Dataset and mathematical formulation for research paper "Reducing Water Impact in Textile Supply Chains: The Case of Hemp as a More Sustainable Textile Fiber"

SUPPLEMENTARY MATERIAL 1 Reducing Water Impact in Textile Supply Chains: The Case of Hemp as a More Sustainable Textile Fiber Section 1 contains complementary elements for the mathematical formulation of the optimization model proposed in the research paper, mainly the indices, sets, parameters, decision variables, and constraints. Section 2 is related to case study data collection and treatment. 1. Mathematical Formulation 1.1. Indices and related sets 𝑖, 𝑗 ∈ 𝐼 Locations of entities 𝐼 = 𝐼𝑠𝑢𝑝 ∪ 𝐼𝑚∪ 𝐼𝑓∪ 𝐼𝑤∪ 𝐼𝑐∪ 𝐼𝑎𝑖𝑟 ∪ 𝐼𝑠𝑒𝑎 ∪ 𝐼𝑟𝑎𝑖𝑙 𝐼𝑠𝑢𝑝 Suppliers 𝐼𝑚 Mills 𝐼𝑓 Factories 𝐼𝑤 Warehouses 𝐼𝑐 Clients 𝐼𝑎𝑖𝑟 Airports 𝐼𝑠𝑒𝑎 Seaports 𝐼𝑟𝑎𝑖𝑙 Railway stations 𝑝, 𝑞 ∈ 𝑃 Products 𝑃 = 𝑃 𝑟𝑚 ∪ 𝑃 𝑓𝑎𝑏 ∪ 𝑃 𝑓𝑝 𝑃 𝑟𝑚 Raw material 𝑃 𝑓𝑎𝑏 Fabric 𝑃 𝑓𝑝 Final product 𝑡 ∈ 𝑇 Transport modes 𝑇 = 𝑇𝑝𝑙𝑎𝑛𝑒 ∪ 𝑇𝑏𝑜𝑎𝑡 ∪ 𝑇𝑡𝑟𝑎𝑖𝑛 ∪ 𝑇𝑡𝑟𝑢𝑐𝑘 𝑇𝑝𝑙𝑎𝑛𝑒 Plane 𝑇𝑏𝑜𝑎𝑡 Boat 𝑇𝑡𝑟𝑎𝑖𝑛 Train SUPPLEMENTARY MATERIAL 2 𝑇𝑡𝑟𝑢𝑐𝑘 Truck 𝑘 ∈ 𝐾 Time periods 𝐾 = 𝐾𝑓𝑖𝑟𝑠𝑡 ∪ 𝐾𝑜𝑡ℎ𝑒𝑟 𝐾𝑓𝑖𝑟𝑠𝑡 First time period 𝐾𝑜𝑡ℎ𝑒𝑟 All but first time period 𝑐 ∈ 𝐶 Environmental midpoint categories 𝑈 Allowed entity-entity connections 𝑈 = {(𝑖, 𝑗): 𝑖, 𝑗 ∈ 𝐼} 𝑈𝑠𝑢𝑝𝑚 Connection between supplier and mill 𝑈𝑚𝑓 Connection between mill and factory 𝑈𝑓𝑐 Connection between factory and client 𝑈𝑓𝑤 Connection between factory and warehouse 𝑈𝑤𝑐 Connection between warehouse and client As for airports, seaports, and railways, all connections are possible. For transportation restrictions purposes, the following were also computed: 𝑈𝑙𝑎𝑛𝑑 Connection between non-island countries 𝑈𝑖𝑠𝑙𝑎𝑛𝑑𝑠 Connection between island countries 𝑈𝑙𝑎𝑛𝑑_𝑖𝑠𝑙𝑎𝑛𝑑𝑠 Connection between non-island and island countries 𝑈𝑖𝑠𝑙𝑎𝑛𝑑𝑠_𝑙𝑎𝑛𝑑 Connection between island and non-island countries 𝑉 Allowed product-entity relations 𝑉 = {(𝑝, 𝑖):𝑝 ∈ 𝑃 ˄ 𝑖 𝜖 𝐼} 𝑉 𝑠𝑢𝑝𝑟𝑚 Relation between supplier and raw material 𝑉 𝑚𝑟𝑚 Relation between mill and raw material 𝑉𝑚𝑓𝑎𝑏 Relation between mill and fabric 𝑉 𝑓𝑓𝑎𝑏 Relation between factory and fabric 𝑉 𝑓𝑓𝑝 Relation between factory and final product SUPPLEMENTARY MATERIAL 3 𝑉𝑤𝑓𝑝 Relation between warehouse and final product 𝑉𝑐𝑓𝑝 Relation between client and final product As for airports, seaports and railways, all relations are possible. 𝐹 Allowed flows of materials between entities 𝐹 = {(𝑝, 𝑖, 𝑗): (𝑝, 𝑖) ∈ 𝑉 ˄ (𝑖, 𝑗) 𝜖 𝑈} For the description of this subset, please consider the following examples: 𝐹𝑂𝑈𝑇𝑠𝑢𝑝𝑟𝑚 Flow out (OUT) of raw material (RM) that leaves suppliers (SUP) and enters entity 𝑗 𝐹𝐼𝑁𝑚𝑟𝑚 Flow in (IN) of raw material (RM) that leaves an entity 𝑖 and enters the mills (M) 𝑁𝑒𝑡 Allowed transport modes in connections between entities 𝑁𝑒𝑡 = {(𝑡, 𝑖, 𝑗): 𝑡 ∈ 𝑇 ˄ (𝑖, 𝑗) 𝜖 𝑈} 𝑁𝑒𝑡𝑝𝑙𝑎𝑛𝑒 Connections between airports by plane 𝑁𝑒𝑡𝑏𝑜𝑎𝑡 Connections between seaports by boat 𝑁𝑒𝑡𝑡𝑟𝑎𝑖𝑛 Connections between railway stations by train 𝑁𝑒𝑡𝑡𝑟𝑢𝑐𝑘 All remaining connections 𝑁𝑒𝑡𝐶𝑜𝑛 Network with all allowed connections 𝑁𝑒𝑡𝐶𝑜𝑛 = {(𝑡, 𝑝, 𝑖, 𝑗):(𝑡, 𝑖, 𝑗)∈ 𝑁𝑒𝑡 ˄ (𝑝, 𝑖, 𝑗)𝜖 𝐹} 1.2. Parameters Parameters are grouped by type, namely entity, product, transport and environment, costs, and others. Entity related parameters 𝑠𝑐𝑎𝑝𝑝𝑖 Maximum supply capacity for raw material 𝑝 by supplier 𝑖 (ton) 𝑚𝑐𝑎𝑝𝑖 Maximum production capacity in mill 𝑖 (ton) 𝑓𝑐𝑎𝑝𝑖 Maximum production capacity in factory 𝑖 (ton) 𝑤𝑐𝑎𝑝𝑖 Maximum inventory capacity in warehouse 𝑖 (ton) 𝑖𝑖𝑝𝑖 Inventory level of product 𝑝 in warehouse 𝑖 in time period 1 (ton) SUPPLEMENTARY MATERIAL 4 𝑤𝑖 Necessary number of workers in entity 𝑖 to produce/store one ton of product 𝑝𝑖 Price per unit sold in client 𝑖 (c.u.) 