The recent disturbing events across the globe such as climatologic disasters, epidemics, political instability, terrorism and financial scams have emanated the potential risks to global supply chains practices. Apart this the inclination towards the efficient strategies like lean practices, lower inventories, outsourcing and single sourcing are also making the supply chains vulnerable to dependency risks. Supply chains can be disrupted at supply side, customer side or at the focal firm locations during the processing, but the associated criticality is that the severity of the risks propagates through supply chains and deteriorates the performance of not only the one firm but the entire network. This paper examines risks to the supply side primarily focusing on lower tier suppliers which are usually the small medium enterprises. In this study elements of system reliability and product availability are estimated with the infrastructural risks of lower tier suppliers to develop a combined risk assessment index under demand and supply uncertainties. This approach can further be enhanced to rank suppliers and to assess overall supply chain performance.

Keywords- SME, reliability,availability, risk assessment

I. INTRODUCTION Supply chain management is a key concern in today’s scenario with the prime motive to integrate elements of supply networks to improve the overall profitability and value addition [1]. The firms around the world realized the benefits of focusing on their core competencies and rely on external agencies to supply the raw materials, parts, components, services etc having sufficient expertise and efficient mechanisms [2]. Globalization has further enhanced this practice by exploding the work across various countries and enterprises. Thus the dependency on suppliers created a situation where the firms survive and grow on the basis of the competency of each member of the network rather than their own performance. This situation has emanated dependency risks in supply networks. Moreover, the recent series of climatologic disasters, like tsunami in Japan and middle east, Hurricanes in US, Volcano eruption in Europe etc, political instability and terrorisms across the globe, financial scams in many countries and economic slowdowns has drastically interrupted the supply base that further deteriorate the performance of entire supply chains operated globally [3]. The changing business environment is affecting the entire globe but the impact on developing nations like

India is more significant. Indian market provides a giant supply hub for global manufacturing as well as process industries with their strength of low production costs and superior IT solutions. These scenarios have filled the Indian markets with enormous opportunities but on flip side make the whole supply networks vulnerable to uncertainties and risks. Indian market is also marked by a high number of small medium enterprises (SMEs) operated under various industrial sectors. Certain marked limitations with Indian SMEs like inadequacy in global exposure, lack of managerial and financial expertise, insufficient working capital, infrastructural obsolescence and lack of agility has make their position vulnerable in changing business environment. Large enterprises are found in better position to manage themselves under the pressure of global risks but SMEs are still struggling to cope up with this situation. They are losing their stakes in global supply networks as they are considered as the source of the supply risks. This observation is found very close to the Zanger’s [4] study that reflects SMEs position in supply chain and indicates that in large supply networks, SMEs noticed a marked increase in the risk of dependency and a loss of autonomy, as well as opportunistic behaviour of the larger companies. Finch [5] also endorses this with his empirical investigations and argues that SMEs greatly increases the supply risks when becoming part of supply chain with LEs. Therefore, in changing business environment study of supply side risks are becoming the focal issue of research to secure the uninterrupted supply of material, money and information throughout the networks. Thus assessing the suppliers based on their capability to manage demand and supply uncertainties and their infrastructural status which relates to operational risks could be a significant contribution. We include two aspects in our study to develop the combined risk assessment index. Firstly, we consider the product availability as a measure to suppress the demand supply mismatch and secondly, the operational reliability of the firm, that ensures the functional effectiveness and ability of the firm to fulfill the orders in due time with desired quality and quantity. The remainder of the paper is as follows: In section II we have discussed the thematic background of the supply risk issues relevant to the study. In section III methodology is discussed and a framework is proposed. Section IV presents a case-study with SME followed by discussions on findings in subsections. In section V limitations of study are given and finally section VI

Risks Assessment of Lower Tier Suppliers Using Operational Reliabilities and Product Availabilities

Gopal Agarwal1, Piyush Singhal2, Murari LaiMittal1

1Department of Mechanical Engineering, Malaviya National Institute of Technology Jaipur, Jaipur, India 2Department of Mechanical Engineering, GLA University, Mathura, India

(piyushsinghal2003@yahoo.co.in, agarwal.drg@gmail.com, mlmittal.mnit@gmail.com)

978-1-4577-0739-1/11/$26.00 ©2011 IEEE 226

presents significant conclusions of the study and area of future search.

