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The 48-hour timeline, hour by hour<\/h2>\n
Hours 0-8 on day one are site arrival, safety briefing, sensor installation, network configuration (local WiFi or 4G uplink), and operator tablet setup. The installation is non-invasive: clamp-on current sensors, magnetic-mount vibration sensors, photoelectric sensors mounted to existing frames. No equipment is stopped, no PLCs are touched. The consulting team uses this period for stakeholder interviews on the affected lines \u2014 operators, line leads, maintenance staff \u2014 which contributes to the diagnostic independently of the POC.<\/p>\n
Hours 8-24 are the first production shift with data flowing. At this point the dashboard is live but the data is not yet statistically meaningful. The consulting team should watch the real-time feed to validate the sensors are capturing the events they should \u2014 if the line stops and the dashboard shows it stopping within a second, the sensor configuration is correct. If there is a lag or missed events, the vendor engineer adjusts on the spot.<\/p>\n
Hours 24-40 are the second production shift. By this point there is enough data for initial pattern recognition. The JEMBA AI layer starts generating hypothesis outputs: “availability losses on line A concentrated in changeover events between SKU-102 and SKU-104, average duration 38 minutes, 3x the target”; “micro-stop frequency on line B is 47 per shift, above the 12-per-shift benchmark, concentrated at station 4.” These are the raw material for the diagnostic deck.<\/p>\n
Hours 40-48 are data review, root-cause conversation with client operations leaders, and export of the findings into the consulting team’s deck. The vendor engineer typically leaves site at hour 48; the system continues running and producing data that the consulting team can reference for the rest of the engagement.<\/p>\n
What to put in the week-two diagnostic deck<\/h2>\n
The POC output changes the diagnostic deck in three specific ways. First, the OEE baseline slides move from estimated numbers to measured numbers with a clear methodology footnote \u2014 this is the single most credible upgrade a diagnostic can get. Second, the loss decomposition slides have actual timestamps and frequencies rather than hypothesis, which lets the team skip the usual debate with the client about “are these numbers right?” and move directly to “what do we do about them?”<\/p>\n
Third, the improvement program proposal gains specificity. Instead of a generic LEAN program proposal, the consulting team can propose targeted interventions: “reduce changeover time from 38 to 20 minutes on line A via SMED workshop in weeks 4-5; this alone should yield 4-5 OEE points”; “investigate station 4 bottleneck on line B via dedicated micro-stop Kaizen in weeks 6-7.” Specificity in the improvement proposal is what separates engagements that close with measurable impact from engagements that close with a strategy deck.<\/p>\n
Many consulting teams include the TeepTrak dashboard screenshots directly in the deck \u2014 with client permission \u2014 because the visual evidence of real-time OEE monitoring reinforces the credibility of the recommendation. The typical format is a two-slide appendix showing the dashboard as-is and a summary of the data quality baseline (event capture rate, sensor uptime, operator entry rate).<\/p>\n
Communicating the POC to the client sponsor<\/h2>\n
Client sponsors \u2014 typically plant managers, operations directors, or CIOs \u2014 have heard many “48-hour” and “quick-win” pitches before, most of which did not deliver. The credibility bar for this communication is higher than a generic vendor proposal.<\/p>\n
Three messages to lead with: the POC does not require any PLC work from the client IT team, the POC is structured to produce decision-quality data within 48 hours, and the POC can be shut down at any point with no residual footprint on the plant’s systems. These three messages address the three concerns that typically kill quick-win proposals: IT workload, time-to-insight uncertainty, and lock-in risk.<\/p>\n
The sponsor will often ask what happens at the end of the POC. The honest answer: the consulting team owns the data generated during the POC, the client decides whether to continue with a scaled deployment (typical next step: 4-8 weeks of additional lines) or to stop and take the POC findings forward with other tools. The decision is not pre-loaded. This framing builds trust; the opposite framing (“you’ll love it so much you’ll have to buy the full system”) does not.<\/p>\n
Common failure modes and how to avoid them<\/h2>\n
The POC fails when the consulting team skips the line selection work. A badly chosen line \u2014 one where OEE is already high, or where production volume is low during the POC window, or where the line is undergoing maintenance \u2014 produces data that does not generate diagnostic insight. The line selection conversation with the client should take at least an hour in kickoff week.<\/p>\n
The POC fails when the consulting team does not prep the operators. Operators who see the tablets appear without context will resist. A ten-minute briefing in the pre-shift meeting \u2014 “this is a tool to help us understand what is slowing the line down, it is not a performance surveillance tool, no one is getting reviewed on this data” \u2014 makes the difference between 90%+ entry rate and 30% entry rate.<\/p>\n
The POC fails when the consulting team does not carve out time to review the data. Data that is generated but not reviewed by the consulting team is wasted. Blocking two hours at hour 24 and two hours at hour 40 for the team to sit with the dashboard and the vendor engineer is the minimum. Skipping this ends with a week-two deck that references POC data but has not actually internalized it.<\/p>\n
How the POC changes the engagement economics<\/h2>\n
Consulting engagements with a 48-hour POC at the start consistently produce larger follow-on scope than engagements without. The mechanism is straightforward: the client sees measurable value within the first two weeks, which builds confidence in the consulting team’s technical judgment, which makes the client more receptive to scaled improvement programs, plant rollouts to adjacent sites, and multi-year transformation mandates.<\/p>\n
For the consulting firm, the economics are visible at engagement closeout. Engagements with the POC integrated routinely show 1.5-2x the follow-on conversion of engagements without. For the individual consultant, the effect is reputational: clients who saw the consulting team deliver measured OEE improvement within the engagement are materially more likely to be reference-able to the firm’s next prospect, and materially more likely to bring the consulting team back for the next phase of work.<\/p>\n
External references: Lean Manufacturing \u2014 Wikipedia<\/a> \u00b7 OEE \u2014 Wikipedia<\/a> \u00b7 SMED \u2014 Wikipedia<\/a><\/p>\nRelated reading: Why strategy consultants recommend lightweight OEE over MES<\/a> \u00b7 What 4 years at BCG taught me about OEE design<\/a> \u00b7 How consultants evaluate MES \/ OEE vendors for client recommendations<\/a><\/p>\n\nConsulting with a Manufacturing Client?<\/strong>
\nOur team has supported BCG, McKinsey, Bain, Kearney and Roland Berger engagements across 30+ countries. Free 48h POC on any client site \u2014 no client commitment required.<\/span>
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