Weighing New Bag Lines Against Overtime
Decoupling Packaging From Bulk Production Shows Low-Capital Ways to Grow

At a global specialty chemicals company’s plant, each bag line is tied to one bulk production line and has to pack off product as it is made. Overtime is the central cost, and the company has to choose between alternative bag line configurations. A model run over a ten-year horizon weighs each option against the overtime it would need, including a layout with short-term storage between bulk production and packaging. The company comes away with low-capital options for growth it hadn’t seen, and keeps using the model for its own scheduling and production decisions.

The business

A global specialty chemicals company · bulk chemical production and bagging

The question

Which bag line configuration, weighed against the cost of overtime?

The model

Customer orders driving bulk production and bag lines over a ten-year horizon

What it is worth

Low-capital growth options; a model the company keeps using

Source

SDI project record · client testimonial

What the plant is up against

The company is restructuring its manufacturing network and needs to judge capital investments and asset use without trying them on live operations. On the packaging side, each bag line is dedicated to a bulk manufacturing line. Bagging has to take product as it comes off bulk production, so neither side can be scheduled on its own.

Overtime is the central issue. In some demand scenarios, demand runs ahead of manufacturing capacity. The plant can cover that with overtime, or it can spend capital on a different bag line configuration. Reorder quantities and batch sizes feed into the same decision.

What the model is built to answer

• What’s the tradeoff between the cost of alternative bag line configurations and the cost of overtime?
• Which combinations of scheduling parameters work best, measured in inventory levels and overtime cost?

How it is modeled

SDI builds a model-based application that ties ordering policy, planning and production capacity together, using a library for modeling supply chain dynamics. A stream of customer orders drives the model. Experiments vary the demand and forecast scenarios, the scheduling parameters and the bag line configuration.

Two configurations anchor the study. In the current setup, each bag line is tied to its bulk line. In the “To-Be” setup, short-term storage between bulk production and new high-speed packaging lines breaks that tie, so packaging can run on a different schedule from bulk production. Safety stock at the customer-facing inventories uses the basic calculation, unchanged.

Each run covers a ten-year experiment horizon. The model tracks total inventories, customer order fill rate and overtime hours, with overtime as the critical measure for each bag line scenario. For every bag line it reports average weekly hours split into production, repair, changeover, idle and overtime. That lets the team judge a configuration by how it spent its time rather than by nameplate capacity.

What the model shows

The model weighs the capital cost of each bag line configuration against the overtime it would need, across the demand scenarios. It also shows what the “To-Be” storage is worth. With bulk storage between production and packaging, the two can be scheduled separately. That kind of postponement gives the plant more flexibility in scheduling both production and packaging resources.

What changes

The company’s analyst calls the modeling “extremely successful.” After SDI’s first round of experiments, the company keeps using the model on an ongoing basis to assess schedules and production. It shows the same rate-based method at work well outside food, on bulk chemical production and bagging.

Where this work comes from

Built by Simulation Dynamics, the team behind ReliaSim, with the rate-based method ReliaSim is built on. Source: the SDI project record and the client’s testimonial. The results are reported in words, with no published throughput or cost figure.