As shown in the Ishikawa diagrams, the prevalence of activity-triggering influence factors in academic and practical literature reinforces the connection between material efficiency and the avoidance of wasting activities and events. These activities and events are shown in Table 9 with their corresponding material waste forms, as identified in the Ishikawa diagrams.
In the following sections, the term “activity” will be used to describe both planned activities as well as unplanned events for brevity.
Some of the activities are already depicted in operating-state-based manufacturing simulations (Section 4.2), while others have not previously been modelled. To investigate how well the operating states represent material-wasting activities, Haag’s set of operating states, arguably the most extensive set, is compared with the identified material-waste activities. These include work, warmup, wait, block, error, setup, off/standby, and save (Haag 2013).
Table 9: Linkage of waste forms to planned and unplanned activities
Planned activities Unplanned activities
Material waste form
Processing interval Stock material unit replenishment Destructive testing Transport Setups Housekeeping Maintenance Shut-offs and Startups Machine breakdown Machine idling Material aging interval
5.1.1 Process defects ● ○ ● ● ● ○ ○ ● ● ● ○
5.1.2 Shrinkage ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ●
5.1.3 Transport loss ○ ○ ○ ● ○ ○ ○ ○ ○ ○ ○
5.1.4 Trim loss ● ● ○ ○ ○ ○ ○ ○ ○ ○ ○
5.1.5 Chips ● ○ ○ ○ ○ ○ ○ ○ ○ ○ ○
5.1.6 Byproducts ● ○ ○ ○ ○ ○ ○ ○ ○ ○ ○
5.1.7 Auxiliary materials ● ○ ○ ○ ● ● ○ ○ ○ ○ ●
5.1.8 CLOM with workpiece
contact ● ○ ○ ○ ○ ○ ○ ● ○ ○ ●
5.1.9 CLOM without workpiece
contact ● ○ ○ ○ ○ ○ ● ○ ○ ○ ○
5.1.10 Cleaners ● ○ ○ ○ ● ● ○ ○ ● ○ ○
5.1.11 Packaging and protectors ● ○ ○ ○ ○ ○ ○ ○ ○ ○ ○
●=Material waste form linked to activity ○= No linkage
Workpiece or batch processing: Material consumption or waste occurring with every workpiece or machine batch could be modelled in a work-state on the respective machine with a lump sum per piece or batch, or as a waste rate analogous to power consumption.
Stock-material unit replenishment: Haag’s set of operating states neglects to consider the activity of removing material lost at the end of a material stock unit (e.g. a stock sheet or coil remainder). The act of removing and discarding the remainder material may take place during processing without interruption (work-state), when the machine feed is empty (wait state) or during setup procedures (setup state). The most logical approach would be to model this
waste as occurring in a lump sum when transitioning into an idle or setup state.
However, stock material remainders are not removed during every wait or setup state, only when the machine is nearly starving, i.e. the stock unit is almost completely consumed, or needs to be changed to process another order type. Therefore a more complex operating state logic is required to differentiate between starving conditions, and other sources of machine waiting (i.e. employee absence, no orders, material not delivered).
Destructive testing: testing operations are not explicitly addressed in the machine-operating state approach, though they could be added by modelling testing equipment as a machine. Destructive testing would occur in the work-state of a testing machine. The length and frequency of testing intervals would depend on testing policy.
Transport intervals: With the exception of fully automated transport systems, logistical and handling operations are frequently neglected in operating-state-based modelling due to the low energy consumption in manual handling operations and the extent of employee influence (e.g. forklift energy consumption). Transport activities however cannot be neglected in material efficiency modelling. Machine operating state logic applies very loosely to transport activities, as some material waste may occur due to the dynamics of operating state transitions (e.g. sudden breaking modelled as wait state or errors). However, there is no indication in literature that setup activities and startups of transport equipment and vehicles are material intensive.
Additionally the effort of conceptualizing an operating state-logic of a human driven system may exceed the benefit of modelling these operations.
Therefore a lump sum per transportation activity is assumed adequate in detail.
Setup intervals: Due to the difference in materials consumed during setup procedures to those in normal operations (e.g. cleaning materials, testing materials), the two operating states need to be distinguished. However, the
more material intensive than the setup itself, warranting consideration of the transitions between operating states. State transitions are ignored with the exception of machine warm-ups in energy efficiency modelling. Depending on the combination of successive product variants, the amount of material waste occurring may vary (e.g. light clean-up vs. heavy clean-up). Therefore the lump material waste quantities per transition cannot be assumed constant.
Housekeeping intervals: machine-centric operating-state-based simulation methods neglect the material consumed in housekeeping and secondary activities in the direct peripheral of the machine. These cannot be integrated into the operating state logic, as they are performed manually independent from the machine’s operating state. For that reason, modelling housekeeping as a lump sum per activity or as a waste rate over the duration of the activity is suggested.
Maintenance intervals: maintenance activities requiring material consumption (e.g. cleaners, lubricant disposal and replenishment) can either take place in an unplanned repair activity (analogous to Haag’s error-state), or in a planned preventative maintenance activity. The latter option is not featured operating-state-based modelling. During a planned maintenance activity, the machine may remain in an idle state or be turned off. To account for the additional material consumption of planned maintenance, an additional operating state is suggested. Similarly to idle or off, it is assumed that shutdown and startup loss respectively immediately precede or follow the planned maintenance state.
Machine shutdowns and startups: Since defects occur at increased levels during machine startups and shut downs, transitions between the operating states work and off cannot be neglected.
Machine idling: To account for startup losses in processes where the machine is never fully shut-off, as well automatic material disposal after prolonged
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.