Lean energy assessment for wineries: A data-driven framework for screening potentially avoidable electricity demand

Improving sustainability in process-intensive industries requires moving beyond annual totals to determine when, where and why avoidable electricity demand arises. This study develops the Lean Energy Assessment for Wineries (LEAW), a data-driven framework that translates traditional Lean muda into eight Energy Muda and connects meter-derived signals with operational verification. An exploratory application combined 8,760 hourly observations from an industrial winery in 2022 with process mapping and structured Gemba-walk sessions. Plant electricity use was 3.83 GWh. Bottling and warehousing represented the largest share of plant electricity use (31.2 %), followed by refrigeration, with measured fixed refrigeration accounting for 12.9 % and baseline subtraction from the non-disaggregated balance estimating an additional 13.2 % attributable to seasonal rented units, for a total refrigeration-associated share of 26.1 %. Pumping accounted for a further 9.7 %. The August–October harvest represented 40.8 % of demand, while crushing, press compressors and pumping showed Harvest Energy Shares of 95.7 %, 64.4 % and 50.8 %. Night, weekend and harvest indicators differentiated seasonal loads from persistent services and generated hypotheses of potential Energy Muda. Operational investigation documented insulation deficiencies, long-distance hot-water delivery, candidate over-conservative temperature practices, monitoring gaps and limited formalization of energy-related knowledge. A participatory Action Priority Score identified refrigerant-piping insulation as the leading follow-up action. However, 21.5 % of plant demand was not covered by complete dedicated-meter series and the proposed actions were not implemented or evaluated ex post. This single-case application therefore demonstrates the diagnostic feasibility of LEAW rather than verified energy savings; its effectiveness and transferability require validation through implementation and additional cases.