5 WORKING CAPITAL MANAGEMENT

Transcription

1 105 5 WORKING CAPITAL MANAGEMENT 5.1 INTRODUCTION In working capital management, firms are employing more sophisticated collection and disbursement systems. Maintaining appropriate cash balances or inventory levels involves managing flows. Inefficient use of cash and materials ultimately reduces the firm s profitability. The C2C cycle time attempts to measure the time elapsed between paying suppliers for material and getting paid by customers. C2C cycle time is a unique financial performance metric that indicates how well an entity is managing its working capital. In integrated SCM, the problem arises when one or few companies enjoy the pooled benefits at the detriment of the others. The idea of reallocation of benefits may be good. But in reality, there must be a mechanism to identify opportunities to strengthen all the channel members through strategic agreements. In the present research work, A LPP model is developed with an objective of minimizing total penalty to all entities (firm, suppliers and distributors / customers) in an integrated approach. The LPP model provides optimal solution (payment period, collection period and deferral period) associated with minimum penalty to all entities along the

2 106 supply chain. Firms may use this model as a tool to benchmark the payment and collection periods while formulating strategic partnerships with their counter parts. 5.2 EXPRESSION FOR C2C CYCLE TIME The components of C2C cycle time are inventory days, average collection period and average payment period. C2C cycle time is estimated using the following relation: C2C cycle time = Inventory Average Average days collection period payment period (5.1) Average Inventory Where, Inventory days = X 365 Cost of Goods Sold (5.2) Accounts Re ceivable Average collection period = X (5.3) Net Sales Accounts Payble Average payment period = X 365 Direct Material Cost ---- (5.4) All the above terms are expressed in same units (i.e., days).

3 107 From industry specific goal, the companies try to achieve C2C cycle time that is as low as reasonable (or even negative). A lower C2C cycle time indicates that the company is more efficient in managing its cash flows as it turns its working capital more times per year and generates more sales per rupee invested. Reducing C2C cycle time leads to operational and financial improvements. This also provides guidelines to business that seeks to obtain a proper mix between the amount of resources deployed to working capital and to capital investments. It is evident that a shorter C2C cycle time results in higher present value for net cash flows generated by the assets and ultimately higher value for the business. 5.3 LP APPROACH TO OPTIMIZE C2C CYCLE TIME C2C cycle time for any firm depends on two factors: firstly, inventory days and secondly, the difference between average collection period and average payment period (payment deferral period). The first factor is an internal operational performance measure which reflects the inventory management of a firm. While the second factor is cross-border performance measure which reflects cash conversion efficiency as well as the buyer-vendor relationships upstream and downstream in a supply chain.

4 108 Maintaining longer credit periods (at suppliers end) may be beneficial to the buying firm but supplier(s) are penalized by loss of interest on money blocked with its customers. Similarly, at customers end, longer credit periods may be beneficial to distributors / customers but the firm loses interest on credit sales. On the other hand, shorter credit period leads to loss of interest on purchase price for distributors. In this competitive world, every company tries to maintain shorter C2C cycle time by making the difference of collection and payment periods minimum (or negative). This practice definitely leads to weakened link(s) along the supply chain as it is impossible to all firms along a supply chain to get benefited by achieving minimum (or negative) difference. In an integrated approach to supply chain performance measurement, we must arrive at an optimal combination of average payment period and average collection period which is beneficial and acceptable for buyer(s) and vendor(s) while formulating or revising strategic agreements Formulation of Linear Programming Problem Variables declaration: Let T p = Average Payment Period T c = Average Collection Period

5 109 X 1 = Reduction in payment period X 2 = Reduction in collection period r = Rate of interest Now, the penalty to different entities may be as given below: Penalty to Suppliers = ( Tp X 1) * Payables * r (5.5) Penalty to Distributers = X * Re ceivables* r (5.6) Penalty to Firm = ( 1 Tc X 2 ) * Re ceivables * r Payables * X * r (5.7) Considering a simple case of one-tier supply chain, the total penalty to entities along the chain is given by Z = ( Tp ( Tc X X 1 2 ) * Payables * r 365 ) * Re ceivables 365 X 2 * r * Re ceivables 365 Payables * X * r * r (5.8) The above expression can be simplified as follows: Let a 1 Payables 365 * r a 2 Re ceivables * r 365

