Sustainable Procurement
Site: | Saylor Academy |
Course: | BUS606: Operations and Supply Chain Management |
Book: | Sustainable Procurement |
Printed by: | Guest user |
Date: | Saturday, September 7, 2024, 7:23 PM |
Description
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.
Abstract
Postponement strategy is mainly used to handle the perceived variety and actual variety of product in the most cost effective way. However, postponement strategy has never been used by researchers in procurement to reduce cost and to enhance overall flexibility of the process. This paper gives a new concept of postponement, called procurement postponement, which is considered to be effective for the multi-product, assemble-to-order (ATO) system. A two-stage integrated approach of sustainable procurement and postponement method is proposed for the multi-product ATO production system to deal demand uncertainty, green house gas (GHG) emission, reliability of supply, level of disassembly and social issues of supplier selection with intuitionsitic fuzzy analytic hierarchy (IF-AHP) and multi-objective genetic algorithm (MOGA). Case study of an Indian company is discussed to use the proposed method by cost-emission-decision matrix.
Keywords:
Intuitionsitic fuzzy set; AHP; MOGA; Cost-emission-decision matrix; Postponement strategy
Source: Krishnendu Mukherjee, https://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-65132016000200249
This work is licensed under a Creative Commons Attribution 4.0 License.
Introduction
Traditional supply chain deals with man, money and
material (3M) and the green supply chain deals with man, money,
material and environment (3Me). Finally, sustainable supply chain deals
with man, money, material, environment and society (3MeS). Hence, the
sustainable procurement process (SPP) should include the coordination of
social, environmental and economic dimensions (the triple bottom line),
see e.g Matos & Hall. Sustainable procurement process (SPP)
should also include recycling of disposed product to improve
sustainability of supply chain.
Good
practices yield better result and inspire other. Unilever launched
"Unilever Sustainable Living Plan" to achieve hundred percent sourcing
of agricultural raw material sustainably by 2020. As part of its
Sustainable Living Plan, Unilever promotes the use of tomatoes raised
sustainably in Knor soups. In 2010, Tesco developed Tesco Knowledge Hub
to share knowledge with suppliers and agricultural producers to develop
sustainable supply chain to reduce the energy costs, waste and
environmental impacts of the products Tesco buys to achieve thirty
percent reduction of carbon emissions from the company's supply chain by
2020, compared to 2006-07 levels. In 2014, Ceres conducted the research
to measure to what extent more than 600 of the largest, publicly traded
U.S. companies are integrating sustainability into their business
systems and decision-making to develop the supplier self-assessment
questionnaire (SAQ) to form the foundation of sustainable of supply
chain. Today companies are keen to know the process of augmenting the
sustainable procurement. Thus, it is true that only companies that make
sustainability a goal will achieve competitive advantage.
In this paper an attempt has been made to integrate
the concept of sustainable procurement with an innovative postponement
strategy. The complexity of production process can be avoided by
manufacturing a base product or core product as per aggregate demand and
then auxiliary parts/components can be manufactured and assembled after
the receipt of customer demand. This approach can reduce cost, lead
time and also the complexity of logistics and inventory. Thus, 'procure
when required' or 'procurement postponement' could be an effective
strategy of any manufacturing company that assembles products. In this
paper an integrated approach of procurement postponement and sustainable
procurement process is discussed in light of a case study of an Indian
company. This paper is organized as follows. Section 2 discusses about
literature review. Section 3 describes methodology. Section 4 depicts
the mathematical model for two-stage supplier selection for ATO. Section
5 provides the case study of an Indian company. Section 6 and 7
discusses about results and conclusions.
Literature review
Sustainability can be achieved by changing the
objectives from the economy driven towards economy, environment and
society driven. This triple bottom line
approach is also the cornerstone of sustainable procurement process.
Material intensity, energy intensity, water consumption, toxic emissions
and pollutant emissions are five basic sustainability indicators. The sustainable procurement process basically
improves above five metrics. Literature on the sustainable procurement
process is very limited. Only few journal articles have been found which
considered environmental and social aspects – separately or together. With this limited journal articles an attempt
has been made to answer few interesting questions: Is supplier
selection, one of the key elements essential to supply chain success?
What are the key dimensions to measure supplier performance? How to
improve sustainable procurement process with postponement strategy?
