Quantify Uptime, OEE, and Downtime Cost

The goal here is simple: lock in real Availability, Performance, and Quality from run data, then put a fair price on each lost hour so downtime shows up in money, not just minutes. When those three OEE pieces are grounded in logs—not guesses—planning and ROI decisions stop drifting, because output and loss are quantified the same way every time.

Actions

• Set the three OEE factors by SKU and shift from recent stable runs: Availability = Run Time / Planned Time; Performance = (Ideal Cycle Time × Total Count) / Run Time; Quality = Good / Total; OEE = A × P × Q.

• Price lost production per hour using Good UPH × contribution margin per unit; add idle labor, overhead burned during stops, and typical repair costs from maintenance records and SLAs to avoid undercounting.

• Build a downtime ledger: list top loss reasons (breakdown, changeover, resin change, block/starve), count events, average duration, and total hours to forecast annual exposure and target fixes with real numbers.

Avoid

• Avoid assuming 90%+ OEE without logging changeovers, minor stops, and resin variability; these hit Availability and Performance more than most teams expect.

• Avoid using nameplate speed for Performance; use ideal cycle time and actual counts over run time so slow cycles and micro‑stops are captured.

Tools

• Maintenance logs for MTBF/MTTR, typical fault durations, and parts consumed; these feed Availability and cost-of-repair lines in the ledger.

• Service SLAs and parts lead times to model extended MTTR windows and the probability of longer outages when spares aren’t on hand.

Outcome

• A per‑SKU sheet with Availability, Performance, Quality, OEE, Good UPH, and monetized downtime exposure, rolled up by line and plant for annual downtime cost and OEE‑backed output.

• A ranked list of loss buckets showing where time and money leak, justifying SMED workshops, preventive work, or resin SOPs with clear capacity and cash impact.

Formulas

• OEE: $\text{EGE (Eficiencia General de los Equipos)}=\text{Availability}\times \text{Performance}\times \text{Calidad}$.

• Availability: Run TimePlanned Production TimePlanned Production TimeRun Time; Performance: Ideal Cycle Time×Total CountRun TimeRun TimeIdeal Cycle Time×Total Count; Quality: GoodTotalTotalBien.

• Downtime cost per event: $(\text{Good UPH}\times \text{Contribution Margin})\times \text{Downtime Hours}+\text{Idle Labor}+\text{Overheads}+\text{Repairs}$.

Visual

• OEE pie chart with three slices—Availability, Performance, Quality—summing to 100%, annotated with top availability losses like changeovers and breakdowns from the downtime ledger.

  Calculate Scrap and Material Losses

  This step is about turning every gram of waste into a clear cost so material losses stop hiding in averages and assumptions, especially in moldeo por soplado where flash, start‑ups, and thickness variation add up fast. When scrap is logged by type and multiplied by real resin prices and part weights, it becomes a simple per‑bottle and annual number that’s easy to track and improve.

  Actions

  • Log start‑up scrap: count and weigh rejects during heat‑up and mold start, then tag by SKU, mold, and resin grade so the cost can be tied to specific conditions and shift patterns.

  • Track steady‑state rejects: use the basic scrap rate formula—Scrap Rate = Scrap Quantity / Total Production—to quantify ongoing defects and convert to mass using SKU weight specs for accurate resin costs.

  • Capture flash and regrind loop: record flash mass per cycle and actual refeed percentage; note that blow molding routinely generates large regrind volumes and properties can shift versus virgin, affecting usable blend ratios and yield.

  • Multiply by resin price: compute cost for each scrap bucket as Scrap Mass × Resin Price; report both cost per good bottle and annual cost using SKU volumes and negotiated resin rates.

  Avoid

  • Don’t ignore parison control and wall thickness programs—poor control drives overweight parts and higher reject rates; modern parison control evens thickness and can cut part weight materially while improving quality.

  • Don’t assume all regrind behaves like virgin; particle size and fines change bulk density and processing, impacting flow, stability, and defect rates if unmanaged.

  Tools

  • SKU weight specs: use target weight and tolerance to convert counts to kilograms for precise scrap costing and to flag overweight trends that burn resin silently.

  • Parison control settings and mold data: correlate thickness profiles, head programs, and mold cavitation with scrap spikes to pinpoint where control changes reduce loss without hurting strength.

  Outcome

  • Clear metrics: scrap cost per year and per good bottle by SKU, broken into start‑up, steady‑state, and flash/regrind categories so actions have obvious ROI and accountability.

  • Weight optimization: evidence that better parison control reduces average part weight and rejects, improving yield and lowering resin spend at the same time.

  Formulas

  • Scrap rate by count: Scrap Rate=Scrap UnitsTotal UnitsScrap Rate=Total UnitsScrap Units and by mass: Scrap Rate=Scrap MassTotal Material MassScrap Rate=Total Material MassScrap Mass for periods or runs by SKU.

