Search results

The purpose of this paper is to show that intentional demand forecast bias can improve warehouse capacity planning and labour efficiency. It presents an empirical methodology to detect and implement forecast bias.

A forecast model integrates historical demand information and expert forecasts to support active bias management. A non-linear relationship between labour productivity and forecast bias is employed to optimise efficiency. The business analytic methods are illustrated by a case study in a consumer electronics warehouse, supplemented by a survey among 30 warehouses.

Results indicate that warehouse management systematically over-forecasts order sizes. The case study shows that optimal bias for picking and loading is 30-70 per cent with efficiency gains of 5-10 per cent, whereas the labour-intensive packing stage does not benefit from bias. The survey results confirm productivity effects of forecast bias.

Warehouse managers can apply the methodology in their own situation if they systematically register demand forecasts, actual order sizes and labour productivity per warehouse stage. Application is illustrated for a single warehouse, and studies for alternative product categories and labour processes are of interest.

Intentional forecast bias can lead to smoother workflows in warehouses and thus result in higher labour efficiency. Required data include historical data on demand forecasts, order sizes and labour productivity. Implementation depends on labour hiring strategies and cost structures.

Operational data support evidence-based warehouse labour management. The case study validates earlier conceptual studies based on artificial data.

The purpose of this paper is to consider the use of temporary storage offered by intermodal transshipment points to position some stock of fast moving consumer goods in advance of demand; this floating stock concept combines transport and inventory management. Intermodal transport is compared with direct road transport for a supply chain.

First an analytical comparison is made which shows that the floating stock concept has advantages in inventories over pure road and intermodal transport. Next, a simulation study of a real case is made which quantifies the cost‐differences in detail.

It is found that both storage costs can be lowered and shorter response times be gotten by sending shipments in advance to intermodal terminals. The advance positioning can offset the disadvantage of a longer transit time in intermodal transport.

Demand needs to be somewhat predictable. The pooling effects depend on geographical layout of the customers. The availability of intermodal transport options is based on the situation in Western Europe.

The floating stock concept considers both the transport and inventory issues. By positioning some of the stock at transshipment points close to the customer in anticipation of demand, the concept can yield lower inventory costs as well as a lower customer lead time. The benefit for logistics service providers is a more regular supply chain. Using intermodal transport provides an opportunity to green the supply chain as the environmental impact per ton/kilometer is lower than road transport.

This paper draws on the areas of logistics and inventory management to consider the choice of transport mode; most studies look at these issues in isolation. Considering the holding and storage costs in addition to the distribution strategy enables a more thorough comparison of the transport modes.