Measuring Efficiency of a Supply Chain pdf

Measuring Efficiency of a Supply Chain -I by Prabir Jana, Prof. A.S. Narag and Dr. Alistair Knox December 2007 The two part article is based on doctoral research by Prabir Jana at Nottingham Trent University, UK “An Investigation into Indian Apparel and Textile Supply Chain Networks.” In first part we will discuss about efficiency measurement framework in Apparel Supply Chain and in second part we will discuss a case study of practically measuring supply chain efficiency of an apparel manufacturing organization and associated complications and nuances. Introduction: What can’t be measured can’t be improved. Even though Supply Chain Management is the most talked about topic today, currently no tool is available to measure any manufacturing organizations’ supply chain efficiency. Unlike productivity and or quality measurement, where the parameter can be measured objectively and expressed in unit or ratio, supply chain measurement is currently more of a qualitative statement. Even though the word ‘performance’ or ‘efficiency’ is often used communicating the same meaning, measuring the performance or efficiency of an ‘enterprise’ or a ‘supply chain’ conveys different meaning altogether. Challenges of Measuring Efficiency of an Apparel Supply Chain If we define ‘supply chain’ as an extended enterprise then efficiency measurement of a supply chain will mean efficiency measurement of multiple organizations in synchronization. One of the major strategic objectives of supply chain planning and management is to maximize total profit in the chain rather than maximizing profit of an organization in isolation. The typical adversarial relationship between upstream and downstream players in the apparel supply chain is still prevalent making the job more difficult than saying. Can you imagine if the buying organization you are dealing with, is sharing the profit with you or you have to share your profit and loss with your fabric supplier! Can you blindly trust your fabric supplier that the fabric developed for you will not be shown to another apparel manufacturer? Information that potentially influence the bottomline of an organization is kept so confidential, no trust, or partnership can penetrate that. It is not impossible, but difficult and not yet common in marketplace. What Are The Measurement Systems Available? A variety of measurement approaches that have been developed and traditionally used for measuring supply chain performance (Lapide 1999). Apart from the wildly popularized Balanced Scorecard, there are other measurement approaches like Supply Chain 1 Council’s SCOR Model, the Logistics Scoreboard, Activity-Based Costing (ABC) and Economic Value Analysis (EVA). Balanced Scorecards Balanced Scorecard (BSC) was developed by Robert S. Kaplan and David P. Norton in 1992 (Kaplan et el 1992). BSC recommends use of executive information systems (EIS) that track a limited number of balanced metrics based on the following four perspectives: financial, customer, internal process, and learning and growth, which are closely aligned to strategic objectives. Financial perspective (e.g., cost of manufacturing and cost of warehousing) Customer perspective (e.g., on-time delivery and order fill rate) Internal business perspective (e.g., manufacturing adherence-to-plan and forecast errors) Innovative and learning perspective (e.g., APICS-certified employees and new product development cycle time) While BSC is popular among several industry segments and considered most balanced measurement of possible parameters, application of BSC in contract apparel manufacturing is not suitable because organizations are secretive about financial data, customer perspective is out of bound and innovative and learning perspective is virtually missing in majority. That leaves out only internal business perspective. The Supply Chain Council’s SCOR Model The Supply Chain Council (SCC) was set up between 1996 and 1997, with members representing most industries and global geographies, including BASF, Bayer, Colgate- Palmolive, Lucent technologies, Procter & Gamble, Unilever and Siemens, as well as consulting organisations. The SCC designed SCOR model, which is designed and maintained to support supply chains of various complexities and across multiple industries. It spans all customer interactions (order entry through paid invoice), all physical material transactions (supplier’s supplier to customer’s customer, including equipment, supplies, spare parts, bulk product and software) and all market transactions (from understanding of aggregate demand to the fulfillment of each order). This model is finally adopted to develop the measurement framework, and will be discussed in detail in part II of this article. The Logistics Scoreboard Another approach to measure supply chain performance was developed around logistical measures like Logistics financial performance measures (e.g., expenses and return on assets) Logistics productivity measures (e.g., orders shipped per hour and transport container utilization) Logistics quality measures (e.g., inventory accuracy and shipment damage ) Logistics cycle time measures (e.g., in transit time and order entry time) This method was developed by Logistics Resources International Inc. (Atlanta, GA), a consulting firm specializing primarily in the logistical (i.e., warehousing and 2 transportation) aspects