Production planning: A two-step approach

The company

One of the leading players operating in the cork industry.

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The challenge

Given the growth of the business volume, the planning activities were becoming increasingly complex and the inefficiencies too costly.

The capacity planning was done manually considering typical constraints from the planning production formulation. Hence, different possible machines for the same operation and a multistage production process were daily challenges for the individual responsible for production planning.

The execution planning was done by the shift supervisors. At the beginning of each shift, they would decide which orders would be produced; this approach led to an increase of set-ups, idle time and delays.

The approach

The first step towards meeting this challenge is to follow an integrated methodology, combining the main concepts of flow management by leveraging the Kaizen methodology to build robust and efficient processes with the power of analytical skills.

All this starts with a clear definition of requirements, goals and data collection/quality:

• List and question every production constraint –some were not relevant or were not a constraint.
• Set the goal of the algorithm to maximize output.
• Map every data flow and build network protocols to ensure data extraction and importation to the database.

Once these topics were addressed, it was time to move on to the production algorithms.

In order to do so, it was important to set two different planning layers:

• Capacity Planning (medium level): Management of production capacity - determining which equipment and shifts are necessary to meet the proposed deadlines and objectives according to the demand variability.
• Execution Planning (low level): Sequencing of production orders, allocating them to a machine and a start time, respecting the sequence of operations and maximising efficiency.

Note that these two layers are not independent: the execution layer cannot start its sequencing work if the necessary and adequate capacity to meet the demand is not present at the mid-level. So, it becomes vital to coordinate information from planning levels in a consistent and coherent manner.

Therefore, the first step was the development of a tool that would plan the orders for a specific week and then a low-level tool that would minimise delays and set-ups in the shop floor.

This two-step approach ensures a connection between the two planning layers, always supported by information connected directly to the systems.
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          Capacity Planning

The capacity planning algorithm determines which equipment and shifts are necessary to meet the proposed deadlines and objectives according to the demand variability, allocating orders to groups of resources/machines.
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An interface was developed to better visualise the results of the capacity planning algorithm that incorporated different departments of the factory.

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          Execution Planning

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The Execution Planning algorithm sequences production orders, allocating them to a machine and a start time, respecting the sequence of operations and maximising efficiency.

This algorithm was run at the beginning of each shift in order to integrate the current stocks and wip available.

The results were imported to a web app in which, with different logins, one could see different KPIs, add new shifts and machines parameters. A dynamic Gantt chart allows a bigger and detailed picture of the production plan simultaneously. The user can easily grab an order and try to change to another machine/sequence and automatically a set of alerts appear in case the solution is not feasible.

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Achievements

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Considering the work done, the capacity planning was done more efficiently due to the increase of visibility of the different departments and the introduction of a set of rules to minimise set-ups.

The Execution Planning tool led to a better distribution of production time between machines and a reduction in the number of set-ups.

The possibility to acknowledge when a certain order would be ready to send and the opportunity to anticipate an order and recalculate the production sequence was the main breakthrough of the project.