𝑑𝑖𝑘 Demand by client 𝑖 in time period 𝑘 (ton) 𝑤𝑠𝑖 Water stress level index for each entity 𝑖 𝑤𝑐𝑓 Water consumption of fabric production (m3/ton) 𝑤𝑐𝑓𝑝 Water consumption of final product production (m3/ton) Product related parameters 𝐵𝑂𝑀𝑝𝑞 𝑓𝑎𝑏 First stage production bill of materials that specifies the amount of raw material 𝑝 necessary to produce one ton of fabric 𝑞 𝐵𝑂𝑀𝑝𝑞 𝑓𝑝 Last stage production bill of materials that specifies the amount of fabric 𝑝 necessary to produce one ton of final product 𝑞 𝐵𝑂𝑀𝑝𝑞 𝑚 Bill of materials at mills 𝐵𝑂𝑀𝑝𝑞 𝑓𝑤 Bill of materials at factories and warehouses 𝐵𝑂𝑀𝑝𝑞 𝑡 Bill of materials at airports, seaports and railway stations 𝑤𝑓𝑝𝑖 Water footprint of each raw material 𝑝 in each location of supplier 𝑖 𝑓𝑝𝑤 Final product’s weight (ton) Transport and Environment related parameters 𝑤𝑐𝑡𝑡 Water consumption of each transport mode 𝑡 (per tkm) 𝑤𝑡 Average number of jobs created per transport mode 𝑡 in airports, seaports and railway station (per tkm) 𝑒𝑖𝑝𝑐 𝑟𝑚 Environmental impact characterization factor of raw material 𝑝 processing, at midpoint category 𝑐 (per ton) 𝑒𝑖𝑝𝑐 𝑓𝑎𝑏 Environmental impact characterization factor of fabric 𝑝 production, at midpoint category 𝑐 (per ton) 𝑒𝑖𝑝𝑐 𝑓𝑝 Environmental impact characterization factor of final product 𝑝 production, at midpoint category 𝑐 (per ton) 𝑒𝑖𝑡𝑐 Environmental impact characterization factor of transport mode t, at midpoint category 𝑐 (per tkm) SUPPLEMENTARY MATERIAL 5 𝑛𝑐 Normalization factor for midpoint category 𝑐 Costs 𝑟𝑚𝑐𝑝𝑖 Cost of raw material 𝑝 supplied by supplier 𝑖 (c.u./ton) 𝑝𝑚𝑐 Production cost at mills (c.u./ton) 𝑝𝑓𝑐 Production cost at factories (c.u./ton) 𝑖𝑐 Inventory cost at warehouses (c.u./ton) ℎ𝑐𝑖 Hub cost of entity 𝑖 (c.u.) 𝑡𝑐𝑖𝑡 Transport cost for each transport mode 𝑡 from location 𝑖, per tkm (c.u.) 𝑙𝑎𝑏𝑐𝑖 Labor cost of entity 𝑖 (c.u.) 𝑙𝑐𝑖 Lease cost of entity 𝑖 (c.u.) Others 𝑑𝑖𝑠𝑡𝑖𝑗 Distance between entity 𝑖 and entity 𝑗 (km) 𝐵𝑖𝑔𝑀 Large number 𝑆𝑚𝑎𝑙𝑙𝑀 Small number 𝑛𝑦 Time horizon (years) 𝑡𝑟 Tax rate 𝑖𝑟 Interest rate 1.3. Decision Variables Continuous and positive variables 𝑋𝑝𝑖𝑗𝑡𝑘 Amount of product 𝑝 transported by transport mode 𝑡 from entity 𝑖 to entity 𝑗 in time period 𝑘 𝑆𝑝𝑖𝑘 Amount of inventory of final product 𝑝 in warehouse 𝑖 in time period 𝑘 𝑊𝑝𝑖𝑘 Amount of product 𝑝 produced in entity 𝑖 in time period 𝑘 Binary variable 𝑌 𝑖 = 1 if entity 𝑖 is used (excludes suppliers), 0 otherwise SUPPLEMENTARY MATERIAL 6 1.4. Constraints Material Balances Material balance at mills 𝑊𝑝𝑖𝑘 =∑𝐵𝑂𝑀𝑝𝑞 𝑚⋅ 𝑋𝑞𝑖𝑗𝑡𝑘 𝑡:(𝑡,𝑞,𝑖,𝑗) ∈ 𝑁𝑒𝑡𝐶𝑜𝑛 (𝑞,𝑗):(𝑞,𝑖,𝑗) ∈ 𝐹𝑂𝑈𝑇𝑚𝑓𝑎𝑏 , 𝑝 ∈ 𝑃 𝑓𝑎𝑏 𝑖 ∈ 𝐼𝑚 ∧ 𝑘 ∈ 𝐾 (1) ∑𝑋𝑝𝑖𝑗𝑡𝑘 𝑡:(𝑡,𝑝,𝑖,𝑗) ∈ 𝑁𝑒𝑡𝐶𝑜𝑛 𝑖:(𝑝,𝑖,𝑗) ∈ 𝐹𝐼𝑁𝑚𝑟𝑚 = ∑𝑊𝑞𝑗𝑘𝑞 ∈ 𝑃𝑓𝑎𝑏 ⋅ 𝐵𝑂𝑀𝑝𝑞 𝑓𝑎𝑏, 𝑗 ∈ 𝐼𝑚 ∧ 𝑝 ∈ 𝑃 𝑟𝑚 ∧ 𝑘 ∈ 𝐾 (2) Material balance at factories 𝑊𝑝𝑖𝑘 =∑𝐵𝑂𝑀𝑝𝑞 𝑓𝑤 ⋅ 𝑋𝑞𝑖𝑗𝑡𝑘 𝑡:(𝑡,𝑞,𝑖,𝑗) ∈ 𝑁𝑒𝑡𝐶𝑜𝑛 (𝑞,𝑗):(𝑞,𝑖,𝑗) ∈ 𝐹𝑂𝑈𝑇𝑓𝑓𝑝 , 𝑝 ∈ 𝑃 𝑓𝑝 𝑖 ∈ 𝐼𝑓 ∧ 𝑘 ∈ 𝐾 (3) ∑𝑋𝑝𝑖𝑗𝑡𝑘 𝑡:(𝑡,𝑝,𝑖,𝑗) ∈ 𝑁𝑒𝑡𝐶𝑜𝑛 𝑖:(𝑝,𝑖,𝑗) ∈ 𝐹𝐼𝑁𝑓𝑓𝑎𝑏 = ∑𝑊𝑞𝑗𝑘𝑞 ∈ 𝑃𝑓𝑝 ⋅ 𝐵𝑂𝑀𝑝𝑞 𝑓𝑝, 𝑗 ∈ 𝐼𝑓 ∧ 𝑝 ∈ 𝑃 𝑓𝑎𝑏 ∧ 𝑘 ∈ 𝐾 (4) Material balance at warehouses in time period 1 𝑖𝑖𝑝𝑖 +∑𝐵𝑂𝑀𝑝𝑞 𝑓𝑤 ⋅ 𝑋𝑞𝑖𝑗𝑡𝑘 𝑡:(𝑡,𝑞,𝑖,𝑗) ∈ 𝑁𝑒𝑡𝐶𝑜𝑛 (𝑞,𝑗):(𝑞,𝑖,𝑗) ∈ 𝐹𝐼𝑁𝑤𝑓𝑝 = 𝑆𝑝𝑖𝑘 +∑𝐵𝑂𝑀𝑝𝑞 𝑓𝑤 ⋅ 𝑋𝑞𝑖𝑗𝑡𝑘 𝑡:(𝑡,𝑞,𝑖,𝑗) ∈ 𝑁𝑒𝑡𝐶𝑜𝑛 (𝑞,𝑗):(𝑞,𝑖,𝑗) ∈ 𝐹𝑂𝑈𝑇𝑤𝑓𝑝 ,(5) 𝑖 ∈ 𝐼𝑊∧ 𝑝 ∈ 𝑃 𝑓𝑝 ∧ 𝑘 ∈ 𝐾𝑓𝑖𝑟𝑠𝑡 Material balance at warehouses in remaining time periods 𝑆𝑝𝑖(𝑘−1)+∑𝐵𝑂𝑀𝑝𝑞 𝑓𝑤 ⋅ 𝑋𝑞𝑖𝑗𝑡𝑘 𝑡:(𝑡,𝑞,𝑖,𝑗) ∈ 𝑁𝑒𝑡𝐶𝑜𝑛 (𝑞,𝑗):(𝑞,𝑖,𝑗) ∈ 𝐹𝐼𝑁𝑤𝑓𝑝 = 𝑆𝑝𝑖𝑘 +∑𝐵𝑂𝑀𝑝𝑞 𝑓𝑤 ⋅ 𝑋𝑞𝑖𝑗𝑡𝑘 𝑡:(𝑡,𝑞,𝑖,𝑗) ∈ 𝑁𝑒𝑡𝐶𝑜𝑛 (𝑞,𝑗):(𝑞,𝑖,𝑗) ∈ 𝐹𝑂𝑈𝑇𝑤𝑓𝑝 ,(6) 𝑖 ∈ 𝐼𝑊∧ 𝑝 ∈ 𝑃 𝑓𝑝 ∧ 𝑘 ∈ 𝐾𝑜𝑡ℎ𝑒𝑟 Cross-docking at airports, seaports, and railway stations ∑𝐵𝑂𝑀𝑝𝑞 𝑡⋅ 𝑋𝑞𝑖𝑗𝑡𝑘 𝑡:(𝑡,𝑞,𝑖,𝑗) ∈ 𝑁𝑒𝑡𝐶𝑜𝑛 (𝑞,𝑗):(𝑞,𝑖,𝑗) ∈ (𝐹𝐼𝑁𝑎𝑖𝑟𝑟𝑚 ∪ 𝐹𝐼𝑁𝑎𝑖𝑟𝑓𝑎𝑏∪ 𝐹𝐼𝑁𝑎𝑖𝑟𝑓𝑝) = ∑𝐵𝑂𝑀𝑝𝑞 𝑡⋅ 𝑋𝑞𝑖𝑗𝑡𝑘 𝑡:(𝑡,𝑞,𝑖,𝑗) ∈ 𝑁𝑒𝑡𝐶𝑜𝑛 (𝑞,𝑗):(𝑞,𝑖,𝑗) ∈ (𝐹𝑂𝑈𝑇𝑎𝑖𝑟𝑟𝑚 ∪ 𝐹𝑂𝑈𝑇𝑎𝑖𝑟𝑓𝑎𝑏∪𝐹𝑂𝑈𝑇𝑎𝑖𝑟𝑓𝑝) ,(7) (𝑝, 𝑖) ∈ (𝑉𝑎𝑖𝑟𝑟𝑚 ∪ 𝑉𝑎𝑖𝑟𝑓𝑎𝑏 ∪ 𝑉𝑎𝑖𝑟𝑓𝑝) ∧ 𝑘 ∈ 𝐾 ∑𝐵𝑂𝑀𝑝𝑞 𝑡⋅ 𝑋𝑞𝑖𝑗𝑡𝑘 𝑡:(𝑡,𝑞,𝑖,𝑗) ∈ 𝑁𝑒𝑡𝐶𝑜𝑛 (𝑞,𝑗):(𝑞,𝑖,𝑗) ∈ (𝐹𝐼𝑁𝑠𝑒𝑎𝑟𝑚 ∪ 𝐹𝐼𝑁𝑠𝑒𝑎𝑓𝑎𝑏∪ 𝐹𝐼𝑁𝑠𝑒𝑎𝑓𝑝) = ∑𝐵𝑂𝑀𝑝𝑞 𝑡⋅ 𝑋𝑞𝑖𝑗𝑡𝑘 𝑡:(𝑡,𝑞,𝑖,𝑗) ∈ 𝑁𝑒𝑡𝐶𝑜𝑛 (𝑞,𝑗):(𝑞,𝑖,𝑗) ∈ (𝐹𝑂𝑈𝑇𝑠𝑒𝑎𝑟𝑚 ∪ 𝐹𝑂𝑈𝑇𝑠𝑒𝑎𝑓𝑎𝑏∪𝐹𝑂𝑈𝑇𝑠𝑒𝑎𝑓𝑝) ,(8) (𝑝, 𝑖) ∈ (𝑉 𝑠𝑒𝑎𝑟𝑚 ∪ 𝑉𝑠𝑒𝑎𝑓𝑎𝑏 ∪ 𝑉𝑠𝑒𝑎𝑓𝑝) ∧ 𝑘 ∈ 𝐾 ∑𝐵𝑂𝑀𝑝𝑞 𝑡⋅ 𝑋𝑞𝑖𝑗𝑡𝑘 𝑡:(𝑡,𝑞,𝑖,𝑗) ∈ 𝑁𝑒𝑡𝐶𝑜𝑛 (𝑞,𝑗):(𝑞,𝑖,𝑗) ∈ (𝐹𝐼𝑁𝑟𝑎𝑖𝑙𝑟𝑚 ∪ 𝐹𝐼𝑁𝑟𝑎𝑖𝑙𝑓𝑎𝑏∪ 𝐹𝐼𝑁𝑟𝑎𝑖𝑙𝑓𝑝) = ∑𝐵𝑂𝑀𝑝𝑞 𝑡⋅ 𝑋𝑞𝑖𝑗𝑡𝑘 𝑡:(𝑡,𝑞,𝑖,𝑗) ∈ 𝑁𝑒𝑡𝐶𝑜𝑛 (𝑞,𝑗):(𝑞,𝑖,𝑗) ∈ (𝐹𝑂𝑈𝑇𝑟𝑎𝑖𝑙𝑟𝑚 ∪ 𝐹𝑂𝑈𝑇𝑟𝑎𝑖𝑙𝑓𝑎𝑏∪𝐹𝑂𝑈𝑇𝑟𝑎𝑖𝑙𝑓𝑝) ,(9) (𝑝, 𝑖) ∈ (𝑉𝑟𝑎𝑖𝑙𝑟𝑚 ∪ 𝑉𝑟𝑎𝑖𝑙𝑓𝑎𝑏 ∪ 𝑉𝑟𝑎𝑖𝑙𝑓𝑝) ∧ 𝑘 ∈ 𝐾 Constraints (1) and (2) model the material balance at mills. Constraint (1) assures that the production of fabric at mills (first term), during all time periods, is equal to the outgoing flow of fabric (second term). Constraint (2) sets the necessary amount of raw material to be sent by the supplier, by matching the amount of ingoing flow