An Analytical Model for A-Frame System Design

A Literature review on

An Analytical Model for A-Frame System Design

Executive Summary

In this paper, we review the 2010 paper “An analytical model for A-frame system design”, by Jennifer A. Pazour and Russel D. Meller. Authors performed a systematic analysis to understand the design decisions of using an A- frame system in a distribution center. A math programming is used to determine the investments of a frame along with assignment and allocation of SKU’s. The authors considered one of the main constraints “Throughput” in this paper. In order to meet the throughput a heuristic based approach is used to perform adjustments so that the throughput is met. After design testing it is proved that A-frame has the greatest potential in labor savings when the DC has High item commonality, small order sizes and high skewness levels and in throughput in case of small orders, low item commonality and low skewness levels.

We analyze Caputo and pelagagge’s previous (2006) work on automated order picking systems, where they developed a decision support system and improved management criteria to reduce the need for manual decision-making based on subjective judgment. The authors analyzed the relationship between stochastic demand, setup parameters and performances by simulation and rules were defined to cost-effectively select the values of setup parameters. Then the decision support system was built based on heuristic rules to update the equipment setup. By utilizing this system it is proved that high level of picking automation efficiency is maintained while reducing the support operators, which improves the profit margins of high volume high rotation distribution centers.

Next we analyze previous (2008) work of Bartholdi and Hackman “Allocating space in a forward pick area of a distribution center”. The author’s main concentration was in a forward pick area. The authors extended the work of Hackman and Rosenblatt (1990) by analyzing two commonly used stocking strategies and are compared with optimal stocking strategy for small parts. The authors do not consider infrastructure costs and assumptions are made like fixed storage capacity, each replenishment have fixed replenishment time regardless of number of items. Their paper does not address the discreteness and different lengths of channels.

Then we turn to Liu, Zhou et al. paper (2008) “Slotting the Complex Automated Picking System in Tobacco Distribution Center”. In this work, the authors presented a comprehensive analysis of the general multi-tier, multi-mode inventory system of CAPS (Complex Automated Picking System) and restocking, picking activities of Horizontal Dispense and A-frame is analyzed. The optimal number of channels assigned to each SKU to minimize the restocking cost of HD is presented considering the safety stock. A three-tier, four-mode inventory system to identify the optimal proportion of picking buffer is developed for HD and A-frame. The equivalent two-tier, three-mode inventory system is analyzed and a practical Greedy Heuristic Strategy to slot the CAPS is developed. However they assumed all SKUs have same physical dimensions and pick costs, infrastructure costs and throughput are ignored