6 110 The objective is to find the optimal combination of payment and collection periods that would minimize the total penalty to all firms along the supply chain. The simplified objective function and constraints subject to which the objective function is optimized are furnished below. a 1 Tp a * Tc 2a * * X Minimize Z = Subject to b 1 T p b 2 ; (Constraint 1) b 3 T c b 4 ; (Constraint 2) T p - X 1 b 5 ; (Constraint 3) T c - X 2 b 6 ; (Constraint 4) T c - T p b 7 ; (Constraint 5) T p, T c, X 1, X 2 0. (Constraint 6) In the LPP model, the right hand side values of constraints are current average payment and collection periods (b 2 and b 4 ) or expected lower limits of decision variables (b 1, b 3, b 5, b 6 and b 7 ). The above LPP is solved using TORA (Windows version 2.0, 2006) and the results i.e., the optimal combination of average payment period and average collection period that would minimize total penalty to all firms along the supply chain are tabulated.

7 C2C CYCLE TIME ANALYSIS OF ARBL SUPPLY CHAIN In the present analysis, the focus is on minimizing total penalty by optimizing the payment deferral period. The required data is collected from financial reports of the firm in the past 9 years (i.e., from FY: to ). Table 5.1 provides the data on inventory days, average collection period, average payment period, payment deferral period, C2C cycle time and penalty to supply chain partners with existing payment and collection mechanism in the past nine years assuming rate of interest r = 0.1 (10%). Table 5.1 C2C data of Batteries manufacturing company Financial year Inventory days Average Collection period Average payment period Payment deferral period C2C cycle time Penalty (millions)

8 112 From table 5.1, it is understood that the firm has achieved a minimum of 48 days C2C cycle time but with penalty to its suppliers (by delayed payments). Higher values of C2C cycle time are due to large difference between collection and payment periods (in FYs , & ). Mostly, the average payment period is less than the average collection period (except in FY: ). Still, there is wide scope for improving C2C cycle time by decreasing inventory days and minimizing payment deferral period. Excluding abnormal values (in FY: and ), the correlation between payment deferral period and penalty is Which means that there exists a very high correlation between them Analysis of payment deferral period of ARBL The LPP for optimizing payment deferral period is formulated using the data extracted from financial reports of ARBL. The specimen calculations and formulation of LPP are presented below. 1 Tp a * Tc 2a * * X Objective function: Minimize Z = a Subject to b 1 T p b 2 ; b 3 T c b 4 ; T p - X 1 b 5 ;

9 113 T c - X 2 b 6 ; T c - T p b 7 ; T p, T c, X 1, X 2 0. The values of payables and receivables are extracted from table 3.5 and the specimen calculations are carried out for the year as follows. Specimen Calculation: Payables = million rupees Receivables = million rupees a 1 Payables 365 * r * 0.1 = a 2 Re ceivables * r * =

10 114 From table 5.1, the average payment and average collection periods in the year are taken as upper limits for constraints 1 & 2 (i.e., b 2 = 82 days, b 4 = 85 days). The lower limits are taken as 35 days, 28 days, 21 days and 14 days (5 weeks, 4 weeks, 3 weeks and 2 weeks) with payment deferral period of 3, 4 or 5 days. Hence, b 1 = b 3 = b 5 = b 6 = 35 days (say) b 7 = 5 days (say) The LPP is formulated using the above values as follows: Minimize Z = 0.04*T p + 0.1*T c 0.08*X 1 Subject to 35 T p 82; 35 T c 85; T p - X 1 35; T c - X 2 35; T c - T p 5; In this thesis, TORA is used to solve the LPP as the software facilitates ease of entering data regarding decision variables in objective function and constraints, selecting sense of optimization and type of constraints. It also provides iterative solution as well as final solution report with sensitivity analysis. The screenshot of LPP model in TORA for entering data is shown in figure 5.1 below.

11 115 Figure 5.1 Screen shot showing the formulation of LPP in TORA The computations have been made for different right hand side values of constraints and coefficients in objective function. In each case, an optimal solution is obtained. The results clearly indicate that the optimal combination of average payment period and average collection period are acceptable for pair(s) of trading partners both upstream and downstream the supply chain.

