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� Fig 2. Cosling Scheduler Graphical Planning Manager Interface: Resource
� Fig 3. Cosling Scheduler Graphical Planning Manager Interface: Order View
View (who does what and when).
(start, end, deadlines and resources assigned).
to be effective for various industries: Aerospace and Defence, Transportation and Logistics, Oil and Gas, etc.
If the schedule needs to be revised, the planning sheet of step one is overwritten or modified as necessary.
Current scheduling process
Limits and issues
To illustrate a recurring situation, an example from a South America container glassmaker will be considered. The production planning process attempts to efficiently meet the demand for the finished product within the available resources, given the restrictions associated with furnaces, forehearths, labour, supplies, etc. The process is carried out internally and executed by the planning team made of two people. It consists of three subprocesses: 1. Weekly manual update of a planning sheet, which consists of importing and organising data from different Excel documents of different sources. 2. Identification of inventory coverage by bottle types. 3. Bottle production scheduling.
Such a system described in the previous section presents substantial limits, including: � Manual processes leading to potential mistakes (handling and rewriting of several Excel documents). � Time consuming tasks for many people of different teams. It requires several hours per week for planning managers who could be working on higher added-value activities. � Complex tasks, which usually require one year of training for a new planning manager to become efficient. Then, the knowledge is held by few persons within the company and not stored and capitalised in a robust software solution. If planning managers are absent (injury, Covid, etc.), the activity is deeply impacted. � Production constraints and their associated costs are not taken into consideration at the beginning of the scheduling, leading to iterative and inefficient calculations. � Suboptimal scheduling, leading to a waste of production yield. � When input changes, such as urgent customer demands or machine failure, plans are just adapted rather than being optimised once again. � No time can be spent on what-if scenarios, which reduces the ability of the company to anticipate issues and adapt accordingly. � Additional meetings are necessary with other process teams for final validation.
These steps are all manual and take the planning team several hours every week. Once the scheduling of the bottles is complete, the changes and transitions in each production line (from the production of one bottle to another) are analysed to ensure no changes can delay production (for example colour transitions or complex machine changes). Therefore, when necessary, modifications, exchanges or replacements of scheduling plans previously done are manually reexecuted. Finally, two weekly meetings between the planning team, the sales and production coordinators are held to share and review the schedule and validate that all the urgent orders are prioritised.
In this specific case study, one of the biggest concerns is the ability of the company to efficiently react to demand changes. Indeed, in the context of low and predictable demand, production changes can be managed relatively easily. However, when demand is high and unpredictable, the need for a robust solution is highly acute. In these scenarios, the productive restrictions have associated costs, necessary for consideration.
Improvements The first step for improving the production planning process consists of formalising the scheduling problem and gathering all the input data (e.g. machine qualifications, operator availabilities, stocks, contracts). Then, the CP model is built in an agile way, using customer feedback to come up with a model as close to operations reality as possible. The scheduling problem can be modelled within CP through tasks and resources. The general idea is to associate each order to a set of tasks, that must be assigned to qualified and available resources, before a given due date. Some resources, such as furnaces and forehearths are subject to capacity constraints, while others, such as operator, are subject to disjunctive constraints2. Unavailable resources, such as furnace maintenance, could be modelled through additional fixed tasks, which are aimed at reducing resource capacities to zero during this period. Each task would be associated to an assignment variable whose domain can be reduced by considering business constraints such as the type of IS machines, or possible combinations of moulds within IS machines.
70 0 Glass International March 2022
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