II. BACKGROUND Supply side risks are associated to the procurements and are considered to be the threat to supply assurance, possibility of improper supplier selection, increased company liabilities and uncertainty in supply lead time [6,7,8]. Upstream risks are well studied by researchers as the controlling of them can improve both efficiency and responsiveness of the supply chain. Wu, Blackhurst and Chidambaram [9] propose an integrated framework to classify, manage and assess inbound risks with an emphasis on quantification of the risk factors associated with suppliers so that supply disruptions can be mitigate. Procurement portfolios are also considered for smooth and uninterrupted supply. Aggarwal and Ganeshan [10] propose a model integrating B2B and risk management tools for optimal procurement portfolio under demand and price variability. Supply risk are also investigated on dyadic relationships empirically by [11] and suggest verifiable measures of the supply risk construct including technological uncertainties, market dynamism and market thinness which can be generalize across the firms in different industries. Supplier reliability is a key concern to deal with supply disruptions and high reliability of suppliers and supply networks is always desired. Levary [12] relates the reliability of supplier to the risk and disruption of company operations and term this phenomenon as reliability chain and propose a model to rank the suppliers considering the supplier reliability, country risks, transportation reliability and supplier’s supplier reliability. Keeping optimal redundancy is a crucial decision while designing the system focusing on reliability. Lee [13] focuses on this issue and suggested the mean variance approach to obtain the optimal redundancy considering supplier failure risks. Supplier’s responsibilities, trust and information sharing and associated legal issues are also explored [2]. Benski and Cabau [14] suggest that reliability based on actual field data is better as it provides more realistic results. Focusing on the literature we proposed a framework to assess the risks with suppliers including the operational reliability and the product availability to indicate the responsiveness of system.

III. METHODOLOGY Assessing the suppliers based on operational risks considering system reliability and product availability is a complex phenomenon as it incorporates the number of entwined issues. Supplier’s system reliability plays a significant role towards the uninterrupted supply. For small suppliers it is essential to estimate the operational reliability and relate it with product availability under uncertainty in demand and supply. A Three phase framework is presented in this paper which provides the

information at every phase that improves the decision making abilities of manager (fig.1).

Fig.1. Framework for risk assessment.

A. Phase-I Measurement of Operational Reliablity Operational reliability of supplier plays a significant

role in overall supply chain risk. High value of this parameter is always expected as it indicates the less operational risks with a particular firm. Various traits of supplier related to infrastructural, financial, managerial and technical issues plays a significant role towards the operational reliability of the system. But in our study we primarily focused on SME and consider the infrastructural element to assess the operational reliability. We follow the definition suggested by [15] to measure the reliability that states that ‘reliability is the probability that a system will perform its intended functions satisfactorily in the specified environmental conditions for a stipulated period of time’.

1) Estimation of Empirical Reliability There are various methods through which empirical reliability distribution can be derived directly from the failure data. These methods are nonparametric or distribution-free and do not require the specifications of theoretical distribution and estimation of distribution’s parameters [16]. Limitations of these methods like limited range of prediction, lack of information of physical phenomenon behind the failure can be overcome by fitting the standard distribution and conducting the statistical tests for verification. One of the very accepted methods to estimate the empirical reliability is rank increment method.

a) Rank increment This method is used initially to derive the cumulative failure and subsequently the empirical reliability distribution with or without censored data. Since a censored unit has some probability of failure before or after the next failure, it may influence the rank of the subsequent failure. Ri = [(n+1)–rank of previous failed] / [(n+1)–All previous failed or withdrawn] (1) Ri= increment in rank n = total number of system under consideration

