Process Mining for the Consumer Goods Industry

Process Mining for the Consumer Goods Industry

In consumer goods, promotion-driven order spikes, pack-size variants, and returns can change how work moves through order, production, and distribution processes. Upload your event data to see where cases wait, take different paths, or need rework. Compare cycle times and other measures across variants, then assess proposed changes before rollout.

How do promotion-driven order spikes affect fulfillment?

Promotions can quickly change order volumes and product mix. Compare cases from promotion periods with cases from other periods in your own data. Check order-to-delivery cycle time, waiting time at each step, late orders, and rework linked to exceptions.

This analysis can show whether delays cluster around order entry, approvals, allocation, or distribution. If your event log includes channel or order type, compare process paths and measures across those groups.

To learn how to investigate a process question with event data, read how to analyze your process.

Where do pack-size variants create extra work?

A product may follow a different path depending on its pack size, production site, or order type. One process map can hide these differences. Compare variants in your event data and check which paths take longer or repeat activities.

Use measures your data supports, such as cycle time by pack size, repeated activity counts, or the share of cases that follow an unexpected path. This helps you distinguish a process issue from a variant that needs a different route. Learn how to analyze process variants in ProcessMind.

What can returns reveal about rework?

Returns may pass through several handoffs, checks, and decisions before resolution. Trace each return through the activities recorded in your systems. Measure case duration, where cases wait, and how often work repeats or returns to an earlier step.

If your data includes product, return reason, channel, or site, compare paths across those attributes. The results can show where to investigate without relying on industry averages.

How can you find bottlenecks across production and distribution?

Start with a specific question, such as why some orders arrive late or why certain production cases need rework. Review the actual process flow and compare cycle time, waiting time, throughput, and deviations across relevant variants.

ProcessMind shows where work queues up, takes an unexpected path, or loops back, based on the event data and process scope you choose. For a related view of production processes, see the manufacturing use case.

How can you test a process change before rollout?

Use your findings to create or update a process model and define a proposed target state. With BPMN modeling, document the flow you want teams to follow. With process simulation, compare scenarios before implementation.

For example, you could model a routing or staffing change and compare its potential effect on cycle time or capacity. Simulation helps you assess scenarios. It does not implement changes or guarantee a particular result.

What data do you need to get started?

Choose a high-variation flow, such as order fulfillment or production. Prepare an event log with:

• A case ID for each order, production run, or return
• An activity name for each recorded step
• A timestamp for each event
• Useful attributes, such as product, pack size, site, channel, or order type

The attributes in your data determine which variants you can compare. Start with the fields available in your systems and the question you want the analysis to answer. See what you need to run Process Mining for more on data requirements.

What should you measure first?

Choose measures that answer your process question and can be calculated from your data. Depending on your event log, you might compare:

• Cycle time by product, pack size, channel, or order type
• Waiting time at key steps
• Repeated activities and rework
• How often different process variants occur
• Late orders or return resolution time

These measures help you decide where to investigate. Results depend on your data, process scope, and any changes you make.

Where should you begin?

Start with an order or production flow that has meaningful variation. Use its event log to find paths with delays or rework, then decide which change is worth testing.