12 116 Table 5.2 provides the optimal average collection and payment periods for which the total penalty will be minimum (at rate of interest r = 10%) by solving LPP model using TORA. The analysis carried out for different Right Hand Side (RHS) values (taking 2 weeks, 3 weeks, 4weeks and 5 weeks as payment period) reveal that the penalty decreases with decrease in payment deferral period as well as average payment and collection periods. Table 5.2 Optimal payment & collection periods for ARBL (FY: ) (14 days) (21 days) (28 days) (35 days) Optimal T p Optimal T c Optimal Deferral period Penalty (Millions) (a 1 = 0.04, a 2 = 0.1 for payables = 133 millions & receivables = 362 millions) The penalty values in table 5.2 for different payment periods reveals that the total penalty along the supply chain is decreasing as the payment and collection period are decreased, for the given values of receivables and payables (sundry debtors and sundry creditors). The screen shots of formulation and solution of LPP using TORA for different values of objective function coefficients and RHS values (from

13 117 FY: to ) are furnished in appendix A. The optimal payment and collection periods associated with minimum penalty in successive financial years have been furnished in tables 5.3 to Table 5.3 Optimal payment & collection periods for ARBL (FY: ) (14 days) Payment (21 days) period (28 days) (35 days) Optimal T p Optimal T c Optimal Deferral period Penalty (Millions) (a 1 = 0.05, a 2 = for payables = 184 millions & receivables = 453 millions) Table 5.4 Optimal payment & collection periods for ARBL (FY: ) (14 days) (21 days) (28 days) Payment (35 days) period Optimal T p Optimal T c Optimal Deferral period Penalty (Millions) (a 1 = 0.047, a 2 = for payables = 173 millions & receivables = 456 millions)

14 118 Table 5.5 Optimal payment & collection periods for ARBL (FY: ) Payment (14 days) period Payment (21 days) period Payment (28 days) period Payment (35 days) period Optimal T p Optimal T c Optimal Deferral period Penalty (Millions) (a 1 = , a 2 = for payables = 102 millions & receivables = 472 millions) Table 5.6 Optimal payment & collection periods for ARBL (FY: ) (14 days) (21 days) (28 days) (35 days) Optimal T p Optimal T c Optimal Deferral period Penalty (Millions) (a 1 = , a 2 = for payables = 283 millions & receivables = 650 millions)

15 119 Table 5.7 Optimal payment & collection periods for ARBL (FY: ) (14 days) (21 days) (28 days) (35 days) Optimal T p Optimal T c Optimal Deferral period Penalty (Millions) (a 1 = 0.157, a 2 = for payables = 574 millions & receivables = 857 millions) Table 5.8 Optimal payment & collection periods for ARBL (FY: ) (14 days) (21 days) (28 days) (35 days) Optimal T p Optimal T c Optimal Deferral period Penalty (Millions) (a 1 = 0.167, a 2 = 0.4 for payables = 608 millions & receivables = 1460 millions)

16 120 Table 5.9 Optimal payment & collection periods for ARBL (FY: ) (14 days) (21 days) (28 days) (35 days) Optimal T p Optimal T c Optimal Deferral period Penalty (Millions) (a 1 = 0.22, a 2 = 0.62 for payables = 808 millions & receivables = 2265 millions) Table 5.10 Optimal payment & collection periods for ARBL (FY: ) (14 days) (21 days) (28 days) (35 days) Optimal T p Optimal T c Optimal Deferral period Penalty (Millions) (a 1 = 0.257, a 2 = for payables = 937 millions & receivables =2078 millions)

17 121 From tables 5.2 to 5.10, it could be understood that the penalty is decreasing with decrease in payment and collection periods as well as the deferral period. Also, the optimal payment and collection periods are independent of the current payment and collection periods of the firm. The sensitivity analysis reports for different objective coefficients and RHS values of constraints from FY: to is furnished in appendix B. The results of sensitivity analyses for different objective coefficients are presented in table The optimal solution given by the LPP model is helpful for firms while negotiating on terms and conditions of supply. The firms can use these values during benchmarking average payment and collection periods. Practically, some tolerance may be allowed on either side of optimal value while taking strategic decisions. Hence, this model helps to identify the penalty associated with different combinations of average payment and collection periods associated with minimum penalty and shorter C2C cycle time for any firm and its supply chain in an integrated approach.. Through the practice of scientific estimation of payment and collection periods, this ideology can be extended to successive echelons for the profit of all firms along the supply chain.

