A Fuzzy Model for Vendor Rating with Risk Assessment

Transcription

1 A Fuzzy Model for Vendor Rating with Risk Assessment Nicola Costantino Ingegneria Meccanica Gestionale Mariagrazia Dotoli Elettrotecnica ed Elettronica Marco Falagario Ingegneria Meccanica Gestionale Maria Pia Fanti Elettrotecnica ed Elettronica Abstract The paper addresses a crucial objective of the strategic purchasing function in supply chains, namely vendor rating. In particular, a novel fuzzy model for vendor rating is proposed. The technique ranks the suppliers consulted by a buyer in a single item exchange taking into account both the obvious price factor and the important risk issue. Based on the presented fuzzy vendor rating model, an effective and yet simple to implement algorithm is proposed and is applied to a real case study. Keywords: Supply chains, risk management, vendor rating, risk index, fuzzy logic. Introduction A Supply Chain (SC) is a network of business entities collectively responsible for procurement, manufacturing and distribution activities associated with product families. The SC network configuration is essential to pursue a competitive advantage and to meet the market demand. Indeed, the performance of any entity in a SC depends on the performance of the other SC partners, and on their willingness and ability to coordinate activities within the SC [6]. Because of the inherent complexity of decision making in SCs, high performance SC networks require decision support in many areas, e.g. including information gathering, locating potential suppliers and buyers, and deciding what to buy and what to sell [3]. Among the major decision areas of SC management, we may single out purchasing, procurement, manufacturing and distribution and logistics. In particular, in the new global business environment purchasing is becoming one of the most significant and strategic decision areas of the physical SC, because external suppliers now exert a major influence on a company s success or failure [8]. Traditionally, when buying materials from suppliers, companies focused on short-term transactional purchases, primarily on the basis of cost considerations. However, incorrect decisions about supplier selections may lead to serious profit losses [9]. As a result, in the last decade many corporations have changed their focus from the classical purchasing concept to an effective tactical management of the procurement task, including identification of supplier selection criteria, supplier selection decisions, and monitoring of supplier performance [8]. In this direction, a recent survey indicates that companies are showing a growing interest in vendor selection using decision support systems [9]. Typically, purchasing teams use a technique known as vendor rating or vendor assessment to select suppliers from whom to make purchases [8]. This method assesses candidate suppliers based on a selected number of criteria, essentially taking into account as a major factor for the supplier choice the offered product price [4, 5]. The different vendor rating approaches available in the literature mainly try to increase the SC competitive advantage by a more efficient purchase management, that is based on including other factors to price in the transaction evaluation, e.g. quality, quantity, delivery etc. Consequently, a serious limitation of such approaches is that in the buyer/supplier relationship they disregard the assessment of risk, which has been demonstrated to significantly influence a transaction [7, 9]. This paper focuses on purchasing and partner selection with the objective of incorporating an important but qualitative factor such as risk into the vendor selection problem. Indeed, risk is a crucial component in a purchasing choice: suppliers may fail to keep delivery promises, and the underlying causes range from bad planning to extended delivery chains [0]. On the other hand, companies tend to reduce the number of suppliers, to save time and money, especially in markets where only few suppliers can be found [7], but this process makes the SC more vulnerable to an interruption of supplies. With single sourcing the firm no longer faces competitive pricing, so there can also be a risk for higher prices. Just in time activities may also increase risks when they are taken to their extreme. The complexity of the problem forces researchers to find decision making solutions in order to help buyers in choosing partners while taking into account both quantitative (e.g. price) and qualitative (e.g. risk) decision making variables. Thanks to its ability of incorporating qualitative knowledge with quantitative information such as real data, fuzzy reasoning is an ideal candidate to achieve improved vendor rating approaches. Albino et al. [] propose fuzzy

