Themes for facilitating material flow in manufacturing systems.

Introduction

The term "new wave manufacturing" (NWM) strategies[1] has been introduced to embrace the radical new approaches to manufacturing pioneered by the japanese and packaged under such labels as lean production, just-in-time (JIT) and world class manufacturing. NWM strategies have essentially practical origins: their conceptual foundations have yet to be established[2,3]. Many pronouncements of the answer to material flow facilitation in manufacturing systems are only applicable to anecdotal circumstances. McCutcheon and Meredith[4] have referred to these as "war stories, ivory tower descriptions and black boxes". This article aims to identify and collect together NWM themes for facilitating the flow of materials by reviewing current literature, and to indicate implications to further research.

A review of the multitude of possible issues which have been identified as impacting on the flow of materials has here been organized into six themes. The first theme evaluates the effects of under-capacity loading; the second, the role of pull scheduling; third, the impact of variability reduction; fourth, the reduction of throughput time; fifth, the effects of reduced throughput time and schedule stability on outbound logistics; and sixth, the application of synchronous flow to in bound logistics. These themes were selected because the literature divided up reasonably logically in this way, and because the chosen themes describe key features of a transformation system (input/process/output sequence[5]). A conceptual model which relates these six themes to material flow in a manufacturing system -- encompassed in the boxed area -- is shown as Figure 1.

This article concludes by taking the stance that such a holistic view of material flow is necessary in order to establish the impact of NWM strategies on logistics. Too often, only a limited view is taken, and simplistic solutions emerge which apply, at best, to limited conditions. It is argued that case study research offers an appropriate methodology to investigate the complex interactions between the themes.

Capacity

Schonberger[6] describes one of his total quality control concepts as "less-than-full-capacity scheduling". This concept "helps assure the daily schedule will be met, avoids pressuring workers ... and so avoids errors in quality which could arise from haste". If things go well, the slack time can be used for team meetings, preventive maintenance, etc. Shingo[7] defines excess capacity as the percentage amount by which capacity exceeds load. He argues that all processes should have a positive excess capacity, otherwise variability such as that caused by overtime work will create timing problems.

Such thinking prompts two fundamental questions. The first is: surely a better approach is to level excess capacity, so that the required and potential capacities are equal? This minimizes the cost of excess capacity and improves return on investment. Oliver[8] points to the trade-off between idle materials (inventory) and idle machines. This would be a particular problem for capital intensive plants making use of very expensive machines that are in limited supply Hammesfahr et al.[9] state that "should excess capacity be provided as part of the planning process, the cost of products increases accordingly. The cost of excess capacity is largely uncontrollable once production begins". They blame over-capacity for loss of competitiveness in US manufacturing, and propose a two-facility approach to capacity planning: one designed to meet minimum demand, and the other -- with a capacity equal to the difference between minimum and maximum demand -- for flexibility.

A second question is posed by Lambrecht[10]. How can we guarantee flexibility and a fast response without the protection of inventory, as JIT asks us to do? JIT, he argues, is fairly good for stable production environments. But short lead times and fast response cannot be achieved together with high capacity utilization when demand uncertainty is high. In such an environment, excess capacity can be used to create "capacity slack", which can be used to reduce both lead time and inventory[11]. Capacity slack is then a strategic alternative to lead time. South[12] argues that it is necessary and economically desirable to acquire extra capacity to control queue size in a random environment.

Other authors take up further aspects of the capacity-inventory trade-off. South and Hixson[13] analyse simple examples of finished goods safety stock related to probability distribution for total demand. They conclude that opportunities to reduce finished goods stock should be sought whenever there is excess capacity. Building on this approach, Mapes[14] finds that, as capacity utilization approaches 100 per cent, substantial increases in safety stock are necessary in order to maintain customer service levels. Looking at work in process (WIP) rather than at finished goods inventory, Crandall and Burwell[15] modelled a flow shop with a fixed sequence of operations, and concluded that increases in product or process variability cause corresponding increases in WIP if reduction in throughput is to be avoided. Bassett[16] used queuing theory to come up with similar conclusions for a randomly routed job shop. An example of his waiting line model showing estimated waiting times is shown in Table I.

 
Table I. 
Average hours of delay 
as a function of 
capacity utilization in a 
randomly routed job shop 

 
Average percentage of capacity   Days of delay        Total job 
utilization                        in queues     completion (days) 

 
95                                     91.1              96.1 
80                                     16.0              21.0 
60                                      4.5               9.5 
50                                      3.0               8.0 
30                                     0.65               5.65 

 
Note: 
Assumptions: a three-stage job requiring one week of working time: 
task A requires two days of working time 
* task B requires one day of working time 
* task C requires two days of working time 
a simple single channel waiting line model to estimate waiting times 

He argues that queueless work flow achieved through reduced capacity utilization can create for the job shop all the advantages of JIT flow.

Conclusions

There is a distinct dichotomy between those who advocate excess capacity as necessary for flexible response, and those who urge caution because excess capacity is expensive and does not guarantee a fast response. Most of the hard evidence has come from simulation studies, and there 's a marked lack of practically-based research.

Scheduling

The scheduling method closely associated with NWM strategies is pull scheduling. Vollman et al.[17] define a pull system of material flow control as only authorizing a work centre to produce when it has been signalled that there is a need for more parts in a downstream (user) department. Movements and production are authorized by a signal (kanban) from a downstream work centre. The aim is to link all operations -- internal and external -- by "invisible conveyors".

A good deal of confusion has been apparent in the debate between materials requirements planning (MRP) "push" and JIT ("pull") scheduling. Simply put, New[18] sees the MRP focus (make sure everybody knows …