18 122 Table 5.11 Results of sensitivity analysis for different objective coefficients Financial year Payables (million Rupees) Receivables (million Rupees) Objective Coefficients a 1 a 2 X 1 Current Min Max Current Min Max Current Min Max α α α α α α α α α α α α α α α α α α α α α α α α α α α

19 INVENTORY TURNOVER RATIO ITR is defined as the ratio of sales to average inventory with both numerator and denominator being valued at either selling price or original cost. The success of any firm basically depends on how efficiently it is controlling its inventories existing in various forms at different stages of the operations of the firm. Manufacturing firms need to maintain inventories to accommodate unexpected fluctuations in demand and supply. The volume of inventories depends on procurement lead time, the firm s purchasing strategies such as taking advantage of price discounts on bulk purchases, geographical location of suppliers, scarcity of raw materials, expected rise in prices, the accuracy of demand forecast, extent of subcontracting and service level of the firm. By formulating strategic partnership with suppliers, adapting VMI, strategic sourcing decisions such as make / buy / sub-contracting, developing supplier relations (shared vision and objectives), tracking of inventory (Ashwani Kumar, 2007), minimizing inventory record keeping errors (Elgar Fleisch, Christian Tellkamp,2005), customer relationship management (CRM) and so on, the firms can operate with minimum levels of inventory.

20 124 A significant number of inventory related issues such as inventory inaccuracy and its impact on supply chain performance [George M Sheppard & Karen A. Brown (1993), Elgar Fleisch & Christian Tellkamp (2005)], multi-echelon inventory management system [A. Alfieri & R. Brandimarte (1997)], inventory capability & distribution system flexibility [Prashant C. Palvia & D. Lim (2001)], bullwhip effect [Richard Metters (1997), A. Gunasekaran & E.W.T. Ngai, (2004), Bradley Hull (2005), Jiuh-Biing Sheu (2005)], expected inventory level and the stock out probability [Ming Dong & F. Frank Chen (2005)], global inventory visibility [Scott J.Mason et al., (2003)] and Inventory-Distribution coordination [Douglas J. Thomas & Paul M. Griffin (1996)], impact of VMI on buyer-supplier relationship in a single echelon supply chain [A. Mahamani & Dr. K. Prahlada Rao (2010)] have been analyzed by researchers. So far, there is no focus of earlier researchers on the effect of ITR on supply chain performance. ITR is also an important aspect in inventory management which reflects the performance of a firm and its supply chain. C. Madhusudhana Rao & Dr. K. Prahlada Rao (2009) carried out a case study in batteries manufacturing firm (i.e., ARBL) considering ITR as a supply chain performance measure. The methodology to calculate ITR and analysis of ITR discussed in this chapter is published in Serbian Journal of Management, an International Journal for Theory and Practice of Management Science, Volume: 4 Number: 1, in the year 2009.

21 125 Inventory turnover is best thought of as the number of times that an inventory "turns over" or cycles through the firm in a year. Inventory turnover of 12 means the average inventory moves through the firm once per month. For a number of years top-class companies have been focusing on SCM for improving their competitiveness. They were able to demonstrate their success through improved revenue, profit margins and decreased costs (Peter Bolstorff, 2002). Lean is a great method to help organize work areas, reduce WIP and speedup material flow through the entire manufacturing process. Successful lean initiatives yield lower inventory cost, higher productivity and flexibility and faster response time to the customer. ITR is an important measure of performance that indicates the effective utilization of financial resources of the firm. Inventory for customer use is an expensive investment of company money. Instead of investing in people, technology or other important assets that can potentially assist in growing a business faster, companies who invest in inventory have no Return On Net Assets (RONA) until they sell the inventory. In many businesses, inventory is turning at the lowest levels in history and below industry averages. Studies have shown that manufacturers and wholesalers have over 60 days of inventory and that retailers have over 90 days of inventory

22 126 capital tied up. These times (inventory days) do not include inbound inventory in the supply chain. Real supply chain inventory is 25% higher Various causes for inventory supply chain. The following are the causes for accumulation of inventory in any firm and its (a) (b) (c) (d) (e) (f) (g) Revisions and variance in Supply Chain Management Inputs Inadequate process norms Non moving stocks High lead times & batch quantities Variance in material receipt Variance in consumption with actual versus Bill of Material Design and type changes without valid lead time The inventory turnover formula Inventory turnover is a critical performance metric to assess the effectiveness of inventory management. Because it is so extensively used as a diagnostic tool, it is

