Case Study
The concept of a smart city integrated with a low-carbon logistics network for the manufacturing industry is described using two case studies. These case studies are taken from emerging and developed economies, where some aspects of the conceptual framework have been implemented to design the smart city while considering the fact of rapid industrialization of the manufacturing units in a city or country.
Case Study 1
This is a case of a logistics network for a manufacturing company (M1, M2, and M3) operating in several different locations in a large Indian city and procuring different types of raw material from various nearby sources (S1, S2, S3, and S4) to manufacture goods to meet local demand. All units of the manufacturing firm must procure raw material in a timely manner and then distribute it to warehouses (W1, W2, W3, W4, and W5) situated in the market area. This is shown in Figure 15.3. Due to local government and environmental regulations, the company has also to redesign its procurement and distribution network so as to minimize the carbon emissions generated during transportation, procurement, and holding of the raw material and finished goods inventory. All units can procure items through all possible existing routes using different transportation modes such as small-, medium-,
FIGURE 15.3
Distribution network for local city manufacturing units.
and large-sized trucks with varying capacities. These different transportation modes emit different carbon emissions to the city environment. In order to meet government regulations to minimize carbon emissions so as to save the carbon credit and minimize logistics costs, the company needs to optimize the logistics network. For this, the details of the carbon emissions from each transportation mode (i.e., carrier) must be used. The proposed conceptual framework can be applied in this case to optimize the low-carbon logistics network of the manufacturing units in a city to make and keep the city smart. The distance (d7-) data for all manufacturing units from the suppliers and then to all warehouses are taken into account. The distance (d7-) information is used to calculate the carbon being emitted to the city from each carrier type. All carriers having different mileage (Milm ) and carrying capacities emit different quantities of carbon (Fomt). The carbon emission (Fomt ) is calculated using Equation 15.1. Similarly, carbon is being emitted while procuring (Eot) and holding extra inventory (Ewt). Each carrier also emits carbon, which is considered as fixed carbon emission (Fmt). Considering all this information, total carbon emitted (TC?) for each route, that is, from suppliers to manufacturing units and from manufacturing units to warehouses, can be easily calculated by applying Equation 15.2. Based on the total carbon emitted (TC?), carrier capacity, supplier capacity, and demand raised/required at the manufacturing units, the low-carbon logistics network within the city can be minimized, keeping to the minimum level for emission of carbon. The low-carbon network not only makes the logistics smart but also makes the entire city a smart city. In this case, the low- carbon optimum logistics network is represented in Figure 15.4 by bold and dashed lines.
FIGURE 15.4
Low-carbon logistics network for local city manufacturing units for smart cities.
The bold dashed lines represent the low-carbon routes from all suppliers (Sj, S2, S3, and S4) to manufacturing units (M4, M2, and M3), and the bold continuous lines represent the low-carbon logistics routes from manufacturing units (M4, M2, and M3) to all warehouses (Wj, W2, W3, W4, and W5).