Phase-I Estimation of operation

reliability (i)Empirical reliability estimation (ii)Fitting the standard distribution for generalization (estimation of parameters) (iii)Hypothesis testing using MLEs

of the parameters for estimation of theoretical

reliability

Phase-II Estimation of product

availability (i) Estimation of avg demand(s) and supply lead time(s) for study period (ii)Estimate the variability in demand(s) and lead time(s) for study period (iii)Estimation of product availability

Phase-III Risk assessment and ranking of suppliers using strategic combination

of unreliability and unavailability as the risk measure

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b) Rank adjustment it = i t-1 + rank increment (2) ( it = Rank of failures, i t-1 = Rank of previous failure)

c) Empirical reliability R(t)= 1 – [(it - 0.3)/ (n+ 0.4)] (3)

2) Fitting of Standard Distribution and Hypothesis Testing Standard distribution if fitted to failure data and verified statistically provided more significant information about failure pattern, process and history. For failure data analysis Weibull distribution is most appropriate as it can incorporate the increasing and decreasing failure rates [16]. We have taken a two parameter power function to represents the Weibull distribution. The equation 4 represents the failure rate and equation 5 represents the reliability. h�(t) = (�/�) (t/�)���

(4) R(t) = e

-�� h (t) d t = e -(t/�)� (5)

Weibull distribution is characterized by two parameters namely shape parameter (β) that provides insight into behavior of the failure process and scale parameter (�) which influences both the mean and spread/dispersion of the distribution. It is also called the characteristic life and has the same units as that of the failure time T. Estimation of MLEs are also required as certain goodness of fit tests are based on the maximum likelihood estimator (MLE) for distribution parameters. Thus it is required to estimate the MLE and perform the goodness of fit tests for hypotheses testing.

a) Estimation of Weibull MLE The MLEs of the two parameter Weibull distribution can be estimated numerically [16]. For shape parameter �� ��ti

�ln ti +(n-r) ts��ln ts )/ (ti

��+(n-r) ts���)]

- (1/ ������r) ln ti = 0 (6) For scale parameter ���� ���r�ti

�����n–r) ts���������������������������������������������������

r = number of failures, n = number of system on testing, ti = failure time of ith system, ts =1 for complete data, For complete failure r = n

b) Mann’s test forWeibull distribution This is a special test for Weibull distribution developed by Mann, Schafer and Singpurwalla [17]. The test statistic is given as M=K1 ��� ln ti+1 - lnti )/Mi] / K2� ln ti+1 - lnti )/Mi] (8) Where K1 =[ r/2] K2 =[ ( r-1)/2] Mi = Zi+1 - Zi Zi = ln[-ln(1- (i-1)/(n+0.25)] (9) Here [ r/2] is the integer part of the number r/2. if M>Fcrit the hypothesis accepted otherwise rejected The values of Fcrit can be obtained from the F tables. B. Phase-II Measurement of Product Availability Product availability indicates the firm’s ability to fill the order from available stock without stock-out condition

[18]. Managing product availability in the face of demand and supply uncertainties is a critical task for supply chain managers. The low value of this parameter indicates insufficient safety stocks with supplier to manage the demand and supply uncertainties which further causes high operational risks with the particular supplier. To analyze the problem we consider independent-demand item held at a supply chain and replenished from either a company facility or an external supplier. The item demand is uncertain, while the replenishment process is unreliable.