2 logic as a tool to enhance the classical vendor rating technique. In this direction, this paper proposes a fuzzy logic based model for suppliers ranking and selection. The originality of the presented fuzzy vendor rating model lies in the fact that it both takes into account the knowledge of the product price as well as the suppliers risk evaluation. For the sake of simplicity and with the perspective of extending the proposed approach, here we choose to address a multiple source single-item vendor rating problem considering only two dimensions: price (i.e. the offered bid) and risk. The paper is organized as follows. Section 2 briefly discusses the different vendor rating approaches proposed in the literature and argues on the importance of risk assessment in suppliers selection, identifying several main factors of risk. Moreover, Section 3 proposes the fuzzy model for vendor rating, incorporating knowledge on price and risk. Subsequently, Section 4 applies the presented approach to a case study. The paper is completed by a conclusion section and a references section. 2 Vendor rating and risk assessment The vendor rating process is a critical step in the choice of the supplier, which is in turn perhaps the most important responsibility of the purchasing function. Vendor rating systems identify top suppliers, i.e. the candidate partners that are best equipped to meet the customer s expected level of performance, and check them periodically [2]. Hence, vendor selection is a multiobjective decision problem, including many conflicting objectives such as, besides the obvious goal of (low) price, quality, quantity, delivery, performance, capacity, communication, service, geographical location etc. [5]. Generally, a vendor selection problem is defined by a set of bidding suppliers S={s,,s n } and a set of conflicting criteria C={c,,c m }, according to which the consulted vendors have to be ranked. In particular, the i-th supplier is evaluated according to each criterion by way of a performance index v ij, with i=,..,n and j=,..,m. The well-known Vendor Performance Rating (VPR) index integrates linearly the above said criteria as follows [4]: VPR = m i w j v ij, for i=,,n, () j= where w j, with j=,,m and m wj =, represents the j= weight of the generic criterion, i.e. its relative importance in the evaluation. Usually, in () price is the first and most important ranking criterion, i.e. the one with the highest weight. In addition, the supplementary objectives considered in the performance rating model () further characterize the transaction. These additional criteria may range from quality to quantity, to delivery etc., and numerous different approaches are proposed in the literature, defining as many techniques [5]. Moreover, models have evolved from the classical linear weighting model () to more sophisticated formalisms, employing in turn mathematical programming formulations, multi-objective optimization methods or soft computing []. However, a serious limitation of the existing suppliers ranking approaches is that they disregard risk evaluation in the buyer/supplier relationship, which is significantly influenced by risk [7, 9]. In the sequel we briefly identify the risk sources in a generic exchange, with the objective of incorporating risk assessment in vendor selection. Risk associated to procurement is a severe threat to supply assurance, and incorrect decisions about supplier selections may lead to serious profit losses, e.g. see [9] for a report on famous instances of product delivery failures in market-leading SC networks. Besides delivery failures and the deriving profit losses, improper supplier selection may also cause problems such as unfulfilled environmental constraints, amplified company s liability and increased product lead times []. Treleven and Schweikhart [2] identify several supply risk categories, which are related to price, disruption, inventories and schedule, technology and quality. Hallikas [7] adds to these categories the risk of supplier opportunism and the availability risk. In order to build a vendor rating model providing a ranking of the consulted suppliers while assessing the risk of improper vendor selection, we first focus on the risks strictly connected to the buyer/supplier relationship. Hence, among the above categories we select the following ones, that are detailed in the sequel [7, 2]:. price risk; 2. quality risk; 3. opportunism risk. Price risk. This kind of risk is most common in case of single sourcing: it refers to the risk that a supplier escalates its prices once it becomes the buyer s only source of a particular product or service. Obviously, this risk may be directly coped with by adopting multiple sourcing: in fact, under this circumstance the competition among different suppliers leads to low prices. Quality risk. Companies usually recognize this area as responsible for the major risk component. Indeed, a large amount of uncertainty is related to the product quality, which in turn may sometimes require high costs, so that it is crucial for the buyer to reduce such a risk factor [7]. Obviously, the supplier experience in the product field, e.g. its compliance to quality audit processes, and a large magnitude of the company and of its market shares, are factors that greatly reduce this risk.