of raw material (first term) with the level SUPPLEMENTARY MATERIAL 7 of production of fabric (second term). These constraints ensure that there is no possibility of stock in mills, as all the flow of material that enters the mills is used for production in that same time period. Similarly, constraints (3) and (4) model the material balance at factories, ensuring there is no possibility for stock at the factories. Constraints (5) and (6) express the material balance at the warehouses. The first assures that, during the first time period, the initial stock of the final product and its inbound flow to the warehouse (first term) is equal to the amount kept in stock plus the outgoing flow of the final product (second term). Constraint (6) models the remaining time periods by replacing the initial stock of the final product 𝑖𝑖𝑝𝑖 with the remaining stock from the previous time period 𝑆𝑝𝑖(𝑘−1). The airports, seaports, and railway stations operate in a cross-docking mode; therefore, no stock is made available at these sites. Constraints (7), (8), and (9) state that the inbound flow at an airport, seaport, and railway station, respectively, equal their outbound flow. Entity capacity constraints Entity existence constraints ∑𝑋𝑝𝑖𝑗𝑡𝑘 (𝑡,𝑝,𝑗):(𝑡,𝑝,𝑖,𝑗) ∈ 𝑁𝑒𝑡𝐶𝑜𝑛 ≤ 𝐵𝑖𝑔𝑀 ⋅ 𝑌 𝑖, 𝑖 ∈ 𝐼\𝐼𝑠𝑢𝑝 ∧ 𝑘 ∈ 𝐾 (10) ∑𝑋𝑝𝑖𝑗𝑡𝑘 (𝑡,𝑝,𝑗):(𝑡,𝑝,𝑖,𝑗) ∈ 𝑁𝑒𝑡𝐶𝑜𝑛 ≥ 𝑆𝑚𝑎𝑙𝑙𝑀 ⋅ 𝑌 𝑖, 𝑖 ∈ 𝐼\𝐼𝑠𝑢𝑝 ∧ 𝑘 ∈ 𝐾 (11) Supply capacity ∑𝑋𝑝𝑖𝑗𝑡𝑘 (𝑡,𝑗):(𝑡,𝑝,𝑖,𝑗) ∈ 𝑁𝑒𝑡𝐶𝑜𝑛 𝑗:(𝑝,𝑖,𝑗) ∈ 𝐹𝑂𝑈𝑇𝑠𝑢𝑝𝑟𝑚 ≤ 𝑠𝑐𝑎𝑝𝑝𝑖, 𝑖 ∈ 𝐼𝑠𝑢𝑝 ∧ 𝑝 ∈ 𝑃 𝑟𝑚 ∧ 𝑘 ∈ 𝐾 (12) Production capacities ∑𝑊𝑝𝑖𝑘 𝑝 ∈ 𝑃𝑓𝑎𝑏 ≤ 𝑚𝑐𝑎𝑝𝑖⋅ 𝑌 𝑖 , 𝑖 ∈ 𝐼𝑚 ∧ 𝑘 ∈ 𝐾 (13) ∑𝑊𝑝𝑖𝑘 𝑝 ∈ 𝑃𝑓𝑎𝑏 ≥ 0.5 ⋅ 𝑚𝑐𝑎𝑝𝑖⋅ 𝑌 𝑖 , 𝑖 ∈ 𝐼𝑚 ∧ 𝑘 ∈ 𝐾 (14) ∑𝑊𝑝𝑖𝑘 𝑝 ∈ 𝑃𝑓𝑝 ≤ 𝑓𝑐𝑎𝑝𝑖⋅ 𝑌 𝑖 , 𝑖 ∈ 𝐼𝑓 ∧ 𝑘 ∈ 𝐾 (15) ∑𝑊𝑝𝑖𝑘 𝑝 ∈ 𝑃𝑓𝑝 ≥ 0.5 ⋅ 𝑓𝑐𝑎𝑝𝑖⋅ 𝑌 𝑖 , 𝑖 ∈ 𝐼𝑓 ∧ 𝑘 ∈ 𝐾 (16) Inventory capacity ∑𝑆𝑝𝑖𝑘 𝑝 ∈ 𝑃𝑓𝑝 ≤ 𝑤𝑐𝑎𝑝𝑖⋅ 𝑌 𝑖 , 𝑖 ∈ 𝐼𝑤 ∧ 𝑘 ∈ 𝐾 (17) Constraints (10) and (11) define the decision variable 𝑌 𝑖, stating that, within the network, an entity is only used (𝑌 𝑖 = 1) if there is an ingoing flow to that same entity. These constraints exclude the suppliers, as they only have an outgoing flow. Constraints (12) to (17) set capacity limits. The maximum supply capacity is modeled through constraint (12). The maximum and minimum production capacity limits at mills – constraints (13) and (14), and at factories – constraints (15) and (16). Both in mills and factories, the production level must reach at least 50% of the total production capacity of that entity, for it to integrate the network. A minimum capacity ensures that mills and factories are not being used at an undesired low capacity and that the production levels are more evenly distributed. Constraint (17) sets the maximum inventory capacity for warehouses. SUPPLEMENTARY MATERIAL 8 Transportation constraints ∑𝑋𝑝𝑖𝑗𝑡𝑘 (𝑡,𝑗):(𝑡,𝑝,𝑖,𝑗) ∈ 𝑁𝑒𝑡𝐶𝑜𝑛 𝑗 ∈ 𝐼\(𝐼𝑎𝑖𝑟) = ∑𝑋𝑝𝑖𝑗𝑡𝑘 (𝑡,𝑗):(𝑡,𝑝,𝑖,𝑗) ∈ 𝑁𝑒𝑡𝐶𝑜𝑛 𝑗 ∈ 𝐼𝑎𝑖𝑟 ,(18) (𝑝, 𝑖)∈ (𝑉𝑎𝑖𝑟𝑟𝑚 ∪ 𝑉𝑎𝑖𝑟𝑓𝑎𝑏 ∪ 𝑉𝑎𝑖𝑟𝑓𝑝) ∧ 𝑘 ∈ 𝐾 ∑𝑋𝑝𝑖𝑗𝑡𝑘 (𝑡,𝑗):(𝑡,𝑝,𝑖,𝑗) ∈ 𝑁𝑒𝑡𝐶𝑜𝑛 𝑗 ∈ 𝐼\(𝐼𝑠𝑒𝑎) = ∑𝑋𝑝𝑖𝑗𝑡𝑘 (𝑡,𝑗):(𝑡,𝑝,𝑖,𝑗) ∈ 𝑁𝑒𝑡𝐶𝑜𝑛 𝑗 ∈ 𝐼𝑠𝑒𝑎 ,(19) (𝑝, 𝑖)∈ (𝑉 𝑠𝑒𝑎𝑟𝑚 ∪ 𝑉𝑠𝑒𝑎𝑓𝑎𝑏 ∪ 𝑉𝑠𝑒𝑎𝑓𝑝) ∧ 𝑘 ∈ 𝐾 ∑𝑋𝑝𝑖𝑗𝑡𝑘 (𝑡,𝑗):(𝑡,𝑝,𝑖,𝑗) ∈ 𝑁𝑒𝑡𝐶𝑜𝑛 𝑗 ∈ 𝐼\(𝐼𝑟𝑎𝑖𝑙) = ∑𝑋𝑝𝑖𝑗𝑡𝑘 (𝑡,𝑗):(𝑡,𝑝,𝑖,𝑗) ∈ 𝑁𝑒𝑡𝐶𝑜𝑛 𝑗 ∈ 𝐼𝑟𝑎𝑖𝑙 ,(20) (𝑝, 𝑖)∈ (𝑉𝑟𝑎𝑖𝑙𝑟𝑚 ∪ 𝑉𝑟𝑎𝑖𝑙𝑓𝑎𝑏 ∪ 𝑉𝑟𝑎𝑖𝑙𝑓𝑝) ∧ 𝑘 ∈ 𝐾 The above constraints ensure that the flow of material going into an airport (18), seaport (19) and railway station (20) are transported to another airport, seaport and railway station, respectively. Furthermore, they ensure that the transportation is made through a plane, for constraint (18), a boat for constraint (19) and a train for constraint (20). Demand constraint ∑𝑋𝑝𝑖𝑗𝑡𝑘 𝑡:(𝑡,𝑝,𝑖,𝑗) ∈ 𝑁𝑒𝑡𝐶𝑜𝑛 (𝑝,𝑖):(𝑝,𝑖,𝑗) ∈ 𝐹𝐼𝑁𝑐𝑓𝑝 = 𝑑𝑖𝑘, 𝑗 ∈ 𝐼𝑐 ∧ 𝑘 ∈ 𝐾 (21) The demand is modelled through constraint (21). Since the demand by each client must be satisfied, this constraint operates as a driver for all the network’s flows. SUPPLEMENTARY MATERIAL 9 2. Case Study Data This section describes the input data collection and treatment associated with each parameter. Demand Market demand is introduced through parameter 𝑑𝑖𝑘. The 14 clients considered and their respective annual demand of final