, small batches,

make-to-order, and flexibility among the main order-winners) business conditions.

Complex conditions correspond with a high level of uncertainty within the supply

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