of a supply chain. The company sells a spreadsheet-based, educational tool called The Logistics Scoreboard that companies can use to pilot their supply chain performance measurement processes. The Logistics Scoreboard is prescriptive and actually recommends the use of a specific set of supply chain performance measures. These measures, however, are skewed toward logistics, having limited focus on measuring the production and procurement activities within a supply chain. This approach is more suitable for logistics service providers and none of the measures are in direct relevance to contract manufacturing Activity Based Costing Activity based costing (ABC) is an accounting methodology that assigns costs to activities rather than products or services. This was developed to overcome some of the shortcomings of traditional accounting methods in tying financial measures to operational performance. The method involves breaking down activities into individual tasks or cost drivers, while estimating the resources (i.e., time and costs) needed for each one. Costs are then allocated based on these cost drivers rather than on traditional cost-accounting methods, such as allocating overhead either equally or based on less-relevant cost drivers. This approach allows one to better assess the true productivity and costs of a supply chain process. From operational perspective ABC method highlights benefits through lower cost, improve quality and reduced manufacturing cycle time (Agarwal and Manjul 2005). For example, use of the ABC method can allow companies to more accurately assess the total cost of servicing a specific customer or the cost of marketing a specific product. ABC analysis does not replace traditional financial accounting, but rather a post mortem on past orders that provides a better understanding of supply chain performance by looking at the same numbers in a different way and helps better aligning the metrics closer to actual labor, material, and equipment usage. This method can be used for post mortem of cost incurred on different orders that are executed. A case study of a garment manufacturer exporter (Agarwal and Manjul 2005) shows that cost calculated using ABC analysis was 27% to 31% higher compared to cost estimated traditionally using absorption costing. While labour cost is the highest component across all departments namely, sewing, cutting and sampling, it is as high as 90% in sewing and 50-53% in sampling. As this method does not measure any other parameters related to time, quality and output oriented functions, so it is not a holistic approach to supply chain performance measure. Economic Value Analysis One of the criticisms of traditional accounting is that it focuses on short-term financial results like profits and revenues, providing little insight into the success of an enterprise towards generating long term value to its shareholders – thus, relatively unrelated to the long-term prosperity of a company. For example, a company can report many profitable quarters, while simultaneously disenfranchising its customer base by not applying adequate resources towards product quality or new product innovation. To correct this deficiency in traditional methods, some financial analysts advocate estimating a 3 company’s return on capital or economic value-added. These are based on the premise that shareholder value is increased when a company earns more than its cost of capital. One such measure, EVA, developed by Stern, Stewart & Co., attempts to quantify value created by an enterprise, basing it on operating profits in excess of capital employed (through debt and equity financing). These types of metrics can be used to measure an enterprise’s value added contributions within a supply chain. However, while useful for assessing higher level executive contributions and long term shareholder value, economic-value added metrics are less useful for measuring detailed supply chain performance. They can be used, however, as the supply chain metrics within an executive-level performance scorecard, and can be included in the measures recommended as part of The Logistics Scoreboard approach. This measurement method is long term financial health oriented. While majority of the manufacturing organizations are self financed and balance sheets are not public, Economic Value Analysis is not possible for such organizations. What measurement approach is right for apparel manufacturers? In a platter full of so many options it is obviously difficult for apparel manufacturers to select the right approach. While listing a comprehensive list of supply chain measures Lapide noted (lapide 2000) that most performance measurement systems are functionally focused. For example SCOR model is a typical function based supply chain performance measure, often lead to functional silos and conflicting functional goals. A balanced supply chain measurement system should cover function based, process based, cross enterprise and alignment of executives to management level measures. Measuring performance in a department as though it operates in a vacuum can have a negative effect on other departments—and on the bottom line (Barnard 2000). We have first highlighted the measurement parameters in the following table from a clothing manufacturer’s perspective. While almost all manufacturing related measures are theoretically measurable by a manufacturer, only selected