MODELLING COMMODITY SUPPLY CHAIN IN HIGHER EDUCATIONAL INSTITUTE. Abstract 1. INTRODUCTION

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1 MODELLING COMMODITY SUPPLY CHAIN IN HIGHER Abstract EDUCATIONAL INSTITUTE *K. M. Sharath Kumar 1, S. R. Shankapal 1 Senior Lecturer, Director, M.S. Ramaiah School of Advanced Studies, Bangalore *Contact Author sharath@msrsas.org Collaborative commerce ensures adding value at every intermediate stage in the supply chain and is nowadays recognised as a powerful source of competitive advantage. This concept of adding value can be applied to the educational scenario with Higher Educational Institutions (HEIs) procuring multi-commodities. Rationale for the study has been to eliminate legacy procurement system and implement industry models in HEIs based on value and importance of multiple commodities. An attempt has been made to model commodity supply chain which will add value to HEIs. The study involved development of product categorisation matrix which segregated the commodities as general, bulk purchase, critical and strategic commodities. Mathematical modelling of these items has been developed, using economies of scale for general and bulk purchase category, uncertainty for critical category and product availability for strategic category. Frameworks for commodities of different categories have been constructed using spreadsheets. Lastly, the proposed model has been validated using operational data of a HEI (MSRSAS) from previous period Results showed significant savings in supply chain cost. Critical items have been evaluated in terms of cycle service level and fill rate. Finally, based on the demand pattern, suitable inventory levels for multi-commodities have been suggested. In principle, the outcome of the study would help the procurement managers of HEIs to manage multicommodity procurement scientifically and optimally. Keywords: Product Categorisation Matrix, Cycle Service Level, Fill Rate, Safety Stock Nomenclature C, rupees C i for commodity i, rupees CSL Cycle service level, % D Average Demand, D L Demand during lead time, D i Demand for commodity i, L Lead time, days Q Order quantity, Q * Optimal order quantity, Q i Order quantity for commodity i, S Order cost, rupees S * Combined order cost, rupees TC Total supply chain cost, rupees f r Fill rate, % hc Holding cost, rupees m i Order interval of commodity i n Order frequency, times/year n* Optimal order frequency, times/year q i Specific range of order, s i Additional order cost, rupees σ D Standard deviation of demand, Standard deviation during lead time, weeks σ L Abbreviations CD Compact Disc DVD Digital Video Disc ESC Expected Shortage HEI Higher Educational Institute ROP Re-Order Point SCM Supply Chain Management SS Safety Stock 1. INTRODUCTION Institute of Supply Management defines Supply Chain Management (SCM) as design and management of seamless, value-added process across organisational boundaries to meet the real needs of end customer. Commodity supply chain, which processes multicommodity purchase targeting cost through reduction in administrative and processing cost has been considered as opening for the study. The main objective of commodity supply chain has been to attain value for money and accountability [1]. Value for money has been considered to ensure use of funds productively. Accountability has been considered to promote costeffectiveness in administrative and research areas, thus creating a rationale for modelling commodity supply chain in HEIs. Significance of educational SCM improves the well-being of end customer and this exploratory study addresses modelling commodity educational supply chain. The proposed conceptual model for the HEIs provides a novel approach for decision makers using economies of scale for general and bulk purchase category, uncertainty for critical category and product availability for strategic category. From a managerial perspective, the study provides an approach to develop and assess application of SCM in academia. In principle, an attempt has been made to model commodity supply chain which will add value to HEIs. The main objective of mathematical formulation has been to optimise the supply chain cost during multicommodity purchase. This paper examines the possibility of handling multi-commodity in HEIs with an integrated framework by optimising the cost. The spreadsheets help to reduce the calculation time of procurement managers and divert resourceful time for value addition activity. Overall, an attempt to manage multi-commodities objectively has been made which can add value to the organisation. SASTECH Journal 39 Volume 9, Issue, September 010