Suppliers
have been acknowledged as the best intangible assets of any business
organization. Tracey & Tan
mentioned that one of the key elements essential to supply chain success
is an effective purchasing function. H Shin et al. proposed that
the buyer performance can be enhanced with the improvement of supplier
performance. Smith et al. considered that the good relationship
between buyer and supplier is based upon exchanging information and
sharing benefits with each other. However, Spekman showed that
communication between supplier and buyer is always limited as buyer
always focuses on the price of products and ignores the importance of
preserving the long term relationship with suppliers. Van Hoek proposed three approaches for green supply chain management: (1)
reactive approach, (2) proactive approach, and (3) value-seeking
approach. Noci divided corporative green strategies into (1)
reactive strategy, and (2) proactive strategy and integrated these green
issues to supplier selection. Hsu & Hu measured supplier
performance with five dimensions: (1) Procurement management, (2)
R&D management, (3) process management, (4) incoming quality
control, (5) management of the system. Noci suggested that a firm
practicing green supply chain should evaluate suppliers with four
factors: (1) green competencies, (2) green images, (3) current
environmental efficiency, and (4) net life cycle cost. Handfield et al. proposed an environmental conscious purchasing decision to
trade-off between environmental dimensions using the analytic hierarchy
process (AHP). Wu et al. proposed the integrated approach of AHP
and fuzzy logic to select green suppliers. Lee et al. used both
environmental and non-environmental criteria, namely, quality,
technology capability, pollution control, environmental management, the
green product, and green competencies, for selecting green supplier in
high-tech industry. Bai & Sarkis integrated economic,
environmental, and social issues into the supplier selection model.
Particularly they emphasized more the social dimension in their model.
On the other hand, postponement is a concept which brings the efficiency
of the lean concept and the responsiveness of the agile concept
together.The major reason of using postponement
strategy is that one extremely high demand may be offset by another
extremely low demand after aggregation. Apart from
reducing demand variability, postponement is also used to tackle process
and supply uncertainties. There are four forms of postponement
strategies, namely pull postponement, logistics postponement, form
postponement and price postponement. The former three strategies are also referred to as production
postponement. Bowersox & Closs
stated three different postponement strategies- time postponement,
place postponement and form postponement. A detail classification of
postponement strategy is given in Table 1.
Table 1 Classification of postponement strategy.
Sl.No. | Type | Definition | Application |
---|---|---|---|
1 | Pull postponement | It is also known as process postponement ,which refers to moving the decoupling point earlier in the supply chain such that fewer steps will be performed under forecast results. | 1. ABC Bicycle Company, India. 2. Benetton, an apparel manufacturer, postpones its colour dyeing process until orders are received. |
2 | Logistics postponement | Logistics postponement involves the re-designing of some of the processes in the supply chain so that some customization can be performed downstream closer to customers. Packaging postponement and labelling postponement or branding postponement can be subsets of logistics postponement when the packaging, labelling or branding processes are moved closer to customers. | 1.
Hewlett-Packard produces generic printers at its factory and
distributes them to the local distribution centres, where power plugs
with appropriate voltage and user manuals in the right language are
packed. Since generic printers are lighter, more units could be shipped. 2. All products in IKEA retail stores are kept in semi-finished forms (flat packs) and are assembled after home delivery by customers or deliverymen. In this way, truckload capacities can be utilized and configurations can easily be made at customer locations. |
3 | Form postponement | Form postponement, also called product postponement,
gives fundamental change of the product structure by using standardized
components and processes to achieve high customization. |
1. Brown et al. applied the form postponement in a semiconductor company (Xilinx), where it re-designs the IC so that it could be re-configured by software easily and quickly for customized features and functions. |
4 | Price postponement | Van Mieghem & Dada defined price postponement from economic and marketing perspectives. They described the price postponement as a strategy aimed at deferring the pricing decision until customer demand is known. Selling price is negotiated with customers after they place their orders. | 1. In July 2002, Bank of China (BOC) in Hong Kong applied a price postponement strategy for its initial public offering. |
5 | Time postponement | Customer order triggers the forward movement of goods. | 1.Dell usually assembles computer after the orders are placed by customers |
6 | Place postponement | Keep the goods in the central location of the supply chain until the customer orders are arrived. | Hindustan Unilever Limited, India's largest consumer product company, addresses three major market segments: modern trade segment (organized retail sector), general trade segment and rural markets. The modern trade segments, consists of large urban market, are usually serviced by a HUL warehouse that supplies to the cluster of retail locations. |
Methodology
The proposed method is suitable for the assembled product where product variety is achieved by adding auxiliary parts/components to the main product or base product or core product as per demand of customer. To tackle uncertainty of demand a two-stage method is considered. The base product is assumed to be manufactured by deterministic demand and auxiliary parts/component is manufactured as per stochastic demand. Stage 1 is required to select suppliers for base product and stage 2 is required to select suppliers for auxiliary parts/components. In both stages analytic hierarchy process (AHP) is used with intuitionistic fuzzy set (IFS) to select and evaluate suppliers and multi-objective genetic algorithm (MOGA) is used to allocate order to selected suppliers for deterministic and/ stochastic demand. AHP is one of the most cited multi-criteria decision analysis (MCDA) tool and the fuzzy version of integrated AHP is mostly used for supplier selection to deal with uncertainties of the supplier selection process. Intuitionistic Fuzzy Set (IFS) is a generalized fuzzy set and is more suitable in selecting suppliers as it includes the degree of hesitation to measure uncertainty associated with each decision. Further, Shannon's Entropy is included in the proposed method to measure the discord or conflict in selecting suppliers.
