Issue link: https://industrialengineer.epubxp.com/i/905872
32 ISE Magazine | www.iise.org/ISEmagazine Tackling the difficult last mile no time windows and one-hour time windows. The results indicate that routing tools can reduce costs by approximately 13 percent. Investing in eco-friendly fleets can improve the image of the company, but it also can affect costs. The cost model shows that fuel efficient fleets do not have a significant effect on last- mile costs in the city; however, the results are slightly more meaningful when deliveries are made across the state. Using cargo bicycles for delivery in the city center could save 30 percent to 40 percent, according to the cost model. The last-mile costs can be competitive even if time windows are offered. Of course, using cargo bikes for last-mile delivery is feasible only in certain sections, even in urban areas. Returns represent a big threat to last-mile optimization. However, the effect varies across industry sectors. Three dif- ferent sectors were considered in the cost model: fashion and accessories, sports and recreation, living and furnishing. Re- turn rate data from Germany was used as a baseline and modi- fied based on industry expert opinion. Fashion return rates (15.83 percent) are almost twice that of sports and recreation (8 percent) and almost three times that of the living and fur- nishing sector (5.62 percent). The model results, shown in Figure 2, indicate a big differ- ence among the three sectors, with returns of fashion goods increasing costs by more than 30 percent. Improving the sys- tems for reverse logistics and materials handling can signifi- cantly decrease the effect of returns on last-mile costs, espe- cially in urban areas. While a high-fashion dress might not take up much space, numerous deliveries involve items that are large and/or heavy, products like furniture, expensive electronics and televisions. A number of companies, including Ikea, even offer assembly and installation for an extra fee. These scenarios often use larger vehicles and reduce the number of stops. There trips might have additional insurance costs and may need another worker to help with delivery or installation. The cost model shown in Figure 2 indicates that the last-mile costs can be 10 to 15 times the base reference cost. Combining individual strategies The number of combinations increases exponentially as col- lection points, secure boxes, cargo bicycles, time windows, routing tools, handling time improvements, wages, fuel ef- ficiency improvements (hybrids) and returns are incorporated into the model simultaneously. To gain interesting insights, three different scenarios have been simulated using the cost model: a "customer service fo- cus" scenario, a "delivery mode focus" scenario and a "tech- nological innovations focus" scenario. The results are shown only for the city; however, users can broadly extend the results to the metropolitan areas and to the state. In the customer service focus scenario, the logistics features that increase customer service, namely time windows and re- turns pickup and handling, were added incrementally to the base reference case. The cost model results show that the cus- tomer service focus has a significant impact on the costs of the last mile. The ratio between the most expensive and the cheapest option is 7.67. Figure 3 shows the cost impact of incrementally adding 10 percent returns, one-hour time windows and 100 percent col- lection points. Frequent returns of products may result in cost increases exceeding 50 percent, and offering time windows can more than double the cost. However, extensively using collection points can significantly lower the cost of offering time windows but has the drawback of lowering perceived customer service. In the delivery mode focus scenario, the logistics features that focus on delivery mode improvements, namely cargo bikes, collection points and secure boxes, were added in- crementally to the base reference case. Returns and materi- als handling were added first, followed by the use of cargo bikes, collection points and secure boxes. The ratio between the most expensive and the cheapest option was 2.41, which is substantially lower than the one obtained in the customer service focus scenario. Figure 3 shows a sample of the results from the model. The prospective savings that result from the use of collec- FIGURE 2 Heavy costs In general, improving materials handling and reverse logistics can cut last-mile logistics costs, but not enough to compensate for returns and oversize or heavyweight items. AREA Base reference case Fashion Sports Living Fashion Sports Living Only delivery Delivery + 50% install Delivery + 100% install Chicago 100 144 123 116 130 115 111 983 1,170 1,543 Chicago Met 117 164 140 133 149 133 128 1,049 1,247 1,641 Illinois 162 216 189 181 202 182 176 1,224 1,457 1,923 The base reference cost for the city of Chicago is $2.66. This has been indexed as 100. Standard returns Returns with improved materials handling and reverse logistics Oversize and/or heavyweight items