3 Opportunism risk. All transactions carry the risk of supplier s opportunism, i.e. seeking self-interest with guile. Partnership as a part of proactive procurement is a good risk management policy [7]. Hence, carrying out an exchange with well-known and trusted suppliers reduces this risk. Besides the above risk categories, that are directly connected to the suppliers, several additional risk classes may be identified with respect to the product required in the exchange, as detailed in the sequel [7, 2]: 4. asset specificity risk; 5. disruption risk; 6. availability risk. Asset specificity risk. This kind of risk is related to the product customization. Clearly, the higher the specificity of the required goods, the higher the so called hold-up risk, i.e. the danger that the buyer becomes dependant from a particular supplier. Disruption risk. In case of a supply failure, the different causes, e.g. strike, fire, natural disaster etc., lead to diverse concerns, depending on the expected impact on the organization. Indeed, different levels of strategic importance of the failed product in the buyer productive cycle lead to different consequences for the purchaser. Availability risk. This risk category is related to the availability on the market of the specific products or materials required by the transaction. Obviously, the specificity of the product is one of the dominant factors which influence the availability uncertainty. 3 The proposed approach 3. The vendor rating model Considering the risks connected to the suppliers selection, we investigate a novel approach to help the buyer in determining a ranking of the consulted suppliers, in the simple case of single item with multiple sourcing. In particular, as previously mentioned, price and risk are selected as the main criteria for vendor assessment. Let us suppose that the decision maker has to select the top supplier in the set of bidding vendors S={s,,s n }, where s i with i=,,n denotes the generic supplier. This vendor is chosen as the one exhibiting the lowest so-called Vendor Performance Rating (VPR), which is an overall indicator both of price and risk and has consequently to be minimized. To this aim, each supplier is associated with an index VPR i modeling the corresponding supplier score. More precisely, for each supplier s i with i=,,n the VPR i value is calculated as follows according to (): VPRi = wp i + w2rii, for i=,,n, (2) where: P i is the normalized (in the 0 range) price offered by the i-th supplier; RI i is the normalized (in the 0 scale) risk index for the i-th vendor; w is the weight of the price criterion in the 0 range; w 2 is the importance of the risk objective in the 0 scale. In particular, while each supplier may be deterministically evaluated in relation to the price criterion via the presented bid, risk is subjective and has therefore to be estimated. More specifically, the normalized price P i may be easily obtained by dividing the i-th supplier bid p i by the highest price among those offered by all the consulted suppliers, as follows: P p max,..., = i i ( p p ) n. for i=,,n. (3) In addition, we propose to evaluate variable RI i associated to supplier s i with i=,,n by means of the following data, which may in turn be determined by expert judgments on the vendor: (i) the Supplier Experience in the field (SE i ), i.e. the degree of know-how and practice of the i-th vendor in the market area of the required product, expressed in the 0 range. Obviously, a value close to 0 implies that the supplier s skills are limited, while a value close to is related to an experienced vendor, and all the other values correspond to vendors in between. Hence, this parameter is related to the quality risk; (ii) the Supplier Dimension (SD i ), i.e. the magnitude of the i-th supplier s firm expressed in the 0 range. Obviously, a value close to 0 is typical of a small enterprise, while a value close to implies that the supplier s company has a significant market share. Hence, this parameter complements the previous one in evaluating the quality risk; (iii) the Supplier Partnership Level (SPL i ), i.e. the degree of affiliation expressed in the 0 scale existing between the buyer and the i-th consulted supplier. Clearly, a value close to 0 indicates that few contracts previous to the current exchange exist, while a value close to implies that the supplier is well known to the buyer. Hence, this parameter refers to the opportunism risk. Similarly, the price and risk importance weights w and w 2 are determined by means of several parameters, characterizing the product required by the exchange and estimated by the buyer on the basis of expert advice: (iv) the product Standardization Level (SL), i.e. the 0 degree of customization. In particular, 0 corresponds to totally customized products, to completely standardized goods. Hence, this parameter is related to the asset specificity risk;