1) Estimation of Cycle Service Level It is an important metrics to judge the quality of supplier. CSL is the probability that of not having a stock out in replenishment cycle. It is the fraction of replenishment cycles that ends with all the customer demand met [18]. It is measured over a specified numbers of replenishment cycles. In this study the CSL is measured for the dyadic relationship between supplier and supplier’s supplier who provides him certain critical component. To fulfill the demand of their customer, supplier further ordered the components to their suppliers and wants to maintain a certain cycle service level as indicated by product availability. Most of the companies monitor and record product demand (usage rates or unit sales) and replenishment lead times. This can support the statistical theory provides an approach assuming that demand (D) and lead time (L) are independent random variables [19]. Mean Demand during lead timeDL=D.L (10) Std Deviation (uncertainty) in demand during lead time σDL=(L*σD

2 +D2* σL

2)0.5 (11) Reorder point for a certain component = ROP CSL=prob (demand during lead time ≤ reorder point) (12)

C. PhaseIII Risk assessment as the Strategic Mix of Unreliability and Unavailability

The two parameters namely unreliability and unavailability can be derived from reliability and availability and combined strategically to represent the risk index. The index can further be utilized to analyze the performance of a particular firm or as a supplier selection criterion. RI= (1-R)*α+ (1-CSL)*β (13)

IV. CASE STUDY The data was taken from SME produces chemicals and paints. From last few years the firm was facing tremendous pressure at operational level and market uncertainties in form of volatility in demand and unreliability in supply networks. After the discussions with manager and investigation of previous records we identify the root causes of risks and derive the procedure to manage them. As majority of the raw materials are either imported or sourced from small chemical manufacturers which are

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fragmented and located far away, supply reliability seems doubtful. Further, the lack of contractual agreement with suppliers causes the frequent supply failures in the form of short supplies or longer lead times. Paint industries are also characterized by high variety of product having large number of SKUs with small volumes. Apart this the trend of changing customer need and behavior has made the predictability of demand more difficult. The cumulative effect can be studied with the investigation of level of product availability as the indicator of operational risks. Moreover, infrastructural obsolescence and frequent machine failures worsen the problem to the larger extent. To measure the effect of infrastructural elements on operational risk we study the reliability of system as the indicator of firm’s vulnerability. Focusing on actual conditions and available resources of firm, this study attempts to assess the risks considering operational and infrastructural issues.

A. Data Analysis and Results To analyze the risk situation of the firm we employ the model proposed in the paper. We study the records of last three years and group them on quarterly basis for reliability and availability analysis.

1) Operational Reliability Analysis A product line dedicated to produce the cement paint with ball mill as the prime machine is considered for the study. We follow the machine failure data to estimate the empirical reliability on quarterly bases and fit the Weibull distribution to it to measure the theoretical reliability. We further verify the statistical feasibility of data fitting. Results are mentioned in table I.

TABLE I ESTIMATION AND VARIFICATION OF RELIABILITY MLE of Shape parameter

MLE of Scale parameter

Theoretical Reliability(R)

Hypothesis status

1.45 417 0.88 accepted 1.59 281 0.82 accepted 1.51 431 0.90 accepted 1.89 384 0.92 not accepted 1.64 333 0.87 not accepted 1.92 190 0.74 accepted 2.21 190 0.78 accepted 1.83 228 0.80 not accepted 2.11 185 0.76 accepted 1.68 195 0.72 accepted 1.54 208 0.72 Accepted 1.39 215 0.71 not accepted

2) Product Availability Analysis

We estimate the mean and standard deviation of each group for demand as well supply. Knowing that the final product is produced by many components, we consider one most critical raw material for study of supply variability and identify the position of inventory of that component to measure the reorder points. Considering continuous reviewing system of inventory, uncorrelated and normally distributed demand and

supply, we estimated the various cycle service levels as the measure of availability of product. Table II indicates the values of this parameter over the period.

