Fundamental behaviour of Virtual-Build-to-Order systems

Virtual-Build-to-Order (VBTO) is an emerging order fulfilment system within the automotive sector that is intended to improve fulfilment performance by taking advantage of integrated information systems. The primary innovation in VBTO systems is the ability to make available all unsold products that are in the production pipeline to all customers. In a conventional system the pipeline is inaccessible and a customer can be fulfilled by a product from stock or having a product Built-to-Order (BTO), whereas in a VBTO system a customer can be fulfilled by a product from stock, by being allocated a product in the pipeline, or by a build-to-order product. Simulation is used to investigate and profile the fundamental behaviour of the basic VBTO system and to compare it to a Conventional system. A predictive relationship is identified, between the proportions of customers fulfilled through each mechanism and the ratio of product variety / pipeline length. The simulations reveal that a VBTO system exhibits inherent behaviour that alters the stock mix and levels, leading to stock levels being higher than in an equivalent conventional system at certain variety / pipeline ratios. The results have implications for the design and management of order fulfilment systems in sectors such as automotive where VBTO is a viable operational model.

Intelligent pipeline control - a simulation study in the automotive sector

Automotive producers are aiming to make their order fulfilment processes more flexible. Opening the pipeline of planned products for dynamic allocation to dealers/ customers is a significant step to be more flexible but the behaviour of such Virtual-Build-To-Order systems are complex to predict and their performance varies significantly as product variety levels change. This study investigates the potential for intelligent control of the pipeline feed, taking into account the current status of inventory (level and mix) and of the volume and mix of unsold products in the planning pipeline, as well as the demand profile. Five ‘intelligent’ methods for selecting the next product to be planned into the production pipeline are analysed using a discrete event simulation model and compared to the unintelligent random feed. The methods are tested under two conditions, firstly when customers must be fulfilled with the exact product they request, and secondly when customers trade-off a shorter waiting time for compromise in specification. The two forms of customer behaviour have a substantial impact on the performance of the methods and there are also significant differences between the methods themselves. When the producer has an accurate model of customer demand, methods that attempt to harmonise the mix in the system to the demand distribution are superior.