Intuitionistic fuzzy set (IFS)
In 1986,
Atanassov proposed a generalized concept of fuzzy set popularly known as
the intuitionistic fuzzy set (IFS). If X be a universe of discourse,
then IFS A can be defined as where
denote membership and non-membership functions of A and satisfy
. For every IFS A in X, the degree of hesitation can be defined as
which
express whether x belongs to A or not. If
then the normalized hamming distance between A
and B can be represented as
To rank three IFS, their normalized hamming distance from the ideal
solution M (1,0,0) should be calculated. Lowest distance from M will
give better solution.
Shannon's entropy
Shannon's entropy
is a classical measure of discord in probability theory. Let a probability defined on X. Then Shannon's entropy is defined
as
In AHP, priority pi is the probability that ith criterion is preferred by decision maker.
IF-AHP algorithm
- 1 Prepare intuitionistic fuzzy pairwise comparison matrix for each criterion and alternative.
- 2 Calculate the score (Si) of all intuitionsitic fuzzy number with any of the given formula.
- 3 Calculate the normalized score matrix with the given formula
(6) - 4 Normalize each row of
with the given formula
(7) - 5 Calculate entropy w.r.t ith attribute with the given formula
(8) - 6 Calculate entropy weight wi with the given formula
(9) - 7 Calculate normalized entropy weight to rank criteria or alternative with the given formula
(10)
Sourcing strategy for sustainable ATO: a mathematical approach
Due to price competition, many companies are manufacturing in low-cost countries and selecting such a location by considering manufacturing costs, corporate tax rate, export incentives, the presence of key suppliers or duty-free imports, infrastructure, the political situation and skilled labour. In ATO base product are expected to be manufactured at low-cost countries/locations and final configurations as well packaging are expected to be made at the distribution point near to the customer.
Stage-1: selection of supplier for the base product
In this proposed model, shown in Figure 1, we assumed that the base product is manufactured at the manufacturing site and stored at the centralized warehouse in generic form. Once order is arrived, base product is shipped with the bill of material (BOM), shown in Figure 2, to retailer's site where auxiliary part/module is prepared and assembled. In closed loop supply chain, shown in Figure 1, used products are collected from the collection site and sent to the disassemble site where products are disassembled completely or partly. After processing, disassembled parts/sub-assembly is sent to the manufacturer site where they are reused as new parts/sub-assembly. Disassembly cost varies with level of disassembly. Hence, up to a certain level product should be disassembled.
Figure 1 The proposed 2-stage supply chain model for sustainable ATO.
Figure 2 The bill of material. Source: Elaborated by the author.
![](https://learn.saylor.org/pluginfile.php/5619131/mod_book/chapter/49981/f2.jpg)
In
the proposed AHP model, shown in Figure 3, flexibility encompasses
volume flexibility, routing flexibility, material handling flexibility,
machine flexibility, operation flexibility, expansion flexibility,
process flexibility. Proposed AHP model encompasses three main criteria
to select suppliers for the base product through the sustainable
procurement process.
Figure 3 The AHP model of supplier selection for the base product.
Deterministic order allocation model for the base product
The following assumptions are considered to prepare objective functions for supplier selection.
Assumptions
- Multiple items are purchased from selected suppliers.