TABLE II ESTIMATION OF CSL UNDER DEMAND AND SUPPLY UNCERTAINTIES

D (in

tons)

σD L (in days)

σL DL σDL ROP(in

tons)

CSL (in %)

20.5 5.2 6 3 123 62.83 150 0.66 18.5 2.5 7 2 129.5 37.58 150 0.70 20.2 5.4 5 5 101.2 101.9 150 0.68 23.5 10 7 5 164.5 120.4 150 0.45 21.4 8.7 9 6 193.0 131.3 150 0.37 18.5 7 8 7 148 131.0 150 0.50 17.5 6.5 6 5 105 88.93 150 0.69 21 4.5 6 6 126 126.4 150 0.57 16.8 9 8 4 134.4 71.85 150 0.58 18.5 10 9 6 166.5 114.9 150 0.44 16.8 9.5 9 8 151.2 137.3 150 0.49 16.5 9 9 8 148.5 134.3 150 0.59

3) Risk Assessment

After discussion with manager we assign 60% ( α =0.6) waitage to unreliability risk element and 40% (β =0.4) waitage to unavailability risk element to estimate the combined risk index. Fig.2 depicts the variation of risk index, system reliability and availability over the study period.

0 1 2 3 4 5 6 7 8 9 10 11 120.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Time period

system reliabilityproduct availabilityrisk index

Fig.2. Variation in risk index, reliability and availability.

4) Findings and Suggestions

The findings of the study depict various vital insights where the SME can focus to improve its performance and acceptability in global supply networks. While conducting the reliability tests we found that for most of the periods the hypothesis is accepted that justifies the fitting of Weibull distribution to the failure data. As shown in fig.2 the status of system reliability was not satisfactory before three years but after that, due to certain measures taken my maintenance staff, the reliability improved for certain duration but again it reduced continuously. Moreover, the value of shape parameter is found greater than one over period of investigation that indicates that failure rate of machine is increasing as it lying in the wear out zone. Thus increasing unreliability of the system significantly increases the combined risk index. It is also observed that firm was not much concerned with the product availability level and having the smaller values throughout the period of analysis. It

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indicates that firms inventory management policies and safety stocks levels are not appropriate to manage the demand and supply uncertainties. These characteristics increase the supply risks with dealing with such type of suppliers. Thus, due to inability to fulfill the orders in time the firm loses its credibility and stake in marker continuously. Continuously increasing risk index indicates the deteriorating position of the firm in market. Few suggestions are given which can improve the system reliability and product availability and reduce the risk index upto some extent.

a) As indicated earlier the failure rate of system are increasing as it is lying in wear out zone, a preventive maintenance could be better options to avoid the costly losses due to sudden failures. Firm can also think to assign certain budget to induce redundancy of key machines. RAM and FMECA analysis can also be initiated to identify, categorize and mitigate the sources of risks and failures.

b) Firm should also invest to maintain the appropriate safety stocks atleast for the critical raw materials to improve the product availability. Further, the firm should make contractual agreements with their suppliers and motivate them to reduce lead times and their variability as it significantly improves the product availability.

V. LIMITATIONS This framework is basically a data driven model thus availability of data is very essential to get the satisfactory results. Fitting of theoretical distributions like Weibull for reliability estimation and normal distribution for demand and supply can causes the deviation from reality. Moreover, subjectivity is involved to set the risk index coefficient which requires a certain level of expertise and experience.

VI. CONCLUSIONS AND FUTURE RESEARCH SCOPES This study presents a framework to analyze the supply risks using system reliability and product availability as the indicator of system performance and risk. The framework has sufficient capability to represents the underlying complexities of the risk issues which may be difficult to analyze by SMEs. As SMEs have limited budgets and expertise this approach may be helpful to improve their visibility to take crucial decisions under complex business risks environment. Further, a risk index is devised considering the unreliability and unavailability element of the system which can be used to evaluate and rank the suppliers. A case related to SME is also examined with this framework. Investigation of the case depicts various causes of risk at operational and infrastructural level. Suggestions are also provided to improve them by taking certain measures. These models can further be utilized to study the effects of various supplier traits on the supply chain performance and risks, and also to investigate the

effectiveness of various risk management strategies under different business environment. Some critical tradeoff issues like low cost supplier Vs reliable supplier, efficiency Vs redundancy, centralization Vs decentralization etc can also be addressed with this framework.

