Food packaging
Per-Batch Energy Measurement in Coffee Roasting
Industry: Food & Beverage — Coffee Roasting Domain: Energy Management, Industrial IoT, Manufacturing Intelligence Technology: Unified Namespace (UNS), MES Integration, Real-time Historisation
Challenge
In coffee roasting, energy is consumed through both electricity (heating elements, motors, cooling) and gas (burner flame). While total facility energy costs are tracked at an accounting level, the producer had no way to attribute energy consumption to individual roast batches. This made it impossible to understand the true cost per batch or to measure the energy impact of process changes such as curve adjustments, charge temperatures, or drum speeds.
Batch lifecycle data — start and end times, product type, charge weight — resided in the Manufacturing Execution System (MES), while energy meter readings existed in separate, unconnected systems. There was no integration between these data sources, and no operator visibility of energy in real time at the roasting control room.
Solution
logic4human designed and implemented a minimalistic Unified Namespace (UNS) to serve as a single integration layer connecting the MES, the electricity metering system, and the gas metering infrastructure. The UNS collected all data in real time and made it available to operators directly at the roasting control room.
The core design principles were:
- Minimalism — no unnecessary middleware, no heavy platforms; only what was needed to solve the problem
- Real-time availability — energy readings streamed live to control room displays alongside batch context
- Batch attribution — each roast batch is automatically linked to its electricity and gas consumption readings for the duration of the batch
- Historisation — all data is persisted so that historical analysis and trending are possible over time
Implementation
Batch context (start time, end time, product, weight) was pulled from the MES and published into the UNS in real time. Energy meters — both electricity and gas — were connected as live data sources feeding readings into the same namespace. A batch energy attribution engine correlated meter readings to active batch windows, computing consumption figures per batch as the process ran.
Operators at the roasting control room received a live view combining batch metadata and energy readings, giving them immediate awareness of consumption patterns during each roast. All data was historised to a time-series store, enabling retrospective analysis and comparison across batches.
Key Outcome
Each roast batch now carries an exact electricity and gas consumption record. When the roasting team adjusts a process parameter — for example, modifying a roast curve or changing charge temperature — the energy impact of that change can be precisely validated by comparing batch energy figures before and after. This turns energy from a fixed overhead into an actionable variable in the roasting process.