Digital technology: JoonX Consulting

Fig 2. Cosling Scheduler Graphical Planning Manager Interface: Resource � View (who does what and when). � Fig 3. Cosling Scheduler Graphical Planning Manager Interface: Order View (start, end, deadlines and resources assigned).

to be effective for various industries: Aerospace and Defence, Transportation and Logistics, Oil and Gas, etc.

Current scheduling process

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.

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. If the schedule needs to be revised, the planning sheet of step one is overwritten or modified as necessary.

Limits and issues

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.

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.

Once modelled, it becomes possible to launch the solving process. CP Solvers are usually excellent at fi nding a feasible solution in a few seconds. The challenge is to fi nd a near optimal solution within a few minutes to update planning several times in a day. Based on advanced algorithms, the Cosling Solver can fi nd very good solutions within 3 to 5 minutes. The resulting planning can then be visualised through a graphical user interface (Figs 2 and 3).

It is worth noticing that such tools are interactive; the user may modify some data (e.g. inserting a new unavailability) and re-optimise the schedule to apply the change. Once satisfi ed with the given solution, the planning manager can validate and publish planning to make it visible for all operators.

As the tool automatically checks business constraints, such as production capacity and order deadlines, the user workload is considerably reduced. This enables managers to focus on higher added-value tasks, such as studying the following whatif scenarios: � What happens if I postpone/advance this furnace maintenance? � What happens if I hire a new operator next month? � Should I profi t from a period of lower activity to replenish some stocks? � Is it worth making a gob change tonight? � What happens if I agree to prioritise this order? � What happens if I accept this new order?

Modifying input data according to the scenario, and reoptimising it, can now answer all these questions quite easily. The planning manager can then decide what to do by comparing production key performance indicators (KPIs). Impact studies that used to take months can now be done in a several days or minutes.

Conclusion

Issues encountered by planning managers can deeply impact company performance. Advanced planning systems could be used to speed up this process, compute near optimal planning and thus make it possible to test what-if scenarios, leading to a global performance optimisation.

Ultimately, the technology enables managers to spend more time on higher added-value tasks; such systems need consolidated data and a detailed modelling of industrial processes.

Based on the example of a glass bottle production company, we have explained how CP Solvers can be used to schedule production effi ciently and effectively.

The resulting software can be used not only for production planning, but also to empower other processes such as maintenance, supply-chain, sales and human resources. �

*Founder of JoonX Consulting, Cuauhtémoc, Mexico, https://joonx.org/en/home/ **Cfounder and COO of Cosling, Nantes, France, https://www.cosling.com/

Footnotes 1 French-Best PhD Thesis in Artifi cial Intelligence (AFIA 2015) and World-Best PhD Thesis in Constraint-Programming (ACP 2015). 2 One task at a time.