REFERENCES [1] U. Juttner, H. Peck, and M. Christopher, “Supply Chain Risk

Management: Outlining An Agenda for Future Research,” Int. J. Logistics: Research & Applications, vol. 6, no. 4, , pp. 197–210, 2003.

[2] M. Ojala and J. Hallikas, “Investment decision-making in supplier networks: Management of Risk,” Int. J. Production Economics, vol. 104, no. 1, pp. 201–213, 2006.

[3] P.R.Kleindorfer and G.H. Saad, “Managing Disruption Risks in Supply Chains,” Production and Operations Management, vol. 14, no. 1, pp. 53–68, 2005.

[4] C. Zanger, “Opportunities and risks of network arrangements among small and large firms within supply chain,” Proceedings of Sixth International Annual Ipsera Conference , Naples, 1997.

[5] P. Finch, “Supply chain risk management: case study,” Supply chain Management: An Int. Journal, vol. 3, no. 2 , pp 183–196, 2004.

[6] L.R. Smeltzer, and S.P. Siferd, “Proactive supply management: The management of risk,” The J. of Supply Chain Management, vol. 34, no. 1, pp. 38–45, 1998.

[7] M.J. Meixell, and V.B. Gargeya, “Global supply chain: A literature review and critique,” Transportation Research Part E, vol. 41, no. 6, pp. 531–550, 2005.

[8] F. Abbas, A. Marvin, and K. Mehmet, “Perspectives on Global Supply Chain Supply-Side Risk Management,” PICMET-2006 Conference proceedings, Istanbul, Turkey, 9-13 July, pp.2732–2740, 2006.

[9] T. Wu, J. Blackhurst, and V. Chidambaram, “A model of inbound supply chain analysis,” Computers in Industry, vol. 57, no. 4, pp. 350–365, 2006.

[10] P. Aggarwal, and R. Ganeshan, “Using risk-management tools on B2Bs: An exploratory investigation,” Int. J. Production Economics, vol. 108, no. 1/2, pp. 2–7, 2007.

[11] J. Shokley and S.C. Ellis, “Measuring the supply risk construct: An exploratory study”, Clemson University, Clemson, USA, unpublished.

[12] R.R. Levary, “Using the analytic hierarchy process to rank foreign suppliers based on supply risks,” Computers and Industrial Engineering, vol. 55, no. 2, pp. 535–542, 2008.

[13] T.Y.S Lee, “Supply Chain Risk Management,” Int. J. Information and Decision Sciences, vol. 1, no. 1 , pp. 98–114, 2008.

[14] C. Benski, and F. Cabau, “Unrelated Experimental design in Reliability Growth Programs,” IEEE Trans. on Reliability, vol. 44 no. 2, , pp. 199–202, 1995.

[15] C.E. Ebling, “An Introduction to Reliability and Maintainability Engineering,” Tata McGraw-Hill Pub. Company Ltd. 2000, pp. 23–35.

[16] W. Q. Meeker, and L. A. Escobar, “Statistical Methods for Reliability Data,” Hoboken, New Jersey: Wiley, 1998

[17] N. R. Mann, R. E. Schafer, and N. D. Singpurwalla, “Methods for Statistical Analysis of Reliability and Life Data,” John Wiley & Sons, 1974.

[18] S. Chopra, P. Meindl, and D.V. Kalara, “Supply Chain Management, Strategy, Planning and Operation,” Pearson Education Inc, 2007, pp. 361–367.

[19] M. Cardos, C. Miralles, and L. Ros, “An Exact Calculation of the Cycle Service Level in a Generalized Periodic Review System,” The Journal of the Operational Research Society, vol. 57, no. 10, pp.1252–1255, 2006.

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