2 . LITERATURE REVIEW Satisfying customers and creating trust among trading partners in the value chain has made SCM concepts imperative in organisations []. The study also emphasised alignment of supply chain strategy with organisational goals. However, traditional SCM has been associated with flow of orders and materials through the chain adding value, i.e. predominantly a logistics view. The study also embarked upon the concept of adding value at every stage that can be applied to the educational scenario, with Higher Educational Institutions (HEIs) offering industrial placements and exchange facilities with overseas institutions as additional features of their programmes. The evolutionary time line of SCM [4] is shown in Fig.1. During last decade the research findings on SCM have extensively appeared in different types of management areas like strategic management, operations management, information management and knowledge management [1]. Discerningly, these papers discuss the supply chain issues in profit organisation for adding value or reducing cost. Furthermore, only few research papers are available which discuss about educational supply chain [1,3]. Latter concept of adapting industry supply chain models to HEIs through collaboration has been examined and yielded positive results from the study [3]. In perpetuation, three types of educational supply chains have been diversified as commodity supply chain; special requested supply chain; and outsourcing supply chain [1]. The study used Yin s case study approach, which has been considered subjective in nature. Hence, an objective method of obtaining the supply chain performance in educational supply chain scenario becomes necessary for modelling supply chain quantitatively. In principle, this approach connects education management and general business management [1]. In addition, a few studies have been carried out in linking SCM and management accounting [5]. Therefore, modelling supply chain quantitatively has been warranted to minimise the procurement cost optimally as HEIs procure multi-commodities in their value chain. Parallely addressing critical issues to establish effective supply network in a global supply Fig. 1 Evolutionary time line of SCM [4] chain network has been considered important [6]. In the study, supplier portfolio model has been used to cluster suppliers based on key factors. Finally, additional use of integrated spreadsheets allows analyses of logistics and supply chain issues from different perspectives effectively [7]. Modelling supply chain quantitatively under different phases of supply chain along with the usage of spreadsheets has been reported from industry perspective [8]. Nevertheless, data driven mathematical models play a crucial role for managers in effective decision-making process [9]. 3. RESEARCH METHODOLOGY The main aim of the study has been to model commodity supply chain for HEIs using strategic and tactical planning. Firstly, the scope of the case has been clearly defined, wherein data relevant to commodity purchase has been collected. The procurement personnel has been requested to explain the current process followed in chosen HEI. Secondly, categorisaton of different commodities has been carried out using product categorisation matrix, which has been used as a primary tool to differentiate based on their usage in terms of value and importance. The matrix has been used to segregate commodities as general, bulk purchase, critical and strategic commodities. Low value items or indirect materials such as glue, letterheads have been considered as general commodities. Commodities like books, compact discs, which have been procured in bulk, have been considered as bulk purchase commodities. Laboratory equipments have been considered as critical commodities; and security and bookstore have been considered as strategic commodities. Thirdly, modelling of the commodity supply chain for different types of commodity categorisation has been carried out. Demand has been assumed stable for general and bulk purchase modelling. Similarly, continuous replenishment and fixed lead time has been assumed for modelling critical and strategic commodities. Mathematical modelling of these items has been formulated using economies of scale, uncertainty and product availability. General commodities have been formulated by aggregating the products in the form of lot sizing. Bulk purchase commodities have been formulated considering SASTECH Journal 40 Volume 9, Issue, September 010

3 different buying situations. Critical commodities have been evaluated in terms of demand pattern. Fourthly, strategic commodity has been evaluated in terms of Cycle Service Level (CSL) and fill rate (fr). Spreadsheet frameworks for supply chain with respect to different category commodities have been developed to ease calculation procedure. Finally, the viability of frameworks by implementing supply chain model to MSRSAS operation has been carried out by conducting pilot run using past data. Fig. Commodity categorisation in HEIs 4. SOLUTION PROCEDURE Categorisation matrix plays a pivotal role in the study to segregate multi-commodities based on their value and importance. Figure shows the commodity categorisation matrix by value and importance. The matrix shows general commodities having lower value and importance, as listed in first quadrant. Bulk purchase commodities have been considered as high value for money and less importance as shown in second quadrant. Critical commodities have been considered as low value for money and high importance as shown in third quadrant. Similarly strategic items have been considered as higher value and higher importance as shown in fourth quadrant. 