Managers at sorting centres sometimes encounter a problem but are unable to determine what is causing it – and are therefore unable to fix it. For example, managers at one parcel sorting centre noticed that the recirculation rate for packages was far greater than it should be. They knew that this was because operators were unable to empty chutes fast enough, so they kept getting blocked. This excessive recirculation was hampering operations and limiting the efficiency of the centre.
Data analysis revealed that there was uneven parcel distribution to the chutes in the system. Some chutes were heavily used, whereas others were almost unused. This meant that some operators could not keep up with arriving parcels while others had nothing to do. Operators were also having to walk long distances between chutes (up to 16 km per day), which caused fatigue and wasted valuable time.
Data from a similar site in the network showed that the allocation of discharged items to chutes at this site was chaotic in comparison. This caused the observed situation where during peak hours operators were unable to satisfy system demand and chutes filled up and became blocked or jammed.
Based on this data, site managers were able to even out the distribution of parcels to chutes, which spread the workload more equally among the operators. They could also efficiently redirect operators to troubled areas. This eliminated the problem of blocked chutes and also reduced walking distances for operators.
By evening out distribution to the destination chutes, the site reduced the recirculation rate because chutes were no longer getting blocked. This meant higher capacity in the system and a greatly increased throughput.
The number of packages that were discharged at the first attempt (excluding rejects) increased by 3%. Even more impressively, the number of blocked chute errors fell by 19%.
3 %Increase of number of packages discharged on first attempt
19 %Decrease of number of blocked chute errors