23 127 imperative that inventory turnover be calculated using appropriate and valid techniques. Inventory turnover is calculated using the following formula: ITR = Cost of Goods Sold from stock sales during the past 12 months Average inventory investment during the past 12 months (5.9) Alternately, the firms using Manufacturing Resource Planning (MRP - II) software are using the following relations also to calculate ITR. Cost of goods sold for sales in current month (a) ITR = 12 Ending inventory of current month (5.10) Cost of goods sold for sales in current month (b) ITR = (5.11) Ending inventory for previous month When product flow varies throughout the year and inventories expand and contract during different periods, more frequent measures of the inventory level need to be taken to generate an accurate measure of the average inventory. The inventory turnover often is reported as the inventory period, which is the number of days worth of inventory on hand, calculated by dividing the inventory by the average daily cost of goods sold. This metric not only helps a firm as diagnostic measure but also helps to improve C2C cycle time of a firm.

24 128 Average inventory Inventory Period = 365 Annual cost of goods sold (5.12) The following points kept in mind when calculating turnover ratio: 1. Consider only cost of goods sold from stock sales which are filled from warehouse inventory. Non-stock items and direct shipments are not included. 2. The cost of goods sold figure in the formula includes transfers of stocked products to other branches and quantities of these products used for internal purposes such as repairs and assemblies. 3. Inventory turnover is based on the cost of items (what the company paid for them) but not sales dollars (what the company sold them for). Inventory turnover depends on the average value of stocked inventory. To determine average inventory investment, 1. The total value of every product in inventory is to be calculated (quantity onhand times cost) every month, on the same day of the month. One must be consistent in using the same cost basis (average cost, last cost, replacement cost, etc.) in calculating both the cost of goods sold and average inventory investment.

25 If the company s inventory levels tend to fluctuate throughout the month, then it has to calculate the total inventory value on the first and fifteenth of every month. 3. To determine the average inventory value, a firm has to take average of all inventory valuations recorded during the past 12 months. All the above points were considered while collecting data and manipulated as per the requirements of the present research work Turnover goals 1. As a company determines its inventory turnover goals, it should consider the average gross margin it receives on the sale of products. Most distributors who have 20% - 30% gross margins should strive to achieve an overall turnover rate of five to six turns per year. Distributors with lower margins require higher stock turnover. If the company enjoys high gross margins, it can afford to turn its inventory less often.

26 A turnover rate of six turns per year doesn t mean that the stock of every item will turn six times. The stock of popular, fast moving items should turn more often (up to 12 times per year). Slow moving items may turn only once, or not at all. 3. Finally, inventory turnover should be calculated separately for every product line in every warehouse. This will allow a company to identify situations in which its inventory is not providing an adequate return on investment. To improve inventory turnover, a company should consider reducing the quantity it normally buy from the supplier. Inventory turns improve when a company buys less of product, more often. 4. Companies have limited funds available to invest in inventory. They cannot stock a lifetime supply of every item. In order to generate the cash necessary to pay their bills and return a profit, one must sell the materials they have bought. The inventory turnover rate measures how quickly the firm is moving inventory through its warehouse. Combined with other measurements such as customer service level and return on investment, inventory turnover can provide an accurate barometer of a company s success.

27 ANALYSIS OF INVENTORY TURNOVER RATIO As this performance metric reflects not only the internal operational performance but also affects the working capital management of a firm, this is more important performance measure. The companies can benchmark ITR by computing current ITR value and comparing with that of the companies in the same industry sector. If benchmark values are not available, a firm can set benchmark levels by itself basing on earlier performance. As the inventory days depends on ITR, larger the value of ITR, smaller will be the inventory days and in turn shorter the C2C cycle time ITR calculations of IBD In the present research work, the ITR of IBD has been analyzed. After finding the gaps in performance (in terms of ITR), a set of alternatives have been identified to increase ITR. The courses of action adapted for implementation are furnished below: (a) Revision of stocking policy of A - class materials so as to maintain stocks for 15 to 20 days of consumption. (b) Revision of ordering policy for B & C - class items as per lead time and Economic Order Quantity (EOQ) of purchase department.