4.1 General Commodity Framework The formulation has been carried out to maximise the profit or minimise the supply chain cost. The supply chain cost can be obtained as the sum of material cost, ordering cost and holding cost. Demand (D) is assumed as stable and for single commodity lot sizing the supply chain total cost (TC) is shown in Equation (1), which includes all costs (C). Optimum order quantity (Q*) is considered as optimal lot size, as shown in Equation (). At this point the ordering cost (S) is equal to the holding cost (hc). Holding cost is assumed as 0% of the material cost. Optimal order frequency (n*) is represented in Equation (3). D Q TC CD + S + * hc Q Q n * (1) * DS () hc * D * Q (3) With this background, complete aggregation of multi-commodities in a single order has been carried out to know the impact of supply chain cost savings. Demand (D i ) is considered for commodity i and s i has been considered as an additional order cost if product i has been included in the order along with fixed order cost (S). Therefore the combined order cost (S * ) is depicted in Equation (4). The total supply chain cost is represented in Equation (5). S * S + si * * ( ) DihCi TC + + CiDi S * * n _ ni n Similarly, tailored aggregation of multicommodities has been carried out to know the impact of supply chain cost savings. Interval (m), where commodity i is ordered for m i deliveries play a crucial role for multi-commodity lot sizing. Firstly, the most frequently ordered product, when procured independently is calculated using Equation (6). Highest order frequency (n) infers that commodity i which has to be included in every interval cycle. DihCi ( S + s ) i (4) (5) (6) Secondly, the frequency for the remaining commodity has been calculated using Equation (7). ni DihCi s i (7) Thirdly, the evaluation of the intervals for remaining commodity i is carried out using Equation (8). The interval (m i ) of multi-commodity is determined by rounding off the value obtained. mi _ ni ni (8) Lastly, re-calculating the ordering frequency of the most frequently ordered commodity (n) using equation (9), the total supply chain cost is obtained. n S + mi si mi DihCi 4. Bulk Purchase Commodity Framework Comprehensively, all unit quantity discounts have been considered as the buying situation. The main objective of this framework has been to maximise profit or minimise supply chain cost. In all unit quantity discount, the pricing schedule has a specified range in the form of q0, q1, q,, qn; and the subsequent cost (C i ) decreases as the quantity increases. For each value of i, following three cases are considered for Qi : qi Qi qi+1 Qi < qi Qi qi+1 (9) SASTECH Journal 41 Volume 9, Issue, September 010

4 The last case has been ignored as it becomes equivalent to first case in next iteration. Furthermore total cost (TC) can be calculated using appropriate Qi or qi procurement situation. 4.3 Critical Commodity Framework The formulation has been carried out assuming continuous replenishment policy with only demand as uncertain. The main objective of this framework is to counter demand uncertainty. Mathematical formulation to know CSL and fr is carried out to make appropriate trade-off based on the demand pattern. Uncertainty in demand is considered as normal distribution with mean as average demand (D), standard deviation of demand (σ D ) with lead time (L). Average inventory left when the replenishment lot arrives, is known as safety stock (SS). Enumerating the appropriate re-order point (ROP), is considered critical in this framework. The parameter SS is calculated using Equation (10). ss ROP DL (10) With this setting, the expected demand during lead time (D L ) and standard deviation during lead time ( σ L ) are written mathematically as shown in Equations (11) and (1) respectively. D * L DL σ σ D (11) L * (1) L CSL for a replenishment policy is evaluated by considering as the probability of not stocking out during the replenishment cycle. Scientifically, CSL is calculated using Equation (13). ( ROP, ) DL σ L CSL F, (13) Similarly, fill rate (fr) estimates the portion of demand satisfied when Q are ordered as the quantity on hand drops to ROP. The process of Expected Stockout (ESC) has to be understood to evaluate fr. Chopra and Meindl (008) have shown mathematical interpretation of ESC (Equation 14). For a lot size of Q, the expected portion of demand lost is given by equation (15) and the commodity fill rate has been obtained using equation (16). and stockout cost. The formulation is carried out to arrive at appropriate inventories for individual commodities from the desired level of product availability. Firstly, an estimation of appropriate inventory level for different commodities is carried out with respect to desired CSL. Equation (10) and (13) show the mathematical formulation of SS and CSL respectively. Equation (13) is further modified and interpreted as Equation (17) below. ( D + SS, σ ) L DL L CSL F, (17) Equation (16) is re-arranged to get the mathematical relation for SS, as in Equation (18). 1 ( CSL) σ L (18) SS Fs * Secondly, the estimation of appropriate inventory level for different commodities is carried out with respect to desired fr. Equation (14) and (16) shows the mathematical formulation of ESC and fr. After calculating ESC, appropriate inventory level for different commodities is calculated. Spreadsheets are constructed for different frameworks in order to ease the calculation efforts of the procurement manager in mathematically formulating different commodities based on their value and importance. In these spreadsheets, the procurement manager has to key in only the variable information of the commodities. This makes the proposed model simple towards promoting cost-effectiveness in HEIs. This means that keeping academic quality, the HEIs shall manage multi-commodity supply chain effectively with cost being an important factor. 