Sustainable Procurement

Read this article, which highlights a novel strategy for procurement. Focus on sections 1, 2, and 5 and the opening paragraphs for sections 3 and 4. The model in the paper presents a new strategy to reduce procurement costs and enhance overall procurement flexibility.

Case study

An Indian Company is manufacturing/assembling product A and B as per the bill of material shown in Figure 2. The company is 40 km away from the railway station and well connected with other cities by road. Considering fluctuation of market demand of product A and B, company is seeking effective procurement strategy for their ATO production system. The company has assembling unit in Punjab and retailers in different parts of India. Base product is manufactured as per the forecast and stored at the central warehouse shown in Figure 1. After receiving the customer order, the base product is brought to retailer shops in 15 to 20 days. Auxiliary parts/components/sub-assemblies are manufactured or assembled at the retailer site. It is assumed that material handling cost is 10% of procurement cost from each supplier. The aggregate demand for raw material to produce base product in the planning horizon is 4,900 tones. Senior members of different departments such as Finance, Marketing, Design and Manufacturing are asked to form a team of decision makers to select the right supplier for the company. Initially, a supply base is formed based on their industrial certifications such as ISO, TUV etc, material test data and ability to supply within the lead time. Based on the above information supplier's data sheet, is prepared, and shown in Table 2. Distance and mode of transfer mentioned in Table 2 is used further to calculate cost of emission for inbound transport.

Linguistic terms, shown in Table 3, are used to prepare supplier's reliability measurement data sheet, shown in Table 4. Arithmetic mean of each IFN, shown in Table 4, is used to calculate reliability of each supplier, shown in Figure 5.

Table 3 Linguistic terms.

Linguistics terms IFNs Linguistics terms IFNs Linguistics terms IFNs
Excellent [1.00;0.00;0.00] Good [0.7;0.2;0.1] Bad [0.4;0.5;0.1]
Very good [0.85;0.05;0.1] Moderate [0.5;0.5;0.00] Very bad [0.25;0.6;0.15]
Extremely bad [0.0;0.9;0.1]

Table 4 Supplier's Reliability measurement data sheet.

Name Technical Qualification of workers Supplier's Quality System Past supply of similar raw material
Supplier1 Good Good Good
Supplier2 Moderate Good Good
Supplier3 Bad Moderate Bad
Supplier4 Moderate Good Good
Supplier5 Excellent Excellent Excellent
Supplier6 Good Excellent Very good

Figure 5 IFN values for supplier's reliability factors. Source: Elaborated by the author.

The normal hamming distance of each supplier is measured from ideal IFN (1.0;0.0;0.0). Complement of the normal hamming distance is considered as the overall reliability of each supplier. Cost-Emission-Decision Matrix is prepared with Table 5 and Table 6, shown in Table 7. Cost coefficient and emission coefficient from Cost-Emission-Decision Matrix is used to prepare transportation cost and GHG emission objective function, mentioned in Equation 11 and Equation 14, respectively. Local supplier performance matrix is prepared further with Table 3 and Table 8, shown in Table 9. Supplier's data sheet for auxiliary product and demand of products at retailer's site, shown in Table 10 and Table 11, are prepared to select and to distribute order to the selected suppliers at retailer's site as per stochastic demand.

Table 5 Freight transmission emission factor.

Mode Road LGV HGV Rail Small tanker Large container
g/tonne-km 400.1 118.6 28.3 20 13

Table 6 Inbound logistics cost.

Mode Transportation Charges (INR/tone-Km) Mode Distance (Km) Approx. Transportation Charges (INR/tonne-Km)
From To
By road 200 By rail
Train load LR4
Wagon load 120
1 100 120
101 125 142
126 150 165
151 175 185
176 200 207

Table 7 Cost-Emission-Decision Matrix per unit tonne of transport.

Name Mode of transport Cost (INR) Emission (g) Decision
Supplier 1 Option1: By HGV 38000 22534 Option 2 is preferred for low cost and low emission.
Option 2: 100 Km by rail and 90 Km by HGV 30000 13504
Supplier 2 Option 1: By Large container 40000 2600 Option 1 is selected.
Option 2: None --------- ---------
Supplier 3 Option 1: By HGV 36000 21348 Option 1 is selected.
Option 2: None ---------- ---------
Supplier 4 Option 1: 160 Km by rail and 40 Km by HGV. 37600 9272 Option 1 is preferred for low cost and low emission
Option 2: By HGV 40000 23720
Supplier 5 Option 1: 200 Km by rail and 40 Km by HGV. 49400 10404 Option 1 is selected.
Option2: None ------- ---------
Supplier 6 Option 1:By HGV 48000 23720 Option 2 gives 1.03 times higher cost and 2.28 times lower emission than option 1. Assuming equal priority to cost and emission. Hence, option 2 is preferred.
Option 2: 200 Km by rail and 40 Km by HGV. 49400 10400

Table 8 Linguistic terms.

Linguistic Terms IFNs
Very Costly [1.00;0.00;0.00]
Costly [0.75;0.15;0.1]
Cheap [0.6;0.3;0.1]
Very Cheap [0.5;0.3;0.2]

Table 9 Local Supplier Performance Matrix.

Name Price Quality Service Flexibility Technical Capability
Supplier 1 Very Costly Very good Good Very Good Excellent
Supplier 2 Cheap Good Moderate Good Moderate
Supplier 3 Cheap Good Moderate Moderate Moderate

Table 10 Supplier's data sheet for auxiliary product.

Name Cost (INR) Ordering Cost
% late delivery Reliability IFS priority value
Prod A Prod B Prod
Supplier 1 200 350 200 300 1000 2000 0.2 0.65 0.091825
Supplier 2 202 349 200 300 1000 1500 0.15 0.68 0.232825
Supplier 3 201 350 200 300 1000 1500 0.2 0.69 0.6751

Table 11 Demand of products at retailer's site.

Product Type µJ (unit) σJ (unit) α Φ–1(α)
Product A 2500 1000 0.85 1.0364
Product B 2400 1200 0.85 1.034