product, in tons, is shown in Table 1. Due to a lack of information concerning the amount and location of stores in each country, each client’s demand considered is the country’s aggregated demand. Thus, the location for each client corresponds to the geographical center of the country. Table 1: Annual demand per client (𝑑𝑖𝑘). Demand (ton) Client / Year 2023 2024 2025 2026 2027 C1: Cambodia 4.5 5 5.5 6.1 6.7 C2: China 372.9 410.2 451.2 496.3 545.9 C3: India 363.6 400 440 484 532.4 C4: Indonesia 72.2 79.4 87.3 96 105.6 C5: Japan 32.1 35.3 38.8 42.7 47 C6: Malaysia 8.6 9.5 10.5 11.6 12.8 C7: Nepal 7.9 8.7 9.6 10.6 11.7 C8: Pakistan 59.9 65.9 72.5 79.8 87.8 C9: Philippines 29.2 32.1 35.3 38.8 42.7 C10: Singapore 1.5 1.7 1.9 2.1 2.3 C11: South Korea 13.2 14.5 16 17.6 19.4 C12: Sri Lanka 5.6 6.2 6.8 7.5 8.3 C13: Thailand 18 19.8 21.8 24 26.4 C14: Vietnam 25.6 28.2 31 34.1 37.5 Note that each client’s annual demand means to represent the entire country’s demand of final product, however, the values presented are merely indicative, since there was no available information on this matter. The values presented for 2023 were based on an apparel company’s total net revenue and cost of goods sold, associated with the Asian continent (Levi Strauss & Co., 2022). From the sum of these values, a total revenue per client was obtained, considering the population size of each country, having been assumed that population’s size is correlated with demand’s size. This data was used to estimate reasonable demand quantities in each market. According to 2017 forecasts, spending by the global middle class in Asia Pacific was expected to increase by 102% from 2020 to 2030, translating to an average of 10% increase per year (Kharas, 2017). Therefore, the forecasted demand’s annual growth was assumed to be 10%. It is important to mention that it was acknowledged that this forecast was made prior to the COVID-19 pandemic and the current war in Ukraine, events that caused and ensued significant disruptions in the world’s population and economies. Nonetheless, according to Fengler et al. (2022), the global middle class in Asia has recovered strongly from both crises. The final product’s price per country, denoted as 𝑝𝑖, is presented in Table 2. SUPPLEMENTARY MATERIAL 16 Transportation related parameters The airports, seaports, and railway stations mentioned above, and their respective annual hub cost (ℎ𝑐𝑖), can be consulted in Table 11. Table 11: Annual hub costs (ℎ𝑐𝑖). Transport Infrastructure (Airport (A), Seaport (SP), Railway Station (R)) Hub Cost (c.u.) A1: Austria 289 476 A2: Bangladesh 67 657 A3: Cambodia 96 351 A4: China 121 670 A5: Egypt 35 868 A6: India 70 329 A7: Indonesia 86 083 A8: Japan 346 583 A9: Malaysia 96 211 A10: Pakistan 76 659 A11: Philippines 91 991 A12: South Korea 167 384 A13: Sri Lanka 72 580 A14: Thailand 71 877 A15: Turkey 134 470 A16: Vietnam 94 804 SP1: Bangladesh 67 657 SP2: Cambodia 96 351 SP3: China 121 670 SP4: China 121 670 SP5: India 70 329 SP6: Indonesia 86 083 SP7: Pakistan 76 659 SP8: Sri Lanka 72 580 SP9: Vietnam 94 804 R1: Bangladesh (Dhaka) 67 657 R2: Bangladesh (Chittagong) 67 657 R3: China (Shenzhen) 121 670 R4: China (Shanghai) 121 670 R5: India (New Delhi) 70 329 R6: India (Mumbai) 70 329 R7: Pakistan (Karachi) 76 659 R8: Pakistan (Faisalabad) 76 659 SUPPLEMENTARY MATERIAL 17 The hub fixed costs displayed were obtained by assuming a hub cost for France (Mota et al., 2018) and considering France’s price level index 4 , specific for transport services (The World Bank, 2017). Then, through the price level index, for transport services in each country, their respective hub cost was obtained. As for the transport requirements of the supply chain, the company relies on an outsourcing strategy. Considering the extensive geographical footprint of its supply chain and the location of its clients, the company also counts on transport by truck, train, plane, and boat. The transportation costs for each transport mode in each country (𝑡𝑐𝑖𝑡) were calculated similarly to the