Context
Publikovaná případová studie autorů Ferreira, Silva a Mesquita zkoumá výrobce zařízení pro ohřev teplé užitkové vody, který realizoval projekt DMAIC na jednom ze svých interních logistických procesů. Článek představuje případovou studii ukazující, jak výrobce zařízení pro ohřev teplé užitkové vody využil metodiku Six Sigma Define-Measure-Analyze-Improve-Control (DMAIC) ke zlepšení jednoho ze svých interních logistických procesů, konkrétně doplňování supermarkétu na výrobních linkách. Jedná se o přesně ten typ interního, na pohled neoslnivého procesu, který logističtí držitelé certifikace Green Belt často dostávají na starost: nikoli doručovací trasu směrem k zákazníkovi, ale okružní trvalou trasu (milk-run), která zajišťuje pohyb dílů ze skladů na výrobní linku uvnitř závodu.
A milk-run is a fixed-route internal delivery system: a tugger or small vehicle follows a set path at set intervals, dropping components at line-side “supermarkets” so operators never run out of parts. When a milk-run route takes longer than planned, either the line risks starving for parts or the company has to add headcount and vehicles to compensate. That gap between planned and actual route time is what triggered this project.
Problém
Problém spočíval v nejistotě ohledně doby doručení na těchto trasách – nejistotě, která vytvářela pufrovací zásoby a vrásky s plánováním pro linky, které obsluhovala. Článek popisuje, jak výrobce zařízení pro ohřev teplé užitkové vody použil metodiku Six Sigma DMAIC ke zlepšení jednoho ze svých interních logistických procesů, doplňování supermarkétu na výrobních linkách, a posunul jej směrem ke špičkové úrovni kvality. Jednoduše řečeno: existovaly tři okružní trasy milk-run a nikdo nedokázal spolehlivě říct, jak dlouho jakákoli z nich v daném cyklu potrvá. Některé jízdy skončily včas. Jiné trvaly tak dlouho, že ohrožovaly zásobování přímo u linky, a společnost neměla jak předvídat, které dny budou problematické.
Jak to Belt vymezil
A Green Belt project has to be small enough to run part-time over a few months, which means the scope has to be bounded before any data collection starts. In this case the scope was one clearly defined process: the milk-run replenishment cycle serving the production lines, measured by route completion time. The project did not try to redesign the whole internal supply chain, renegotiate supplier contracts, or touch external freight. It picked one measurable output, route time, with a specific and painful symptom: a meaningful share of runs breaching the 30-minute target.
That scoping choice matters for anyone studying this as a template. A Green Belt charter should name the process, the boundary (start and end point of the route, in this case), the primary metric, and the pain point in numbers wherever possible, not a vague complaint about “slow logistics.”
Tools applied, phase by phase
Define (Definovat)
The team defined the process as the fixed-route milk-run cycle from stores to line-side supermarkets, with route completion time as the Y. A SIPOC-style map would typically sit here: driver, vehicle, route, stops, and the production lines as customers of the process.
Measure (Měřit)
The team collected route time data across cycles and routes to establish a baseline. Establishing a baseline distribution is exactly the step where a Green Belt would run a process capability study against the 30-minute target, and check measurement system reliability before trusting the numbers.
Analyze (Analyzovat)
With baseline data in hand, the analysis stage is where a Green Belt trainee would apply the statistical toolkit taught at this level: comparing route times across drivers, shifts or routes with t-tests or ANOVA to see whether differences were due to assignable causes or common variation, and using root cause tools such as fishbone diagrams and stratification to separate route design issues from driver behaviour or stop sequencing. The published account focuses on the outcome rather than every intermediate statistical test, but the structure of the project, moving from a variable baseline to a clearly diagnosed set of causes, follows this same logic.
Improve (Zlepšovat)
Improvements targeted the sources of variation and excess time identified in Analyze, most likely route sequencing, stop consolidation and standardising how loads were staged. The result was a shift not just in average time but in the spread of times, which is the signature of a genuine variation-reduction fix rather than a one-off lucky day.
Control (řídit)
To hold the gain, a Green Belt would put the new route standard on a control chart tracking route time by cycle, with limits tied to the 30-minute target, and a response plan for out-of-control points. That is the mechanism that prevents a project from drifting back to baseline six months after the belt moves on to something else.
Jaký byl výsledek
Zdokumentovaný výsledek je konkrétní. Aplikace metodiky Six Sigma vedla ke snížení podílu tras, jejichž dokončení trvalo déle než 30 minut, ze 25 na 3 procenta, ke snížení koeficientu variability času tras ze 40 na 14 procent a ke snížení průměrného času trasy ze 31 na 24 minut. Tyto výsledky měly výrazný finanční dopad a umožnily odstranění jedné ze tří stávajících tras bez jakéhokoli negativního dopadu na proces doplňování supermarkétu, což vedlo ke snížení počtu odpracovaných hodin a nákladů díky zrušení dvou okružních jízd.
That last point is worth pausing on. The financial payoff here did not come from a single dramatic fix. It came from squeezing variability down far enough that the company could safely consolidate three routes into fewer, which cut vehicles and labour hours without ever touching the customer-facing side of the business. That is a common shape for internal logistics Green Belt projects: the win is capacity freed up, not a defect count reduced.
Co by si z toho měl účastník školení odnést
| Lesson | Why it matters at Green Belt level |
|---|---|
| Scope to one measurable process | The project stayed inside one internal route system, not the whole supply chain, which kept it doable part-time. |
| Track variability, not just the average | The variability reduction, not the mean time alone, is what let the company safely cut a route. |
| Let the data justify structural change | Consolidating routes was only defensible once the data showed the remaining routes could absorb the load reliably. |
| Control plans protect the gain | Any route-time win needs an ongoing chart and response rule, or the old variation creeps back. |
For a trainee studying logistics applications of DMAIC, the value of this case is that it shows a Green Belt-scale project delivering a hard operational outcome, fewer vehicles and fewer man-hours, from what looks on the surface like a routine internal delivery problem. It is a reminder that the discipline of process mapping, capability analysis, root cause work and a control plan applies just as much to a forklift route inside a factory as it does to a customer-facing shipment.