4 (v) the Strategic Importance (SI) of the product in the buyer s productive cycle, i.e. the 0 degree of significance of the supply for the specific firm. Obviously, a value close to 0 is typical of a minor supply, while a value close to indicates a key product. Hence, this parameter refers to the disruption risk; (vi) the product Availability (A), i.e. the ease of access to the product on the market, expressed in the 0 scale. Naturally, a value close to 0 indicates that the required supply is hardly available, while a value close to implies that the product is easy to find on the market. Hence, this parameter models the availability risk. Membership Value L SE, SD, SPL, SL, SI, A Figure : The membership functions for the input variables of FIS and FIS2. H More precisely, after the evaluation via expert judgments for each supplier and for each product of the corresponding parameters (i)-(iii) and (iv)-(vi), the risk index RI i and the weights w and w 2 are respectively evaluated by means of two Fuzzy logic Inference Systems (FIS), that are designed in the following sub-section. Membership Value L M H We remark that in any multiple sourcing exchange the set of data (i)-(iii) has to be evaluated for each different supplier. On the contrary, in a single-item multiple sourcing exchange the above parameters (iv)-(vi) have to be determined only once for a given set of candidate suppliers. However, exchanges of the same product and with the same candidate suppliers taking place at different times may lead to different values of such parameters. Once the supplier score is calculated for each supplier according to (2), a ranking can be obtained and the best supplier for the exchange may be chosen. Obviously, such a supplier is the one exhibiting the following vendor performance rating: VPR = min ( VPR,..., VPR n ). (4) 3.2 The fuzzy logic inference technique We employ fuzzy logic based reasoning to assess the risk index as well as to evaluate the performance indices weights in the vendor rating model (2). Indeed, fuzzy logic provides a natural framework to incorporate qualitative knowledge, such as risk, with quantitative information, such as price. Therefore, fuzzy reasoning is particularly suitable for determining, on the basis of the subjective and qualitative knowledge provided by experts and evaluating parameters (i)-(vi), the subjective risk index parameter necessary as an input to the vendor rating model (2), as well as the subjective weights related to the product required in the exchange. To this aim, a fuzzy system is designed, composed of two different FIS, indicated in the sequel by FIS and FIS RI, w, w 2 Figure 2: The membership functions for the output variables of FIS and FIS2. Inputs Output SE SD SPL RI L L L H H L L M L L H M H L H L L H L H H H L M L H H M H H H L Table : The rule table for FIS. Inputs Outputs SL SI A w w 2 L L L M M L L H H L L H L L H L H H M M H L L M M H L H H L H H L L H H H H M M Table 2: The rule table for FIS2. More precisely, component FIS addresses the problem of determining the risk index variable, RI i, expressed in the 0 range and connected to each supplier s i with i=,..n. In particular, for the i-th supplier the output

5 variable RI i of FIS is evaluated on the basis of the input variables supplier dimensions (SD i ), partnership level (PL i ) and experience (SE i ). Hence, FIS employs as input variables parameters (i)-(iii) that depend on the i-th considered supplier. For the sake of simplicity, in the sequel we omit this dependence. All the same, note that FIS is invoked in the vendor rating procedure n times, i.e. once for each consulted supplier, and the risk index is obviously different for each supplier. Moreover, for the sake of simplicity the membership functions for variables SD, PL and SE are triangular and cross vertically at a 0.5 degree of membership (completeness level). The fuzzy sets describing the output variable RI are three in number, namely Low (L), Medium (M) and High (H). Both the membership functions are represented in Figures and 2, respectively, and the rule table is reported in Table. Similar to FIS, FIS2 evaluates the weights w and w 2, variable in the 0 range, required in the vendor rating model (2) on the basis of the product characteristics. Hence, the outputs w and w 2 of FIS2 are determined based on the input variables standardization level (SL), strategic importance (SI) and availability (A) of the required product. Hence, in contrast with FIS, FIS2 employs as input variables only the data (iv)-(iii) that are fixed in the exchange. Consequently, FIS2 is invoked in the single-item vendor rating procedure only once and the weights w and w 2 are identical for each supplier. In addition, similarly to FIS, the membership functions for variables SL, SI and A are triangular and display a 0.5 completeness level. The fuzzy sets describing the output variables are three in number, namely L, M and H. The input and output membership functions are represented in Figures and 2, respectively, while the rule table is reported in Table 2. In addition, for the fuzzy operators implementing the inference process in both the described FIS components are chosen as follows: the minimum as and operator, the maximum as or operator, the minimum as implication method, the center of gravity as defuzzification method. 3.3 The vendor rating algorithm The above described method may be summarized by the following algorithm, comprising nine steps. Step. Determine the set of bidding suppliers S={s,,s n } and their bids p,,p n. Step 2. Determine the normalized prices P,,P n according to (3). Step 3. Obtain by expert judgments the triplet (SL,SI,A) characterizing the product. Step 4. Invoke FIS2 and evaluate weights w and w 2. Step 5. For each i=,..,n obtain by expert judgments the triplets (SD i,pl i,se i ) characterizing supplier s i. Step 6. For each i=,,n invoke FIS and determine the normalized risk index RI i. Step 7. For each i=,,n apply (2) and determine the i-th vendor performance rating index VPR i. Step 8. Determine the ranked set of alternatives represented by the ordered set S*, where all the elements of set S are arranged in S*={s i,,s in }, according to the increasing order of the corresponding VPR i index