28 132 (c) MRP computation as per months production plan. (d) To reduce the forecast variance of marketing (Market Research Information). (e) Information on design and type changes with valid lead time and clear action to dilute existing stocks. Implementation of these plans has improved the ITR of industrial battery division tremendously in the past five years. The data required to calculate ITR is obtained from sales data and inventory levels of raw materials, work in process and finished goods including those in transit and available at ware houses / market outlets. As a part of continuous improvement program, by closely monitoring the ITR, a firm can continuously improve its capability to rotate money as many times as possible in a year. Specimen calculation of ITR and inventory days for the month May 2004 is given below. The ITR trend of IBD in the past five financial years calculated using equation (5.11) is furnished in the tables 5.12 to Specimen Calculation: The Net Sales in the month May 2004 = 125 Million Rupees Ending inventory of the month April 2004 = Million Rupees 125 Inventory Turnover Ratio (ITR) in May = = 4.8

29 133 Inventory Days for the month May = Days ITR = = days Similarly, the ITR and Inventory Days of Supply (IDS) have been calculated for each month from May 2004 to March The values furnished in tables: 5.11 to 5.15 indicate improved ITR with corresponding decrease in IDS (calculated using equation 5.12) during that period.

30 134 Table 5.12 Inventory Turnover Ratio of IBD in the year: Month Total Inventory (million rupees) Net sales (million rupees) Monthly Sales (million rupees) ITR Inventory Days Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar

31 135 Table 5.13 Inventory Turnover Ratio of IBD in the year: Month Total Inventory (million rupees) Net sales (million rupees) Monthly Sales (million rupees) ITR Inventory Days Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar

32 136 Table 5.14 Inventory Turnover Ratio of IBD in the year: Month Total Inventory (million rupees) Net sales (million rupees) Monthly Sales (million rupees) ITR Inventory Days Apr 06 May 06 Jun 06 Jul 06 Aug 06 Sep 06 Oct 06 Nov 06 Dec 06 Jan 07 Feb 07 Mar

33 137 Table 5.15 Inventory Turnover Ratio of IBD in the year: Month Total Inventory (million rupees) Net sales (million rupees) Monthly Sales (million rupees) ITR Inventory Days Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar

34 138 Table 5.16 Inventory Turnover Ratio of IBD in the year: Month Total Inventory (million rupees) Net sales (million rupees) Monthly Sales (million rupees) ITR Inventory Days Apr May Jun Jul Aug Sep Oct 08 Nov Dec Jan Feb Mar

35 139 The ITR trend in IBD is consolidated in table The graph plotted to present the trend in ITR on annual average performance in the past five financial years is shown in figure 5.2. The graph indicates upward trend with significant improvement in mean ITR in this division. Table 5.17 ITR trends in IBD of ARBL (FY: to ) Year Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Figure 5.2 ITR trends in IBD of ARBL

36 140 Table 5.18 ITR and Inventory days of IBD and the Whole Company Financial Year Industrial Batteries Division Consolidated (All divisions) Mean ITR Inventory days ITR Inventory days The graphs in figure 5.3 clearly indicate that the overall performance of the firm in terms of ITR is always inferior to the performance of IBD. Measures have been now taken to improve the ITR in other divisions also to improve the overall ITR of the firm. Figure 5.3 Comparison of ITR of IBD with that of ARBL group

37 141 The results of analysis indicate clearly that the benefit of improving ITR or decreasing inventory days is two fold: Firstly, the working capital management will be more effective. Secondly, the C2C cycle time will be improved which leads to lesser penalty to all parties along the supply chain and better buyer-supplier relationships along the value chain. 5.7 SUMMARY In this chapter, the attempt is to develop LPP model to find optimal payment and collection periods that would minimize penalty to all the entities i.e., suppliers, firm and customers. The data collected from financial reports of ARBL is analyzed and optimal payment and collection periods associated with minimal penalty have been found in each case. The results of analysis strongly support that the penalty will be less when payment period and diferral periods are short which in turn will lead to shorter C2C cycle time. Also, this analysis helps firms in benchmarking payment and collection periods at supplier and customer ends for the benefit of all the firms along the supply chain in an integrated approach. The analysis of ITR also strongly support that improved ITR will help firms in optimizing their inventory management and at the same time, the small the inventory days shorter will be the C2C cycle time. Hence, it is essential for firms in an integrated supply chain management to focus on improved ITR through inventory visibility and collaborative planning forecasting and replenishment that helps all the firms along the supply chain.