Process Planning, Scheduling and Control for One-of-a-Kind Production
5.2 Literature Review
The other point discussed in the two papers [5.14] and [5.15], is the central importance of customer co-design. This is beyond the scope of this chapter but the reader should be aware of the importance that Gienow places on this co-design. It should also be appreciated what this co-design means in practice. Both authors mention the term “within the solution space”; the Gienow system implements this concept by using a one-of-a-kind production system. By this it is meant that Gienow offers a number of products and allows their customer through a computer interface to specify the variations in a product they require. This co-design process creates a one-of-a-kind product that the customer is entirely satisfied with. Gienow then produces the variation product at the same cost as a stocked standard product.
Gienow’s customers assume that quality is built into products. For a period of time the system did create a competitive advantage on price and flexibility, but what became important to customers was the on-time delivery that was achieved with the system. In future, service will become the dominant factor and the requirement to know what customisation was done to each product will be important in keeping service costs down.
Vandaele and De Boeck, in their paper on advanced resource planning [5.16], discussed the interdependency between material and resource planning and the conflict between resource utilisation and customer satisfaction. They defined lead time as a measurement of demand versus capacity, where lead time is asymmetric with a bias to the right. As interruptions occur in processing, so lead times are extended and customer dissatisfaction increases. As utilisation decreases, lead time increases in a nonlinear manner. Factors affecting utilisation include demand (quantity and time), capacity (processing times, setup times, number of resources, shift patterns), and outsourcing capabilities. These factors can be influenced by lot sizes, sequencing and release mechanisms. Nonlinear effects of utilisation on lead time are further affected when the average parameters from demand and capacity are not deterministic; they have a stochastic nature. Variability in product mix, operations and management decisions also has an impact on the factors. This is the case in OKP where each product is one-of-a-kind, varying in materials, resource usage and operations. They proposed the use of an advanced planning system that would account for variability in these factors and would determine optimum lead times, resource usages, lot sizes and sequence of setups.
In contrast to this, the application developed at Gienow was based on running the OKP system with lot sizes of one (because of the one-of-a-kind nature of the product) and the use of CNC-controlled equipment that would effectively reduce setup times to zero. The application then planned, scheduled and controlled the production process based on releasing orders in groups, while creating a fixed production sequence. The net result was that orders were released according to available capacity and in accordance with customer requirements. Required capacity was determined in advance and resources allocated accordingly. As processes occurred, future schedules needed to adjust as the result of interruptions caused by material delays and machine downtime.
In designing both product and process for OKP where variations on a product provide customisation, it is important to decide whether to build up the variation from a base product or to scale down from a full option set [5.17]. In Gienow’s case, all processes including data generation start from basic products that provide the
minimum requirements of a window or a door. Options are then selected by the customer to meet both functional and technical requirements.
Fujimoto and Ahmed [5.18] discussed the need to consider both process and product in order to provide customised items at a reasonable cost using economies of scale. Variety is caused by differences in basic functions (thermal properties of windows), adaptability requirements (different size and shape of windows), optional functions (windows that open), and non-functional requirements (welded frame or screwed frame). Variety impacts the manufacturing process in a number of ways;
high inventories, feeding complexities, excessive capital investment, change in assembly sequence and complexity in line balancing. Assuming that the FMS has been designed with both these considerations in mind, the processes of planning, scheduling and control are impacted and as a result are not the same as conventional processes.
In recognising the complexity of scheduling for an automated assembly system constructed in the various forms of flow lines and cell groups, Little and Hemmings [5.19] propose the use of a simulator that is “run-ahead” and provides an analysis of what can happen when production starts. In their conclusion, they indicate that the delivery of sub-assemblies and the organisation of components in the correct sequence is a major problem for these systems, particularly when the main orders are to be supplied in a JIT-type manner to the customers. This is addressed in the case study for this chapter.
Zhang et al. [5.20] propose the use of Petri nets for scheduling flexible systems.
The authors identify some of the advantages as being a formal model to define the internal relationships of discrete-event processes, identification of constraints and monitoring the current state of the production system. Even though the focus is on products that do not have a predetermined sequence of assembly, their approach of using a time-based Petri net to determine the production time can be used to determine the production time of a predetermined sequence and determine if there are any constraints likely to occur in the proposed schedule.