measures are possible in customer service, logistics and sales related parameters. It is of pertinent importance to understand the secrecy and confidentiality issues perceived by every typical manufacturer working as CMT supplier or fully-factored clothing supplier to any high street retailer in EU or US. An organization of $ 25 million turnover is typically self financed and the operational efficiency horizon for such manufacturer spans between order receipts till goods trucked out of factory. The objective was to develop easy and simple metrics to measure such organization’s supply chain efficiency. After a thorough investigation of all measures SCORE model was selected for final adaptation. Last, but not the least the measurement parameters are chosen based on the functional link between upstream and down stream players in the supply chain and not merely in house functions of an apparel manufacturer. Table: Lists of Possible Supply Chain Measures 4 Customer Service Measures Process, Cross-Functional Measures Purchasing Related Measures Order Fill Rate Line Item Fill Rate Quantity Fill Rate Backorders/stockouts Customer satisfaction % Resolution on first customer call Customer returns Order track and trace performance Customer disputes Order entry accuracy Order entry times Forecast accuracy Percent perfect orders New product time-to-market New product time-to-first make Planning process cycle time Schedule changes Material inventories Supplier delivery performance Material/component quality Material stockouts Unit purchase costs Material acquisition costs Expediting activities Extended Enterprise Measures Manufacturing Related Measures Logistic Related Measures Total landed cost Point of consumption product availability Total supply chain inventory Retail shelf display Channel inventories EDI transactions Percent of demand/supply on VMI/CRP Percent of customers sharing forecasts Percent of suppliers getting shared forecast Supplier inventories Internet activity to suppliers/customers Percent automated tendering Product quality WIP inventories Adherence-to-schedule Yields Cost per unit produced Setups/Changeovers Setup/Changeover costs Unplanned stockroom issues Bill-of-materials accuracy Routing accuracy Plant space utilization Line breakdowns Plant utilization Warranty costs Source-to-make cycle time Percent scrap/rework Material usage variance Overtime usage Production cycle time Manufacturing productivity Master schedule stability Finished goods inventory turns Finished goods inventory days of supply On-time delivery Lines picked/hour Damaged shipments Inventory accuracy Pick accuracy Logistics cost Shipment accuracy On-time shipment Delivery times Warehouse space utilization End-of-life inventory Obsolete inventory Inventory shrinkage Cost of carrying inventory Documentation accuracy Transportation costs Warehousing costs Container utilization Truck cube utilization In-transit inventories 5 Premium freight charges Warehouse receipts Administration/Financial Measures Marketing Related Measures Other Measures Cash flow Income Revenues Return on capital employed Cash-to-cash cycle time Return on investment Revenue per employee Invoice errors Return on assets Market share Percent of sales from new products Time-to-market Percent of products representing 80% of sales Repeat versus new customer sales APICS trained personnel Patents awarded Employee turnover Number of employee suggestions Source: Lapide 1999 Developing efficiency measurement framework in Apparel Supply Chain Supply chain efficiency measurement framework is developed in terms of efficiency shown by the chain with respect to key functional parameters spanning four different operation domains namely source, plan, make and deliver. There are about five primary key performance indicators (KPI) identified in each operation domain and some primary KPI have multiple secondary KPIs to measure. Each KPI is expressed in percentage. Once all KPI are measured, weighted averages of all KPI would indicate the overall supply chain efficiency of the organization. While a 100 percent supply chain efficiency index would mean perfect organization, there is a possibility of any organization having KPI value more than 100 percent. Operation domain KPI’s Source 1) Inward Material Quality 2) Quantity and Timely Delivery 3) Procurement Unit Cost 4) Material Inventory Level 6 5) Vendor Development Capability Plan 1) Adherence to Production Target 2) Sample Conversion Rate 3) Material Utilization 4) Cost Adherence 5) Planned T&A v/s Actual T&A Make 1) Capacity Utilization 2) Production Cost Efficiency 3) Quality Capability 4) Change Over Time 5) Operator Training Effectiveness Deliver 1) On Time Shipment 2) Order Fulfillment 3) Claims and Discounts 4) Quality at Delivery 5) Transit time Conclusion 7 It is obvious from above parameters that all KPI neither have equal weight in final measurement nor all KPI are equally important for all organizations. Organizations can decide priorities and weight at their will to finally arrive at the supply chain efficiency of an organization as a whole. In next part we will discuss how the above measurement parameters were used in a pilot case study. Measuring Efficiency of a Supply Chain -II by Sharad Diwan, Prabir Jana, Prof. A.S. Narag and Dr. Alistair Knox December 2007 The two part article is based on doctoral research by Prabir Jana at Nottingham Trent