  • Scrap cost per good bottle: Cost/good=∑(Scrap Massi×Resin Price)Good UnitsCost/good=Good Units∑(Scrap Massi×Resin Price) with buckets for start‑up, steady‑state, and flash/regrind to show contributions.

  • Annual scrap cost: $\text{Annual Cost}=\text{Cost/good}\times \text{Annual Good Units}$ using forecast volumes to tie improvements to budget targets.

  Example levers

  • Tighten parison profiles to reduce top‑to‑bottom thinning and overweight compensation, yielding more uniform walls and lower average part mass at spec.

  • Improve regrind quality by controlling particle size and removing fines to stabilize feed density and melt, which reduces steady‑state defects from flow variability.

  Visual

  • Before/after scrap trendline: plot scrap rate or scrap kg per 10,000 bottles by week, annotating the date parison control adjustments were applied to show weight and reject reductions over time.

  Labor, Changeovers, and Automation

  This step turns people time and setup time into a clean cost per good bottle, then shows how automation and SMED reduce both without sacrificing quality checks. The idea is to define who touches the cell, how long changeovers actually take, and how often they happen—then price the lost hours and the labor that keeps running even when the line is stopped.

  Actions

  • Define the staffing plan per shift: operators on the cell, material handling, and QC touches; use time studies or standard work to capture minutes per loop for inspection, pack-out, and rework so labor per unit is grounded in observed cycles.

  • Quantify changeovers: measure setup time from last good of SKU A to first good of SKU B, including tool/mold swaps, purging, parameter dialing, and verification; log frequency per week to forecast total setup hours.

  • Map automation options inline: note quick-change kits, preset recipes, tool carts, and automated stations such as inline deflashing and leak test that can reduce handling and QC touches without bottlenecking the line.

  Avoid

  • Don’t forget start-up waste after each change—those first-off rejects are part of the setup window and should be costed along with lost time, not treated as “free” scrap.

  • Don’t assume a nominal staffing level covers QC; if leak test or deflashing is manual today, count those touches explicitly or justify automation that removes them from the labor denominator.

  Formulas

  • Direct labor cost per good bottle: Labor/good=Direct labor hours (run + setup)Good units×Loaded wage rateTrabajo/good=Good unitsDirect labor hours (run + setup)×Loaded wage rate where “run + setup” includes QC touches and documented changeover time.

  • Annual changeover loss hours: $\text{Setup hours/year}=\text{Avg setup hours/event}\times \text{Events/year}$; monetize with $\text{Cost/hour}=\text{Good UPH}\times \text{contribution margin}+\text{idle labor + overhead}$ for capacity and cash impact.

  Tools

  • Time studies and standard work: video and time the full setup and operator cycles; separate internal work (machine stopped) vs external work (machine running) to convert internal tasks to external per SMED.

  • Quick-change and inline stations: evaluate preset tooling, staged carts, and inline deflashing/leak test so changeovers shrink and QC becomes 100% automated without extra headcount or speed loss.

  Outcome

  • A per‑SKU labor cost per good bottle that includes operators, QC, and setup allocation, plus an annualized changeover loss line item tied to frequency and duration.

  • A prioritized list of SMED and automation levers that free capacity and remove manual QC touches, improving OEE availability and stabilizing throughput without new headcount.

  Visual

  • Cell layout diagram: show operator positions, quick‑change staging, and automated stations (deflash, leak test) on the flow, highlighting which QC touches are eliminated and which setup steps move external under SMED.

  Maintenance, Spares, and Reliability

  This step turns reliability into a budget line: plan the parts and PMs that will definitely be needed, price the time it takes to recover from failures, and hold critical spares on‑site where lead times would otherwise turn small issues into long outages. With MTBF/MTTR and a realistic PM schedule, years 2–5 costs stop being a surprise because both planned spend and risk‑adjusted downtime are modeled upfront.

  Actions

  • Budget planned parts and PM labor: list PM tasks and intervals, attach parts kits and hours, and calendarize them to get a yearly parts-and-labor budget by asset and line.

  • Price MTTR and service response: use failure logs to estimate MTBF and MTTR, then price each failure using lost output per hour plus repair labor; include service SLA response lags that extend downtime windows.

  • Plan critical spares on‑site: run a criticality and lead‑time screen (ABC) to decide what to stock; set reorder points and record carrying cost so the stock vs. downtime trade‑off is explicit.

  Avoid

  • Don’t underestimate years 2–5: wear parts, oil/filters, seals, hoses, sensors, heaters, valves, and drives stack up after warranty; budget replacements on the OEM cycle, not wishful thinking.

  • Don’t ignore lead times: long‑lead electronics and custom tooling should be stocked or dual‑sourced; otherwise MTTR balloons from hours to weeks despite good technicians.

  Tools

  • PM schedule: reliability‑centered mix of preventive and condition‑based tasks with defined intervals and feedback loops to adjust frequencies as data improves.

  • Parts list and BOM: item master with cost, lead time, alternates, and ABC class to drive min/max and reorder points in the CMMS/EAM system.