value. Step 9. Determine the best-rated supplier as the first element of the ordered set S* (i.e. s i, with top score VPR given by (4)). The worst one is s in. 4 The case study We evaluate the presented approach by way of a real case study, namely the contract regarding the renovation of a building facility of, Italy. The number of contacted suppliers is n=45 and their labels, bids and normalized bids s i, p i and P i are collected in the first three columns of Table 3. Note that prices range from bids of about 08 k to about 32 k. In particular, according to price only, s 42, highlighted in bold in the table, is the best supplier, while the worst one is s 9, also highlighted in bold. We now apply the proposed vendor rating model. The requested service is rather standardized, its strategic importance is quite low and the goods availability is very high. Hence, expert judgments lead to selecting: SL=0.7, SI=0.3, A=0.8 and by application of FIS2 we determine the price weight w =0.6 and the risk importance w 2 =0.389, reported in columns four and five of Table 3. Then, for each i=,,45 the suppliers indices SD i, PL i and SE i are evaluated via expert judgments, as reported in columns six to eight of Table 3. Accordingly, FIS is invoked and index RI i is estimated (ninth column of Table 3). Finally, (2) is applied, the suppliers score VPR i is determined (last column of Table 3), and the top supplier is obtained by (4). The obtained results of Table 3 show that the best supplier s 35 (highlighted in bold) exhibits one of the lowest prices, since it holds P 35 =0.840 and the lowest risk index, with RI 35 = On the contrary, supplier s 42, offering the best price with the lowest normalized bid P 42 =0.820, is ranked only twenty-third, since its risk index is quite high with RI 35 =0.53. The latter low rating is mainly due to the lower partnership level and experience of this vendor with respect to the winner, that are not balanced by its larger dimension. Indeed, the top vendor s 35 is well known to the buyer with a partnership level SPL 35 =0.8 and is assigned a value SE 35 =0.8 (since it complies to the ISO 900/00 quality certificate process), but it is a quite small firm with an index SD 35 =0.3. Moreover, supplier s 42 is newer to the customer with SPL 35 =0.2 and, despite being an average dimension company with SD 42 =0.6 and having some experience in the field, does not comply to any quality audit process, so that it is assigned SE 42 =0.7. Finally, note that vendor s 7 (highlighted in bold in Table 3), although offering an acceptable price, is ranked only forty-first since it exhibits the worst risk index with RI 7 =0.56.

6 Supplier s i p i [ ] P i w w 2 SE i SD i SPL i RI i VPR i s s s s s s s s s s s s s s s s s s s s s s s s s s s s s s s s s s s s s s s s s s s s s Table 3: The vendor rating results for the case study. 5 Conclusions The paper focuses on a crucial issue of purchasing in supply chains, i.e. vendor assessment. More precisely, we propose a novel fuzzy logic based approach to vendor rating for single-item multiple sourcing exchanges. Thanks to fuzzy reasoning, the presented method enables the integration of the risk component with the major decision factor for suppliers ranking, i.e. price. Hence, the approach incorporates both quantitative and qualitative data. Moreover, a real case study illustrates its effectiveness. Future research may extend the methodology considering additional factors, e.g. quality and geographical location, as well as the more complex case of multi items exchange. References [] V. Albino, A.C. Garavelli, M. Gorgoglione, Fuzzy logic in vendor rating: a comparison between a fuzzy logic system and a neural network, Fuzzy Economic Review, Vol. 3, No. 2, pp , 998. [2] P. Baily, D. Farmer, D. Jessop, D. Jones, Purchasing, Principles and Management, Prentice Hall, London, UK, 9 th ed., [3] S. Biswas, Y. Narahari, Object oriented modelling and decision support for supply chains, European J. of Operational Research, Vol. 53, No. 3, pp , [4] C.-C. Chen, T.-M. Yeh, C.-C. Yang, Customerfocused rating system of supplier quality performance, J. of Manufacturing Technology Management, Vol. 5, No. 7, pp , [5] Z. Degraeve, E. Labro, F. Roodhooft, An evaluation of vendor selection models from a total cost of ownership perspective, European J. of Operational Research, Vol. 25, No., pp , [6] M. Dotoli, M.P. Fanti, C. Meloni, M.C. Zhou, Design and optimization of integrated e-supply chain for agile and environmentally conscious manufacturing, IEEE Transactions on Systems Man and Cybernetics, part A, Vol. 36, No., pp , [7] J. Hallikas, V.-M. Virolainen, U. Pulkkinen, M. Tuominen, Managing risk in purchasing strategy selection, Proc. th IPSERA Annual Conference, Enschede, The Netherlands, pp , March [8] B. Karpak, E. Kumcu, R. R. Kasuganti, Purchasing materials in the supply chain: managing a multi-objective task, European J. of Purchasing and Supply Chain Management, Vol. 7, No. 3, pp , 200. [9] S. Piramuthu, Knowledge-based framework for automated dynamic supply chain configuration, European J. of Operational Research, Vol. 65, No., pp , [0] K. Sadgrove, The Complete Guide to Business Risk Management, Gower Publishing, Aldershot, Hampshire, England, 2 nd ed., [] L.R. Smeltzer, S.P. Siferd, Proactive supply management: the management of risk, International J. of Purchasing & Materials Management, Vol. 34, No., pp , 998. [2] M. Treleven, S.B. Schweikhart, A risk/benefit analysis of sourcing strategies: single vs. multiple sourcing, J. of Operations Management, Vol. 7, No. 4, pp. 93-4, 988.