- Quantity discounts are not taken into consideration.
- No shortage of item is allowed for any supplier.
- Demand of base product for the planned horizon is constant and known with certainty.
= the purchase cost of product j from ith supplier.
= the transportation cost of product j from ith supplier.
= the overall performance index of ith supplier.
= the ordering cost of jth product from ith supplier.
= the reliability of ith supplier.
= the order quantity of product j to ith supplier.
= the percentage late delivery of product j from ith supplier.
= the capacity of ith supplier for jth product.
= the demand for jth product
= the handling cost per ton of product j.
=Total allocated budget for all products.
= percentage of jth product disposed at disposal site
= Level of disassembly of jth product at disassembly site
= GHG emission factor per weight unit distance due to the use of transportation mode.
= the distance of ith supplier from the manufacturing/retailing site
- alpha = probability value of chance constraint
= 1,2,3……… n of suppliers
=1,2,3……. m no of products
The total cost of purchase (TCP) consists of purchase, transportation, order/setup, and holding cost.
Min TCP:
(11)
In
the second objective function total value of reliable purchase (TVRP)
is considered instead of total value of purchase (TVP) proposed by
Ghodsypour & O'Brien (1998). The reliability of supply, , of each supplier is obtained from supplier's reliability measurement data sheet, Table 2, to form TVRP equation.
Table 2 Supplier data sheet.
Name | Cost (INR) | Ordering Cost (INR/tonne) |
Capacity (Tonne) |
% late delivery | Distance (Km) |
Mode of Transport | |||
---|---|---|---|---|---|---|---|---|---|
Prod A | Prod B | Prod A |
Prod B |
Prod A |
Prod B |
||||
Supp. 1 | 100 | 150 | 2000 | 3000 | 1000 | 2000 | 0.2 | 190 | 1. By HGV 2. 100 Km by rail and 90 Km by HGV |
Supp. 2 | 102 | 149 | 2000 | 3000 | 1000 | 1500 | 0.15 | 200 | 1. By Large container |
Supp. 3 | 101 | 150 | 2000 | 3000 | 1000 | 1500 | 0.2 | 180 | 1. By HGV |
Supp. 4 | 100 | 151 | 2000 | 3000 | 1000 | 1500 | 0.15 | 200 | 1.160 Km by rail and 40 Km by HGV. 2. By HGV |
Supp. 5 | 103 | 152 | 2000 | 3000 | 1000 | 1500 | 0.1 | 240 | 1.200 Km by rail and 40 Km by HGV. |
Supp. 6 | 102 | 150 | 2000 | 3000 | 1000 | 2000 | 0.2 | 240 | 1. By HGV 2.200 Km by rail and 40 Km by HGV. |
Subject to
Stage-II: supplier selection for auxiliary parts
![](https://learn.saylor.org/pluginfile.php/5619131/mod_book/chapter/49981/f4.jpg)
Mathematical model for stochastic demand
Subject to
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 |
![](https://learn.saylor.org/pluginfile.php/5619131/mod_book/chapter/49982/f5.jpg)
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 |
Results
Intel core i5 PC with 3 GB RAM is used to solve the model. Microsoft Excel is used for IF-AHP and Matlab R 2009a is used for MOGA. Uncertainty in demand at retailer's site changes the amount of order in considerable amount for supplier 3, shown in Figure 6. For different values of alpha, highest order is allocated to supplier 3 as supplier 3 has highest reliability and IFS priority value.
Figure 6 Order allocation at different value of alpha. Source: Elaborated by the author.
Conclusion
In this paper, a new concept and an innovative
method is proposed to initiate sustainable procurement process (SPP) by
integrating the concept of sustainable supply chain and postponement
strategy. The proposed method has several advantages, including but not
limited to:
- 1 The proposed model is very simple and easy to apply.
- 2 It can be used for single item as well as multiple item of the assembly-to-order production system for stochastic and/ deterministic demand.
- 3 It considers reliability of supply to reduce inbound risk.
- 4 The proposed model also gives better supply chain visibility as it
integrates far downstream customers with manufacturer through active
co-operation of retailers. However, manufacturer should take appropriate
strategy to share limited information with its retailers to maintain
better security.
In addition to the above this innovative method
can reduce logistic lead time, chances of over stock or under stock and
the complexity of inventory. This proposed method is suitable for
manufacturing industries which particularly assembled different
components to develop complex products.