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Complex Production Plans with millions of variables

The Problem

The Customer is a known lubricant brand in India. They have one plant from where they supply Finished Goods to multiple distribution centres and to several direct Customers. Business is a complex mix of Make to Stock and Make to Order Products. They were scouting for an end to end Supply Chain platform for (a) Demand Planning, (b) Inventory Planning, (c) Logistics Planning and (d) Production Planning & Scheduling. Production Planning & Scheduling was the biggest challenge for them as it was complex and production compliance was much below the desired level.

Key Challenges

The Plant had around 4 reactors, 20 blenders of different capacities and 15 filling lines for packing different pack sizes. Each filling line could be used for packing either cartons, buckets, drums or tankers. There were holding tanks between blenders and the filling lines. In the end, finished goods were packed in shippers on packing lines.

The overall manual planning was a very complex exercise and it required multiple schedulers to spend 3 to 4 hours every day. Being a time-consuming process it was difficult to respond to unplanned changes within the day. Due to the complexity involved in manual Scheduling the customer was using MS Project instead of MS Excel as a rough-cut planning tool which lead to high Production compliance issues. Despite usage of MS Project schedules were made for only one day and were not fully realistic as Material constraint and pack size wise production speeds were not considered.


rhythm 2.0 Production Planning & Scheduling system could meet their dual requirement of comprehensive end-to-end planning and complex detailed shop floor level scheduling. The system was customized, and a variant was created to meet the requirements of the Lubricant Industry. The new variant of the rhythm Production Planning & Scheduling System addressed the key challenges as below :

1. Batch Size Optimization :

rhythm 2.0 batch optimizer maximized the loading of batches across 20 blenders, each having a different capacity. Additionally, the batch optimizer handled the constraint of restricting each blender to load a specific set of Oil type and Grade. It grouped the right set of Orders based on delivery date and blender capacities and ensured that Batch utilization was maximized and at the same time, Orders were delivered on time.

2. Limited Stocking Capacity in Holding Tanks :

Due to limited Stock holding Capacity Blenders had to either load the filling lines online or stock the material in Holding tanks. The Scheduler made intelligent decisions of whether to load the blender output online on the filling line or stock the material in one of the holding tanks for filling later.

3. Filling Line Loading Optimization :

Each filling line could load only a set of Pack Types, Shapes and Sizes. The filling rate depended on a combination of all these attributes. The system optimized the selection of Filling line for each Order such that the overall Production could be done in the fastest possible time.

4. Batch and Filling Line Sequencing Optimization :

Sequencing of Batches and Filling lines was critical for maximizing per day production. Batches as well as Filling sequencing was optimized such that inter-product set times were minimized and at the same time Orders were delivered on time. Additionally, batch sequencing on the blenders had to be synchronized with the sequencing on the filling lines to minimize the usage of holding tanks. Any gap in synchronization would lead to stoppage of blenders and subsequent delays. rhythm 2.0 is capable of sequencing batches and downstream lines based on a unique Delivery Score concept.

5. Lot Optimization on Filling Lines :

Production Lot size for each Order on the filling lines was optimized to maximize synchronization between blenders and to minimize the set up time. Smaller lots on multiple lines lead to parallel filling and faster evacuation of blenders making it ready for the next batch faster. It however lead to more changeovers on the filling lines and increased the set up time. Optimal lot size had to be derived for each Order. rhythm 2.0 is capable of optimizing lot-sizes to do this trade off.

6. Holding Tank selection Optimization :

Selection of the right holding tank was done to minimize the tank requirement and to maximize concurrent loading of multiple batches the same product. Additionally, constraints of Blender to Tank connection and Tank to Filling line connection had to be considered for selecting the Holding Tank.

7. Accurate modelling of Production Speeds :

rhythm 2.0 is capable of modelling Production speeds and constraints in terms of attributes like Pack Type, Shape and Size. Likewise, it is also capable of modelling batch time based on Oil Type and Grade. This ensures that the batch time and filling rates are properly represented, and the plans generated are easy to execute leading to higher Production Compliance.

8. Raw Material Constraint handling :

Additives and Packing Material availability constraint was key to delivering a plan which was realistic and could be adhered to, leading to higher Production Compliance.

9. Long Term Production Plan :

Full month Production plans were created based on Order dates, Raw Material Availability, Material arrival schedule, material lead-times (for materials not procured) and Maintenance schedule of Blenders, Filling lines and Holding Tanks.

10. MTO and MTS Consideration :

All direct customer Orders (MTO) were given priority against MTS orders. MTS production requirement date was computed based on monthly demand using the rhythm 2.0 demand splitting functionality.

11. Continuous Raw Material Replenishment :

Raw Materials had to be replenished in time. Raw Material Safety Stock Norms were optimized for each Raw Material using the rhythm 2.0 Inventory Optimizer. The Optimizer considers the demand variability, procurement lead-times, procurement lot sizes and supply variability to compute these norms. Continuous Replenishment mechanism was set based on the future Production requirement (derived from the Production Plan).

Key Results

Summary of initial results delivered to the customer :

Complex Production Plans with millions of variables_image 1

Blender Schedule :

Complex Production Plans with millions of variables image 2 | Algorhythm Tech

Filling Line Schedule :

Complex Production Plans with millions of variables image 3 | Algorhythm Tech


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