  • Remote diagnostics: enable faster triage and first‑time‑fix by having telemetry and remote support in SLAs to shrink effective MTTR.

  Formulas

  • MTBF: MTBF=Uptime HoursFailure CountMTBF=Failure CountUptime Hours; MTTR: MTTR=Repair Downtime HoursFailure CountMTTR=Failure CountRepair Downtime Hours for each asset or line family.

  • Availability from reliability: A=MTBFMTBF+MTTRA=MTBF+MTTRMTBF; tie A back to OEE to see the impact of maintenance on output.

  • Risk‑adjusted downtime cost/year: ∑i(Failuresi/year)×MTTRi×Cost/hour∑i(Failuresi/year)×MTTRi×Cost/hour, where Cost/hour includes lost margin, labor, and overhead for the line.

  Outcome

  • A two‑part budget: 1) annual planned maintenance (parts and labor by PM interval) and 2) risk‑adjusted unplanned downtime cost from MTBF/MTTR and SLA assumptions.

  • Stocked critical spares with reorder points and carrying costs, reducing risk of long outages while making the capital tied in inventory transparent and purposeful.

  Visual

  • Gantt of PM intervals: plot monthly/quarterly tasks per asset with parts kits and hour estimates; overlay failure‑based tasks as conditional bands to show where CBM signals would trigger additional work.

  Finance, Depreciation, and Compare Alternatives

  This step rolls financing, depreciation, and residual value into the true cost per good bottle and then compares machines head‑to‑head on cost/bottle, payback, and IRR—not just sticker CapEx. The idea is to translate OEE‑backed output and operating costs into cash flows, then use standard finance metrics so the better choice is obvious and defensible.

  Actions

  • Add financing cost: include interest from loans or lease payments in the annual cash flows; for leases, use the payment schedule; for loans, include principal outlay and interest expense timing.

  • Set a depreciation schedule and residual value: choose tax depreciation (e.g., MACRS under GDS/ADS) or book straight‑line, and include expected resale value at end of horizon to reduce net cost of ownership in the model.

  • Compute cost per good bottle: allocate annual fixed costs (finance, depreciation, insurance) over OEE‑backed good units, then add variable costs (energy, labor, scrap, maintenance) to get all‑in cost/bottle by SKU.

  Avoid

  • Don’t pick on CapEx alone; compare total cost/bottle and capital efficiency over time, because higher‑efficiency equipment often wins once output and operating costs are normalized.

  • Don’t ignore payback and IRR; these show time to breakeven and overall return versus the hurdle rate, and they penalize options with light early cash flows or heavy tails.

  Tools

  • Spreadsheet payback and IRR: enter incremental cash flows between Machine A and B; use IRR/NPV functions to get payback and IRR, and sensitivity tables to test OEE, kWh/good, labor, and scrap assumptions.

  Formulas

  • IRR: solve 0=CF0+∑t=1nCFt(1+IRR)t0=CF0+∑t=1n(1+IRR)tCFt with spreadsheet IRR/NPV tools for practical calculation speed.

  • Cost per bottle: Cost/good=Fixed annual costsGood units+Variable cost/goodCost/good=Good unitsFixed annual costs+Variable cost/good, where fixed includes depreciation and financing and variable includes energy, labor, scrap, maintenance.

  • Payback: smallest $t$ where cumulative cash flow turns non‑negative when comparing the preferred option vs. baseline or Machine A vs. B.

  Outcome

  • Side‑by‑side Machine A vs. B showing cost/bottle, payback, and IRR; the winner should have the lower cost/bottle and acceptable payback at or inside the target horizon with IRR above the hurdle.

  • A visual with a table or two bars for cost/bottle difference, making the capacity and operating‑cost advantages easy to explain to finance and operations together.

  Bonus tips / advanced moves

  • Ask vendors for kWh per good bottle and OEE on similar SKUs; cycle time alone is misleading without real losses included.

  • Validate assumptions with a factory reference or short trial run when possible to reduce model risk in OEE and scrap inputs before committing.

  • Model seasonality: hotter months can raise chiller load and defect rates, shifting kWh/good and scrap; reflect this in monthly cash flows if energy tariffs are time‑of‑year differentiated.

  • Consider rHDPE/PCR variability; tighter temperature and parison control often improves yield and stabilizes weight, which reduces scrap cost and energy per good.

  Visual

  • Simple A vs. B chart: two bars for cost per bottle, annotated with the OEE‑backed good units used in the denominator; keep a small table beneath with payback and IRR so decisions hinge on complete financials, not just CapEx.

  Conclusión

  A low sticker price can hide higher energy, scrap, and downtime. By defining scope, loading real OEE, and converting everything to cost per good bottle, you make cleaner comparisons and faster payback decisions. Use the 8-step sheet, then request vendor data in the same format to compare apples-to-apples. Want a ready-to-use TCO calculator tailored for moldeo por soplado por extrusión and SKU changeovers? Ask for the spreadsheet, and I’ll share it.