CONCLUSIONThe study clearly showed

CONCLUSION
The study clearly showed that managing stocks was a critical channel objective for firms seeking to optimize
customer value in the outbound distribution chain. Optimal stock levels must be maintained at all channel levels
especially for fast moving consumer goods companies. Where this is not feasible, then order-cycle times must be
minimized for faster stock replenishment. Companies must implement a strong stock-out policy in order not to
reduce gains made by stock build-up strategies. Deliveries must be perceived to be performed at fast speed even if it
only increases the customer’s value.
Therefore, this study recommends optimal increases in stock availability so as to avail adequate quantities of all
brands and packages to customers and to mitigate all stock outage situations. Reduce order cycle times to the bare
minimum of 1 day (same day) in order to increase stock replenishment speed, reduce delayed deliveries at all costs.
The recommendations may be implemented by paying attention on the behaviour of the study variables on sales.
Future researchers may generate a larger list of predictor and predicted variables through extensive literature review
and interviews with key managers in distribution and logistic functions. They can then model the problem using
structural equation modelling (SEM) techniques such as the use of LISREL models to continuously predict customer
value and make improvements in view of offering the highest value to customers (Henseler, et al.,, 2009). The
LISREL method estimates the unknown coefficients of a set of linear structural questions. It is particularly designed
to accommodate models that include latent variables, measurement errors in dependent and independent variables,
reciprocal causation, simultaneity, and interdependence. These are clear measurement problems in marketing and
business research. Implemented in LISREL 7 program, the method has special cases such as confirmatory factor
analysis, multiple regression analysis, econometric models for time-dependent data, recursive and non-recursive
models for cross-sectional and longitudinal data, and covariance structure models (Henseler, et al., 2009).