5. RESULTS AND DISCUSSIONS The proposed model is assimilated for viability by considering previous data from MSRSAS HEI from April 008 to March 009. General and Bulk purchase frameworks are evaluated based on supply chain cost, which includes material, order and holding cost. In the same way, critical and strategic commodities are evaluated based on CSL and fr. Overall a holistic approach to manage commodity supply chain optimally for HEIs has been made. SS + SS ESC SS 1 Fs σ fs (14) L σ L σ L Portion of demand lost ESC Q (15) Commodity fill rate, f r ESC 1 (16) Q 4.4 Strategic Commodity Framework The formulation has been carried out assuming continuous replenishment policy. The main objective of this framework is to maintain optimum commodity or product availability. Business settings have desired level of product availability and design replenishment policy to achieve that level. The desired level of product availability is determined by trading off holding cost Fig. 3 Order size comparison of legacy and proposed model 5.1 General and Bulk Purchase Framework By implementing the proposed model for HEI, a scientific approach towards procurement activities is SASTECH Journal 4 Volume 9, Issue, September 010

5 carried out based on value and importance of the commodity. The proposed general and bulk purchase framework is implemented through pilot study by considering compact Discs (CDs), white board markers, paper reams and printer cartridges as commodities. Information related to quantity purchased, average unit cost in rupees and order frequency is collected in order to understand the legacy system. Fig. 4 Order frequency comparison of legacy and proposed model Firstly, with this information, a single product lot sizing for the selected commodities has been carried out. Order size (Q) comparison of legacy and proposed model is shown in Fig. 3. The results suggested to increase the order size so as to arrive at an optimum order. The study suggested reducing the order frequency (n), as shown in Fig. 4. Table 1. Comparison between legacy and lot sizing single commodity General Order Size Commodity Comparison Savings 1 Compact Disc 75% 40% White Board Marker 1% 75% 3 Paper Reams 60% 60% 4 1A Cartridge 50% 50% 5 16A Cartridge NIL 7% Table. Multiple lot sizing for commodities Description Demand per Year D in Order Frequency n* Optimal Order Size Q* in Cycle Inventory in Annual Holding in Rs. Annual Order in Rs. Total Annual in Rs. Compact Disc White Board Marker Paper Reams times/year The comparison between order size and fixed cost saved by implementing single commodity lot sizing is shown in Table 1. Secondly, the impact of aggregation is carried out by multiple commodity lot sizing. Commodities like CDs, white board marker and paper reams are considered for the study. Based on the demand pattern, optimal order quantity (Q*), optimal order frequency (n*) and supply chain costs in the form of holding and ordering cost are calculated as shown in Table. Material cost is not considered due to equal demand for commodities in single lot sizing and multiple lot sizing. Table 3. Comparison of single commodity and multiple commodity lot sizing 1 3 Supply Chain Holding Order Annual Single Commodity in Rs. Multiple Commodity in Rs. Saved % % % Table 3 shows the cost comparison between single commodity lot sizing and multiple commodity lot sizing. savings of up to 14.1% have been depicted from the study due to aggregation. Thirdly, tailored aggregation is carried out for the same commodities as that of aggregating multiple commodities. Table 4 shows optimal order frequency (n*), optimal order quantity (Q*), total holding and ordering cost, which is calculated using tailored aggregation. Figure 5 shows the cost comparison between tailored aggregation, and single and multiple commodity frameworks. The comparison of cost savings in percentage between single, multiple aggregation and tailored multiple aggregation is shown in Table 5. Fig. 5 comparison of tailored framework with single and multiple commodity model Similarly bulk purchase framework is applied based on different criteria s of procurement situation like quantity discounts. Significant savings are depicted and the spreadsheet framework is developed in order to ease the calculation procedure for procurement manager of HEI. Figure 6 shows the spread sheet framework for bulk purchase framework. SASTECH Journal 43 Volume 9, Issue, September 010