hub costs and are presented in Table 12, per country. Based on a study carried out by the research institute Panteia (2023), the total cost per tonkm for each freight transport mode was obtained, for the Netherlands. Following the method used to calculate the hub costs, the transportation costs were obtained for each country of the supply chain where a flow could originate. Table 12: Transportation costs (𝑡𝑐𝑖𝑡). Transportation Cost (c.u./tkm) Country/Transport Mode Road Rail Air Sea Austria 0.38 0.014 0.18 0.0032 Bangladesh 0.09 0.003 0.04 0.0008 Cambodia 0.13 0.005 0.06 0.0011 China 0.16 0.006 0.08 0.0014 Egypt 0.05 0.002 0.02 0.0004 India 0.09 0.003 0.04 0.0008 Indonesia 0.11 0.004 0.05 0.0010 Japan 0.45 0.016 0.21 0.0039 Malaysia 0.12 0.005 0.06 0.0011 Pakistan 0.10 0.004 0.05 0.0009 Philippines 0.12 0.004 0.06 0.0010 Singapore 0.23 0.009 0.11 0.0020 South Korea 0.22 0.008 0.10 0.0019 Sri Lanka 0.09 0.003 0.04 0.0008 Thailand 0.09 0.003 0.04 0.0008 Turkey 0.17 0.006 0.08 0.0015 Vietnam 0.12 0.004 0.06 0.0011 4 The price level index considers World = 100. R9: Vietnam (Ho Chi Minh) 94 804 R10: Vietnam (Ha Noi) 94 804 SUPPLEMENTARY MATERIAL 18 The number of workers necessary for each transport mode per ton-km (𝑤𝑡) are presented in Table 12. Table 13: Number of workers necessary per transport mode (𝑤𝑡). Road Rail Air Sea Workers/tkm 2.67 15.56 0.07 11.59 The quantity of workers considered represent the division between the total amount of ton-km of transported freight in 2020 in the United States (Bureau of Transportation Statistics, 2020) and the respective number of employments in each transportation sector (Bureau of Labor Statistics, 2023). Distances The distances between the entities, corresponding to parameter 𝑑𝑖𝑠𝑡𝑖𝑗, were calculated through the application of the Euclidean distance formula, making use of each entity’s coordinates. This approach assumes a straight-line distance between two points. Water-related parameters Three different indicators were employed to measure the water impact across the various stages of the supply chain: water stress (of a region), water footprint (of raw materials), water consumption (for fabric and final product’s manufacturing and transport activities). The water stress indicator corresponds to the environmental indicator 6.4.2 built and used by FAO of the United Nations. The percentual level of water stress measured by FAO (2022) for the countries present in this supply chain, i.e. parameter 𝑤𝑠𝑖, is summarized in Table 14. SUPPLEMENTARY MATERIAL 19 Table 14: Water stress level per country (𝑤𝑠𝑖). Country Water Stress (%) Austria 9.64 Bangladesh 5.72 Cambodia 1.04 China 41.52 Egypt 141.17 India 66.49 Indonesia 29.70 Japan 36.05 Malaysia 3.44 Pakistan 116.31 Philippines 26.25 South Korea 85.22 Sri Lanka 90.79 Thailand 23.01 Turkey 45.17 Vietnam 18.13 The water footprint for RM1 (cotton) and RM2 (hemp) per supplier is showcased in Table 15. This data corresponds to the parameter 𝑤𝑓𝑝𝑖. SUPPLEMENTARY MATERIAL 20 Table 15: Raw material’s water footprint (𝑤𝑓𝑝𝑖). Water Footprint (m3/ton) Supplier / Raw Material RM1 RM2 S1 4 267 724.5 S2 4 267 724.5 S3 2 398 2 183.1 S4 2 398 2 183.1 S5 4 457 - S6 9 724 754.6 S7 4 267 - S8 5 954 - S9 5 954 - S10 3 509 5 991.9 S11 4 267 - S12 4 267 - The study by Chapagain and Hoekstra (2005), in which the values considered for the water footprint of cotton were based on, distinguishes between the three types of water use, previously mentioned: blue water, for the withdrawal of water for irrigation or processing, green water, for the evaporation of infiltrated rainwater for cotton growth, and grey water for the volume of water necessary to dilute the pollution generated from the crop’s cultivation. Due to this blue, green and grey water measure’s specifications, it proved to be vital that the water footprint data was disaggregated on a per-country basis, since each country’s climate, soil and agricultural practices are important factors to consider. The cotton’s water footprint presented represents the sum of