Consecutive-clearance inspection model ($I=[1+(r-1)(1-p{)}^K{]}^{-1}$)

In the consecutive-clearance inspection model, a run of $K$ consecutive good items is required to leave full inspection. With $q=1-p$, $0<p<1$, its Markov chain has transitions $P_{i0}=p$, $P_{i,i+1}=q$ for $i<K$, and $P_{K0}=p/r$, $P_{KK}=1-p/r$. Thus every defect in the clearance phase resets the count, rather than preserving the cumulative total.

Writing $D=1+(r-1)q^K$, the stationary distribution isTo prove this, balance at $i=1,\dots ,K-1$ gives ${\pi }_i=q^i{\pi }_0$, while balance at $K$ gives $(p/r){\pi }_K=q^K{\pi }_0$. Summing the geometric series and normalizing gives ${\pi }_0=p/D$; balance at zero follows as well. The inspected fraction is $1-(1-1/r){\pi }_K=1/D$. The fraction of all production that is both defective and uninspected is $p(r-1)q^K/D$; the fraction among defective items alone is $(r-1)q^K/D$. All these long-run limits follow from the Markov chain ergodic theorem and the independent inspection coins. The distinction from cumulative clearance matters already at $K=2$.