6 Table 4. Lot sizing using tailored aggregation Description Demand per Year D in Order Frequency n* in times/year Optimal Order Size Q* in Cycle Inventory in Annual Holding in Rs. Annual Order in Rs. Total Annual in Rs. Compact Disc 5. Critical Commodity Frame Work White Board Marker Fig. 6 Bulk purchase framework Paper Reams The framework is applied to commodities like Digital Video Discs (DVDs), white board marker and paper reams on a pilot run which are considered as critical for the study. CSL and fr are evaluated for DVD s which helps the procurement manager to devise replenishment policy as shown in Table 6. Table 5. Lot sizing using tailored aggregation Description Single vs. Multiple Lot Sizing Percentage Saved Multiple Lot Sizing vs. Tailored Aggregation Percentage Saved 1 Holding 8.4% % Ordering 14.1% % 3 Annual 11.35% % Current level of CSL is considered as 53.5% and fr is considered as 87.85%. Based on the obtained values, procurement managers can take suitable action to improve CSL and fr. Parallely, CSL and fr are evaluated for other considered commodities and their CSL and fr evaluation comparison are shown in Fig. 7. Results suggested that CSL for critical commodities has to be increased. However, fr found at the present level is appropriate. These results help the procurement manager to arrive at appropriate replenishment policy for different critical commodities which have high importance and low value in commodity categorisation matrix. Fig. 7 Comparison of Cycle Service Level (CSL) and fill-rate (fr) for critical commodities SASTECH Journal 44 Volume 9, Issue, September 010

7 Table 6. CSL and fr evaluation for DVDs Description Fig. 8 Inventory level comparison at 95% fill-rate (fr) Values 1 Average Demand Per Month in Fixed in Rs Holding in Rs Optimal Order Quantity Q* in Optimal Order Frequency n* in times/year Cycle Inventory in Avg. Lead Time in days ROP 75 9 Standard Deviation σd Safety Inventory in Standard Deviation during Lead Time σl Demand during Lead Time DL in CSL in % Expected Shortage per Cycle ( ESC ) in fr in % Strategic Commodity Framework Commodities with high importance and high value in commodity categorisation matrix come under this framework. HEIs need to collaborate with bookstore vendor, security agency etc. as strategic activity in their commodity supply chain. Normally, these collaborations are defined in terms of service level agreements in management contracts. This framework quantifies the appropriate service level into appropriate inventory levels by evaluating CSL and fr. For the present study, 90% CSL and 95% fr has been considered for DVDs, white board marker and paper reams, commodities with respect to book store vendor. The inventory levels at 90% CSL for these commodities are shown in Table 7. Similarly fr at 95% is evaluated for the same commodities. The safety stock level comparison of legacy and proposed model is shown in figure 8. The result suggests to decrease the order quantity for white board markers and increase the order size for paper reams and DVDs. 1 Table 7. Inventory Levels at 90% CSL Criteria Optimal Quantity Q* in White Board Marker A4- Sheets DVD's CSL in % 90 3 Lead Time in days Demand Per Month in Std. Deviation of Lead time σl Safety Inventory Required in Cycle Inventory in Avg. Inventory in 6. CONCLUSIONS The integrated framework helped to rationalise procurement efforts in multi-commodity situation in HEIs. Product categorisation matrix has been useful inorder to segregate commodities based on value and their importance. savings of up to 50% from the selected commodity lot is depicted using aggregate approach for multiple products for general and bulk purchase commodities. CSL and Fill-rate is successfully used for critical and strategic commodities to convert subjective information to objective information. The outcome of the study would help the procurement manager of HEIs to manage multi-commodity scientifically and optimally. Spreadsheet frameworks are constructed in order to cut-down the calculation time for procurement manager. An effort to bring industry oriented supply chain concepts to academic scenario is made. SASTECH Journal 45 Volume 9, Issue, September 010

8 7. REFERENCES [1] Antonio K.W. Lau, Educational Supply Chain Management: a case study, On The Horizon, Vol.15, Issue 1, pp. 15-7, 007. [] B.S. Sahay, Vasant Cavale, Ramneesh Mohan, The Indian Supply Chain Architecture, Supply Chain Management: An International Journal, Vol. 8, Issue, pp , 003. [3] Elain M. O Brien, Kenneth R. Deans, Educational Supply Chain: A Tool for Strategic Planning in Tertiary Education, Journal of Marketing Intelligence & Planning, Vol. 14, Issue, pp , [4] Md. Mamun Habib, Chamnong Jungthirapanich, An Integrated Framework for Research and Education Supply Chain for the Universities, Proceedings of Fourth IEEE International Conference on Management of Innovation and Technology, Thailand, September 1-4, 008. [5] Miguel Martinez Ramos, Interaction between Management Accounting and Supply Chain Management, Supply Chain Management: An International Journal, Vol. 9, Issue, pp , 004. [6] Kun Liao and Paul Hong, Building Global Supplier Network: A Supplier Portfolio Entry Model, Journal of Enterprise Information Management, Vol. 0, Issue 5, pp , 007. [7] Gary A. Smith, Using Integrated Spreadsheet Modeling for Supply Chain Analysis, Supply Chain Management: An International Journal, Vol. 8, Issue 4, pp , 003. [8] Sunil Chopra, Peter Meindl, Supply Chain Management: Strategy, Planning and Operation, 3 rd Edition, Prentice-Hall India Private Limited, New Delhi, 008. [9] Jeremy F. Shapiro, Modeling the Supply Chain, Thomson Duxbury, Singapore, 00. SASTECH Journal 46 Volume 9, Issue, September 010