Anderson [5.21] recognises that product mix has a reduction effect on available capacity. In his paper, he proposes that human management (discretionary capacity management) of product mix increases the reduction effect on capacity when there are interruptions in the schedule. The implication in the conclusion is that discretionary capacity management is detrimental to throughput, and conversely any computer-aided assistance with capacity planning for product-mix variation should have a positive effect.
With regard to the impact of interruptions on scheduling, Foley [5.22]
recommends a number of alternatives – adjusting the schedule, recalculating the schedule or absorbing the interruption in the current schedule. In adjusting or recalculating the schedule, a hierarchical computer system is proposed that would determine which appropriate products should be produced next.
One interesting conclusion from the results of their experiment was: “The implication is that, at the occurrence of an interruption at the high load level, simple myopic modifications to the predetermined schedule may end up leading to a much worse performance than continuing to rigidly follow it.” From this we conclude that since Gienow lines are more often loaded to high levels, when an interruption occurs, the current schedule should not be modified. Subsequent schedules would be
modified to help absorb the interruption, according to how large the interruption is.
This creates the requirement to provide information on the performance of each production line and to show the impact of any interruption on the lines. Because of the type of product mix for orders and the fact that Gienow is using a FMS, the total plant schedule has to be considered.
The reference to the theory of constraints [5.23] is important, because the impact of interruption at a bottleneck is far greater than at a point that is not a bottleneck.
Therefore, to know where the potential bottlenecks are in a schedule would be useful information to management in the event of an interruption. A simulation should provide this information as mentioned before.
In discussing the impact of the interruption on a system that has a rigid schedule (as in the case of Gienow), the observation was made that the impact of the interruption would propagate quickly down the rest of the line. To some extent this is mitigated by designing the system as a one-piece flow. For some types of interruptions it will be possible to put the interrupted product aside and carry on with the rest of the schedule until the problem has been expedited. This has led to the development of some functions in the application to assist with the control of these types of interruptions.
According to Bielli and Dell’olmo [5.24] to solve a large discrete-event stochastic control problem it is necessary to integrate a number of strategies. In their paper on the IS-OPTIMUS system, Palacios et al. [5.25] focused their research on material-cutting optimisation, static scheduling and dynamic rescheduling after production conditions change. The system accounts for product mix and short lead times. In comparison, the majority of CNC cutting equipment at Gienow contains optimisation routines and therefore Gienow is able to concentrate on the development of a schedule, and the control and monitoring of the performance of the schedule. It also provides information that helps new schedules to consider the changing production conditions. In addition, the proposed simulator will also provide useful information to the scheduling process.
Matsui et al. [5.26] discussed the size of buffers, considering the differences between finite and infinite buffers. The basis of Gienow’s production system is one- piece flow. However, since the cutting optimisation capability of the machines is used by Gienow, some of the buffer sizes are greater than 1. Depending on the cutting machine and the size of the material being moved, these buffers are set at 25, 50 or 100. The paper also compared fixed and dynamic routing and concluded that if machine loading is balanced then throughput with fixed routing is almost the same as with dynamic routing. It is possibly more economical because fixed routings are simpler to manage and control. In the case study, the production process uses fixed routings but they do have multiple paths because of the product mix caused by different options selected by the customers.
The impact of sequence changes caused by material shortages, outsourcing delays and machine downtimes was considered when developing the system. Dupon et al. [5.27] researched this issue and concluded that sequence changes would not materially affect production time, but would adversely affect lead time to the customer. As a consequence, consideration was given in the application to provide information that would help management expedite issues. Accordingly, lead time delays are kept to a minimum. Similar to the conclusion in the paper, management at
Gienow emphasises the application of the FIFO rule in expediting issues and this helps maintain the level of customer satisfaction.
In his paper, Miltenburg [5.28] recognised the requirement to solve the two problems of model sequencing and line balancing for a FMS running in JIT mode.
JIT is a pull system and the customer sets the main sequence of production, thus setting the sequence required for other facilities. Miltenburg proposes a genetic algorithm to determine the sequence for optimum model production and line balancing. The problem at Gienow, with largely fixed processing work cells, emphasises the challenge of determining the optimum model sequence for the main lines, while levelling the production of the ancillary facilities. This will be discussed later in the chapter.
Much more has been written on these subjects. The following sections describe the results of similar research and the development of a production planning, scheduling and control application.