the blue, green and grey water required to produce, in each country, 1 ton of seed cotton. Note that the study used did not include every country, therefore, for the missing data, the global average value of blue, green and grey water had to be assumed (Chapagain and Hoekstra, 2005). As for the raw material hemp, less information was available regarding its water footprint. Nonetheless, with the intent of establishing a fair comparison between both raw materials, an effort was made to obtain values that considered the same water impact as the one considered for cotton’s water footprint. The blue, green and grey water values for hemp’s growing stage, per country, were calculated by Averink (2015), in a study conducted under the guidance of Prof. A.Y. Hoekstra. For the few supplier countries that lacked information on their blue, green and grey water impact, it was not possible to complete the gaps with the global average, as it was not available. For these cases, correspondent to S2 in Bangladesh and S6 in India, some research was conducted to obtain the predominant soil type of the countries. This parameter was considered a proxy between countries, and Bangladesh’s water footprint was assumed to be equal to Austria’s water footprint while India’s was assumed to be equal to Chile’s. According to Averink (2015), Austria presents a predominantly sandy loam soil, which is also very present in the region of Dhaka in Bangladesh (Islam et al., 2017), where S2 is situated. As for Chile, Averink assumes a predominantly clay loam soil which was found to be very similar to the soil of the land in Gujarat (Solanki et al., 2021), where S6 is located. The parameter soil was chosen as a proxy instead of climatic conditions because, according to SUPPLEMENTARY MATERIAL 21 Averink (2015) and Mekonnen & Hoekstra (2011) the blue water attributed to raw material hemp is zero, as the production and yield of hemp does not increase with irrigation. Lastly, the water consumption values used for the production stages are presented in Table 16. As it was stated before, it was assumed that the transformation process into fabric and final product is equal for both raw materials. For this reason, it was also assumed that the water requirements for fabric (𝑤𝑐𝑓) and final product (𝑤𝑐𝑓𝑝) production are the same, despite the type of product being fabricated. Table 16: Fabric (𝑤𝑐𝑓) and final product (𝑤𝑐𝑓𝑝) water consumption per ton. Fabric Final Product Water Consumption (m3/ton) 780.9 390.4 The water consumed in the production of fabric and final product was based on a LCA conducted by Levi Strauss & Co. (2015) on a pair of jeans Levi’s® 501®. According to this study, 68% of water consumption was related to the raw material’s fiber, 6% came from the fabric production and 3% derived from the garment assembly and finishing, as well as the sundries application and packaging. The last impact was attributed to consumer care, which is not within the scope of the present work, thus it was disregarded. The sum of these three contributions was equaled to the total water footprint of a finished textile product of cotton, including the blue, green and grey water (Chapagain & Hoekstra, 2005). The value indicated for the water consumption, in Table 17, for the fabric represents 6% of this calculated total water footprint and the final product represents 3%. As for the water consumption associated with the transports, the midpoint category Water Consumption of the method ReCiPe 2016 available in Simapro was used. The values considered are displayed in Table 17 and correspond to parameter 𝑤𝑐𝑡𝑡. The references used for each type of transport mode will be detailed ahead. Table 17: Transport mode’s water consumption (𝑤𝑐𝑡𝑡). Road Rail Air Sea Water Consumption (m3/tkm) 5.09x10-3 8.34x10-5 3.42x10-4 7.01x10-6 Environmental characterization The characterization of the environmental impact of each stage of the supply chain was done through Simapro Ecoinvent 3 database version 9.3.0.3. For the raw material’s crop cultivation