AbstractThe thesis deals with the background, development and description of a
mathematical stock control methodology for use within an oil and chemical
blending company, where demand and replenishment lead-times are generally
The stock control model proper relies on, as input, adaptive forecasts of
demand determined for an economical forecast/replenishment period
precalculated on an individual stock-item basis. The control procedure is principally that of the continuous review, reorder level type, where the reorder level and reorder quantity 'float', that is, each changes in accordance with changes in demand.
Two versions of the Methodology are presented; a cost minimisation version
and a service level version.
Realising the importance of demand forecasts, four recognised variations of the Trigg and Leach adaptive forecasting routine are examined. A fifth variation, developed, is proposed as part of the stock control methodology.
The results of testing the cost minimisation version of the Methodology with
historical data, by means of a computerised simulation, are presented
together with a description of the simulation used. The performance of
the Methodology is in addition compared favourably to a rule-of-thumb
approach considered by the Company as an interim solution for reducing
The contribution of the work to the field of scientific stock control is felt to be significant for the following reasons:-
(I) The Methodology is designed specifically for use with non-stationary demand and for this reason alone appears to be unique.
(2) The Methodology is unique in its approach and the cost-minimisation
version is shown to work successfully with the demand data presented.
(3) The Methodology and the thesis as a whole fill an important gap between complex mathematical stock control theory and practical application.
A brief description of a computerised order processing/stock monitoring
system, designed and implemented as a pre-requisite for the Methodology's
practical operation, is presented as an appendix.
|Date of Award||1982|
|Supervisor||David A. Scrimshire (Supervisor) & John Edwards (Supervisor)|
- inventory control
- non-stationary demand
- adaptive forecasting