Sustainable Procurement
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] |
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 |
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.
Mode | Road LGV | HGV | Rail | Small tanker | Large container |
---|---|---|---|---|---|
g/tonne-km | 400.1 | 118.6 | 28.3 | 20 | 13 |
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 |
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 |
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] |
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 |
Name | Cost (INR) | Ordering Cost (INR/piece) |
Capacity (piece) |
% late delivery | Reliability | IFS priority value | |||
---|---|---|---|---|---|---|---|---|---|
Prod A | Prod B | Prod A |
Prod B |
Prod A |
Prod B |
||||
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 |
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 |