and respective treatment stage, denoted as parameter 𝑒𝑖𝑝𝑐 𝑟𝑚, two different raw materials were identified. For cotton, the reference “Yarn, cotton {IN}| yarn production, cotton, ring spinning | Cut-off, U” was used, and the impacts are modelled according to Indian data. For hemp, due to a lack of information in the Simapro database regarding hemp fiber production, the reference “Yarn, jute {BD}| yarn production, jute | Cutoff, U” was used. According to La Rosa and Grammatikos (2019), jute and hemp are very similar plants, therefore one can expect their impacts to resemble each other. Once again, the reference relies on an Asian-based country’s data, Bangladesh. For the production activities in the supply chain, two references, regarding processes, were considered: “Bleaching and dyeing, yarn {IN}| bleaching and dyeing, yarn | Cut-off, U” and “Finishing, textile, woven cotton {GLO}| finishing, textile, woven cotton | Cut-off, U}. Given that SUPPLEMENTARY MATERIAL 22 it was assumed that the manufacturing of fabric and final product is the same, regardless of the type of final product being produced, it was reasonable to assume as well that the impacts associated with the production (𝑒𝑖𝑝𝑐 𝑓𝑎𝑏 and 𝑒𝑖𝑝𝑐 𝑓𝑝) are equal. An effort was made to select references that would not consider the same processes in their network, to avoid the double, and inaccurate, tally of an impact. Finally, the impacts related to the transportation operations of the supply chain (𝑒𝑖𝑡𝑐) were also considered through the following references: for air transport “Transport, freight, aircraft, medium haul {GLO}| transport, freight, aircraft, dedicated freight, medium haul | Cut-off, U”, for rail transport “Transport, freight train {IN}| transport, freight train, diesel | Cut-off, U”, for road transport “Transport, freight, light commercial vehicle {RoW}| processing | Cut-off, U” and for sea transport “Transport, freight, sea, container ship {GLO}| transport, freight, sea, container ship | Cut-off, U”. The environmental impacts are divided into eighteen midpoint categories, presented inTable 18 Table 18, alongside the code, that will be used to reference them, from this point onwards. For the normalization factors for each midpoint category (𝑛𝑐), Table 19 can be consulted. Note that ReCiPe 2016 also offers an indicator for water consumption. Nonetheless, this indicator does not provide the granularity that this study sought out for the supply chain activities under analysis. Table 18: Midpoint categories and codes. Midpoint Category Code Global warming GW Stratospheric ozone depletion SOD Ionizing radiation IR Ozone formation, Human health OFHH Fine particulate matter formation FPMF Ozone formation, Terrestrial ecosystems OFTE Terrestrial acidification TA Freshwater eutrophication FEU Marine eutrophication MEU Terrestrial ecotoxicity TE Freshwater ecotoxicity FEC Marine ecotoxicity MEC Human carcinogenic toxicity HCT Human non-carcinogenic toxicity HNCT Land use LU Mineral resource scarcity MRS Fossil resource scarcity FRS Water consumption WC SUPPLEMENTARY MATERIAL 23 Table 19 - Units and normalization factors of midpoint categories. Midpoint Category Unit Normalization Factor Global warming kg CO2 eq 0,000125 Stratospheric ozone depletion kg CFC11 eq 16,7 Ionizing radiation kBq Co-60 eq 0,00208 Ozone formation, Human health kg NOx eq 0,0486 Fine particulate matter formation kg PM2.5 eq 0,0391 Ozone formation, Terrestrial ecosystems kg NOx eq 0,0563 Terrestrial acidification kg SO2 eq 0,0244 Freshwater eutrophication kg P eq 1,54 Marine eutrophication kg N eq 0,217 Terrestrial ecotoxicity kg 1,4-DCB 6,58E-05 Freshwater ecotoxicity kg 1,4-DCB 0,0397 Marine ecotoxicity kg 1,4-DCB 0,023 Human carcinogenic toxicity kg 1,4-DCB 0,0971 Human non-carcinogenic toxicity kg 1,4-DCB 3,20E-05 Land use m2a crop eq 0,000162 Mineral resource scarcity kg Cu eq 8,33E-06 Fossil resource scarcity kg oil eq 0,00102 Water consumption m3 0,00375 Other parameters Additional economic parameters were considered: a tax rate of 30% and an interest rate of 10%. Funding: This work is financed by Portuguese funds through the FCT - Foundation for Science and Technology, I.P., under the project UIDB/00097/2020 (CEGIST). SUPPLEMENTARY MATERIAL 24 References Averink, J. (2015). GLOBAL WATER FOOTPRINT OF INDUSTRIAL HEMP TEXTILE. Bureau of Labor Statistics. (2023). Employment in Transport. Retrieved June 9, 2023, from https://explore.dot.gov/views/EmploymentinForHireTransportationEstablishmentsPrimarilyServingFreight/Table?