inactivity (e.g. automatic purging of granulate in injection molding after inactivity), an idling state needs to be modelled.
Machine breakdown intervals: machine error states are associated with higher material waste rates than idle states due to the unplanned deviation of process parameters. Thus, it is necessary to differentiate between idling and breakdowns, as Haag does for energy consumption.
Material aging interval: Material loss due to inventory deterioration has been ignored in resource efficiency simulations, since the machine is not directly responsible. Since inventory deterioration has been modelled in other applications as a function of fixed or variable shelf life, exceeding the shelf life of a product could be modelled as a peripheral event, or the activity of inspecting inventory shelf-life can be modelled as a peripheral activity. To model a variable shelf life, the storage location or ambient conditions may need to be modelled.
Overall, representing machine behavior with a set of operating states and their transitions covers a number of the material waste causing activities, but frequently not in adequate detail. For that reason, this approach must be supplemented by the modelling of peripheral activities and by varying the waste rates or waste quantities of an operating state depending on a number of conditions, which will be described in the next section.
Other factors affect the quantity of material waste per activity. Each of the four types are discussed in the next section.
5.3.1 Activity-Triggering Factors
One way the described influence factors from the Ishikawa diagrams influence the aggregate material waste in the factory is by triggering material waste-causing activities.
Examples from the influence factors discussed in 5.1 that influence the frequency of activities and events are briefly described below.
Disposal policy: Internal and external policies dictate in which intervals or under which conditions materials are discarded, e.g. if paint remainders or half-cut stock sheets can be saved for future jobs. This influences the amount of waste occurring during stock piece replenishment and in setup procedures.
Dispatch policy: The rhythm and sequence in which orders and material are released for processing, e.g. in large batches, determines how frequently undesirable machine states (e.g. idling, setups) occur.
Employee motivation: Employees can trigger machine idling or machine breakdowns through their absence or negligence, depending on their role in the manufacturing process.
Housekeeping policy: Housekeeping policies dictate the minimum frequency for cleaning activities.
Maintenance intervals: The frequency of maintenance activities and their timing, i.e. bundled together, determine how frequently machine shutdowns and startups occur.
Poor machine condition: Deteriorating machine and tool condition are two driving factors in the frequency of machine breakdowns. Poor machine condition may also lead to more frequent repair and preventative maintenance activities.
Lot sizes: Lot sizes dictate how often the machine will be shutdown, setup and started-up with for a different successive product variant. Companies may have minimum lot size policies to lessen the frequency of these activities.
System loading: how frequently a job is assigned to a machine contributes to the number of state transitions (idling, setups, shutdowns), and how much time remains for other activities, including planned maintenance.
Unsuitable ambient conditions: Unsuitable ambient conditions may cause machine breakdowns or material aging.
As these influence factors determine how frequently activities occur, they should be included in the operating state logic, i.e. the logic that dictates which operating state is active at each point in time, or under which conditions peripheral activities occur.
5.3.2 Duration-Dictating Factors
For some activities, material waste occurs at a steady rate over time, rendering the activity duration a decisive factor in the total material consumption. Some influence factors can increase aggregate material waste by prolonging the duration of these activities.
Examples from the influence factors discussed in 5.1 include:
Lot-sizes: The duration of the work-state of a machine may be prolonged by minimum lot size policy, thereby exceeding the requirement of a customer order. If the surplus parts are not sold, the amount of material waste increases disproportionally to the activity.
Quality rates: producing poor quality increases the duration of the work-state to process or rework certain parts, increasing the operating and auxiliary material consumption for processing an order.
Employee motivation: Employee motivation levels not only influence the occurrence of idling and breakdowns but also their duration.
Employee qualification: Employee qualification and experience levels may influence the duration of maintenance and setup activities.
5.3.3 Linking Factors
The linkage between material waste forms and activities may be flexible in some cases. The decision to reuse or dispose of remainders from stock sheets (trim loss), auxiliary, and operating materials is generally within the authority of factory management, while the reuse of other waste materials, e.g.
salvaging defects requires coordination with product engineering and process planning functions.
The influence parameter, disposal policy, describes whether coupling an activity with material waste is necessary:
Disposal policy: determines if a setup activity is coupled with flushing the machine and disposing of residual material, or if partially cut stock sheets can be reused. Similarly, the decision if intermediate packing should be discarded depends on the disposal policy.
5.3.4 Quantity-Determining Factors
Certain influencing factors influence the quantity of material waste per activity or per time-unit, without causing an activity to occur or influencing its duration. These factors serve to explain varying waste rates for identical operating states or changes in the waste rate over the course of time. These influence factors are deemed as “waste amplifiers” for that reason.
Employee qualification and cost-consciousness: Employee qualification and cost-consciousness may explain significantly higher material waste quantities or material waste rates for performing the same activities for similar durations.
Lot sizes: While the startup losses for a production lot are fixed per lot and can be modelled as a fixed quantity per machine startup, the amount
of process defects occurring at the end of long production runs due to machine fatigue exemplifies an increased material waste rate for a large lot size.
Poor machine condition: While the occurrence of part-processing activities and their duration may be identical, poor machine condition may lead to higher material waste rates or waste quantities.
Unfavorable product variant or process batch sequences: Although the processing duration and frequencies of activities (startups and setups) for different product variant sequences are identical, the amount of material waste caused when running unfavorable product variant sequences may be noticeably higher; therefore, unfavorable product variant sequences are considered a waste amplifier.
Unsuitable product variant / machine combination: For work centers with multiple, interchangeable but not identical machines, the assignment of a product variant to a less suitable machine may explain the difference in material waste quantities under the same processing conditions.
Unsuitable ambient conditions: Changes in air quality, possibly stemming from the occurrence of material waste, may explain differences in the material waste rates or material waste quantities for the same activities.
Long holding time: long material holding times in the factory may cause machine parameter deviations when processed or larger amount of inventory disposal in the sudden event of technical obsolescence or spoilage.
Disadvantageous product mix in pipeline: trim loss optimization is contingent on the availability of a mix of geometrically complementary orders. Similarly, some batch oven processes yield lower defect rates
when the part geometry mix allows for optimal temperature distribution.