Different metrics for measuring and analyzing the productivity of manufacturing systems have been studied for several decades. The traditional metrics for measuring productivity were throughput andutilization rate, which only measure part of the performance of manufacturing equipment. But, they were not very helpful for “identifying the problems and underlying improvements needed to increase productivity”.
During the last years, several societal elements have raised the interest in analyze the phenomena underlying the identification of productive performance parameters as: capacity, production throughput, utilization, saturation, availability, quality, etc.
This rising interest has highlighted the need for more rigorously defined and acknowledged productivity metrics that allow to take into account a set of synthetic but important factors (availability, performance and quality). Most relevant causes identified in literature are:
- The growing attention devoted by the management to cost reduction approaches;
- The interest connected to successful eastern productions approaches, like Total Productive Maintenance, World Class Manufacturing or Lean production;
- The importance to go beyond the limits of traditional business management control system;
For this reasons, a variety of new performance concepts have been developed. The total productive maintenance (TPM) concept, launched by Seiichi Nakajima in the 1980s, has provided probably the most acknowledged and widespread quantitative metric for the measure of the productivity of any production equipment in a factory: the Overall Equipment Effectiveness (OEE). OEE is an appropriate measure for manufacturing organizations and it has being used broadly in manufacturing industry, typically to monitor and control the performance (time losses) of an equipment/work station within a production system.
The OEE allows to quantify and to assign all the time losses, that affect an equipment whilst the production, to three standard categories. Being standard and widely acknowledged, OEE has constituted a powerful tool for production systems performance benchmarking and characterization, as also the starting point for several analysis techniques, continuous improvement and research. Despite this widespread and relevance, the use of OEE presents limitations.
As a matter of fact, OEE focus is on the single equipment, yet the performance of a single equipment in a production system is generally influenced by the performance of other systems to which it is interconnected. The time losses propagation from a station to another may widely affect the performance of a single equipment.
Since OEE measures the performance of the equipment within the specific system, a low value of OEE for a given equipment can depend either on little performance of the equipment itself and/or time losses propagation due to other interconnected equipments of the system.
This issue has been widely investigated in literature through the introduction of a new metric: the Overall Equipment Effectiveness (OTE), that considers the whole production system as a whole. OTE embraces the performance losses of a production system both due to the equipments and their interactions.
Process Designers need usually to identify the number of each equipments necessary to realize each activity of the production process, considering the interaction and consequent time losses a priori. Hence, for a proper design of the system, we believe that the OEE provides designer with better information on each equipment than OTE.
In this chapter we will show how OEE can be used to carry out a correct equipments sizing and an effective production system design, taking into account both equipment time losses and their propagation throughout the whole production system.
In the first paragraph we will show the approach that a process designer should face when designing a new production system starting from scratch.
In the second paragraph we will investigate the typical time-losses that affect a production system, although are independent from the production system itself.
In the third part we will define all the internal time losses that need to be considered when assessing the OEE, along with the description of a set of critical factors related to OEE assessment, such as buffer-sizing and choice of the plant layout.
In the fourth paragraph we will show and quantify how time losses of a single equipment affects the whole system and vice-versa.
Finally, we will show through the simulation some real cases in which a process design have been fully completed, considering both equipment and time losses propagation.
Manufacturing system design: Establish the number of production machines
Each process designer, when starting the design of a new production system, must ensure that the number of equipments necessary to carry out a given process activity (e.g. metal milling) is sufficient to realize the required volume. Still, the designer must generally ensure that the minimum number of equipment is bought due to elevated investment costs.
Clearly, the performance inefficiencies and their propagation became critical, when the purchase of an extra (set of) equipment(s) is required to offset time losses propagation. From a price strategy perspective, the process designer is generally requested to assure the number of requested equipments is effectively the minimum possible for the requested volume. Any not necessary over-sizing results in an extra investment cost for the company, compromising the economical performance.
Typically, the general equation to assess the number of equipments needed to process a demand of products (D) within a total calendar time C t (usually one year) can be written as follow:
Table 3.1 Equation
- D is the number of products that must be produced;
- cti is theoretical cycle time for the equipment i to process a piece of product;
- Ct is the number of hours (or minutes) in one year.
- ϑ is a coefficient that includes all the external time losses that affect a production
- system, precluding production.
- η i is the efficiency of the equipment i within the system.
It is therefore possible to define Lt, Loading time, as the percentage of total calendar time C t that is actually scheduled for operation:
The Table 3.1 shows that the process designer must consider in his/her analysis three parameters unknown a priori, which influence dramatically the production system sizing and play a key role in the design of the system in order to realize the desired throughput. These parameters affect the total time available for production and the real time each equipment request to realize a piece1, and are respectively:
- External time losses, which are considered in the analysis with ϑ;
- The theoretical time cycle, which depends upon the selected equipment(s);
- The efficiency of the equipment which depends upon the selected equipments and their interactions, in accordance to the specific design.
This list highlights the complexity implicitly involved in a process design. Several forecasts and assumptions may be required. In this sense, it is a good practice to ensure that the ratio in Table 3.2 is always respected for each equipment:
Table 3.2 Equation
As a good practice, to ensure Table 3.2 being properly lower than 1 allows to embrace, among others, the variability and uncertainty implicitly embedded within the demand forecast.
In the next paragraph we will analyze the External time losses that must be considered during the design.