%3Aembed=y Bureau of Transportation Statistics. (2020). U.S. Ton-Miles of Freight - Bureau of Transportation Statistics. Retrieved June 9, 2023, from https://www.bts.gov/content/uston-miles-freight Chapagain, A. K., & Hoekstra, A. Y. (2005). The Water Footprint of Cotton Consumption. https://www.researchgate.net/publication/228377230 FAO. (2021). FAO STAT. Retrieved April 3, 2023, from https://www.fao.org/faostat/en/#data/QCL FAO. (2022). AQUASTAT data. Retrieved May 3, 2023, from https://tableau.apps.fao.org/views/ReviewDashboardv1/country_dashboard?%3Aembed=y&%3AisGuestRedirectFromVizportal=y Fengler, W., Homi, K., & Caballero, J. (2022). Asia’s tipping point in the consumer class. Brookings. https://www.brookings.edu/articles/asias-tipping-point-in-the-consumerclass/ ILO. (2022). LABOUR. Retrieved April 20, 2023, from https://ilostat.ilo.org/topics/wages/# Islam, Md. A., Hasan, Md. A., & Farukh, M. A. (2017). Application of GIS in General Soil Mapping of Bangladesh. Journal of Geographic Information System, 09(05), 604–621. https://doi.org/10.4236/jgis.2017.95038 Kalkanci, M., & Özer, I. (2018). Developing a software calculating fabric consumption of various bathrobe models. Industria Textila, 69(5), 406–411. https://doi.org/10.35530/it.069.05.1550 Kharas, H. (2017). THE UNPRECEDENTED EXPANSION OF THE GLOBAL MIDDLE CLASS AN UPDATE. https://www.brookings.edu/about-us/annual-report/. La Rosa1, A. D., & Grammatikos, S. A. (2019). Comparative life cycle assessment of cotton and other natural fibers for textile applications. Fibers, 7(12). https://doi.org/10.3390/FIB7120101 Levi Strauss & Co. (2015). THE LIFE CYCLE Understanding the environmental impact of a pair of Levi’s ® 501 ® jeans. Levi Strauss & Co. (2022). Levi Annual Report. Levi Strauss & Co. (2023). New Levi’s® WellthreadTM x Outerknown Features Groundbreaking Cottonized Hemp - Levi Strauss & Co - Levi Strauss & Co. Retrieved March 30, 2023, from https://www.levistrauss.com/2019/03/11/new-levis-wellthread-xouterknown-featuresgroundbreaking-cottonized-hemp/ SUPPLEMENTARY MATERIAL 25 Mekonnen, M. M., & Hoekstra, A. Y. (2011). The green, blue and grey water footprint of crops and derived crop products. Hydrology and Earth System Sciences, 15(5), 1577–1600. https://doi.org/10.5194/hess-15-1577-2011 Mota, B., Gomes, M. I., Carvalho, A., & Barbosa-Povoa, A. P. (2018). Sustainable supply chains: An integrated modeling approach under uncertainty. Omega (United Kingdom), 77, 32–57. https://doi.org/10.1016/j.omega.2017.05.006 OECD/FAO. (2023). Cotton. OECD/FAO - Agricultural Outlook. Panteia. (2023). Cost Figures for Freight Transport-final report. Sarı, B., Zarifi, F., Alhasan, M., Güney, H., Türkeş, S., Sırlıbaş, S., Civan Yiğit, D., Kılınççeker, G., Şahin, B., & Keskinkan, O. (2023). Determining the Contributions in a Denim Fabric Production for Sustainable Development Goals: Life Cycle Assessment and Material Input Approaches. Sustainability, 15(6), 5315. https://doi.org/10.3390/su15065315 Solanki, C. H., Mistry, M. K., & Patel, M. S. (2021). Gujarat. In Geotechnical Characteristics of Soils and Rocks of India (pp. 231–249). CRC Press. https://doi.org/10.1201/9781003177159-12 Szabo, B. (2023). What’s In a NumberPicking the Perfect Denim Weight - SOSO Clothing. . Retrieved April 15, 2023, from https://sosoclothing.se/whats-in-a-number-picking-theperfect-denim-weight/ The World Bank. (2017). Data Bank - World Bank. https://databank.worldbank.org/source/icp2017 United Nations. (2022). COMMODITIES AT A GLANCE: Special issue on industrial hemp.