University, UK “An Investigation into Indian Apparel and Textile Supply Chain Networks.” In first part we have discussed about need and development of efficiency measurement framework in Apparel Supply Chain and in this second part we will discuss how to calculate each KPI and a case study of practically measuring supply chain efficiency of an apparel manufacturing organization and associated complications and nuances. Introduction: In part I we have discussed about the genesis and development of a measurement framework for measuring efficiency of apparel supply chains. Here first we will define and discuss how to calculate each KPI and then we will discuss a case study where we have tried to measure the supply chain performance of ABC Enterprise. ABC enterprise is a $ 15 million enterprise from Northern Capital Region (NCR), India having an ERP system running. Source Under this domain in the supply chain we shall consider the sourcing of raw material and consumables for manufacturing of the garment i.e. fabric, trims and accessories and packing material. All the parameters will be considered under three different heads: fabric, trims and accessories and packaging material. [1] Inward Material Quality: This parameter shall evaluate the adherence of quality standards of material received from vendors to that specified i.e. deviation from the quality levels agreed between the supplier and the company. Also the material quantity accepted may be equal to the 8 ordered quantity or less. If a lesser quantity is supplied then the penalty will be applied in the vendor lead times. But if the material received is of required quantity but of inferior quality then good quality material is accepted after screening. Also if there is some discrepancy in the quantity stated and actual it will be penalized as case three in this KPI. There can be three cases: 1)Quality of material supplied is as per desired standards and 100% material is accepted. KPI is 100 2)Quality of material supplied is not as per desired standards and 100% material is rejected. KPI is 0 3)Quality of material supplied is not as per desired standards and material is accepted fully or partially. KPI is calculated as under : 99-75% accepted -70 points : 74-50% accepted -50 points : 49-25% accepted -30 points Less than 25% accepted -10 point Quality parameters shall be considered as a whole for a product and not individual parameters like fastness, weaving defect etc. However if the company has no quality policy for sourcing, this KPI shall not be applicable. [2] Quantity and Timely Delivery: This parameter shall evaluate whether the quantity ordered is delivered on time or not for all the materials mentioned earlier. The time to be considered will be a percentage of the lead time of the raw material. However in case of late delivery the penalty shall be according to the % lead time delay and quantity supplied as per matrix below. A percentage of the lead time is being taken as different materials have lead times varying from one to sixty days. Only the quantity and time are considered as quality has been covered in earlier parameter. The points can be allotted as: Qty. Rcd. Time delay 100% 99-75% 74-50% 49-25% >25% 0 % 100 70 50 30 0 Upto 20 % 70 50 30 10 0 21-50 % 50 30 20 0 0 9 >50 % 0 0 0 0 0 < 20% early 30 40 50 70 80 Moreover if the material was ordered in bulk to be delivered in lots, then the quantity will be taken as cumulative. Higher the KPI, better the efficiency. [3] Procurement Unit Cost This parameter shall evaluate the cost incurred to procure the material i.e. the various costs such as correspondence (e-mail, fax, courier, telephone etc.), conveyance (transportation cost of personnel involved in procurement), official’s salary, electricity bills, etc. This can be measured as a ratio between the procurement costs per material to the cost of the material. The transportation costs of material will also be included in the material costs and the material costs would thus be costs of material at site. Also costs incurred in testing of raw material will be added in material cost. (Total procurement cost / total cost of material procured) x 100 = procurement unit cost. KPI is expressed as 100 – procurement unit cost. Higher the KPI better the procurement efficiency. Data is collected over minimum 6 representative months and averaged. It may be noted that procurement cost incurred in the month of March may arrive at warehouse during April, so data collected for more number of months will give correct measure of this KPI. [4] Material Inventory Level This parameter shall evaluate the stocked inventory level of the company. Higher inventory level increases the capital investment and also acquires more physical space. Lower inventory level indicates better sourcing efficiency. The inventory level can be measured as a ratio of daily requirements in volume terms upon average daily inventory stock expressed in percentage. • Issued stock per day = (Monthly Closing stock- Monthly Opening Stock + Total Received Stock)/ working days • Stock Held per day = Average daily Opening Stock of the month This KPI is calculated as Inventory Level Stock Ratio i.e. (Issued Stock per day) / (Stock Held per day) expressed as percentage. Higher the KPI better the performance. Data is collected over minimum 6 representative months and averaged. [5] Vendor Development Capability This parameter will determine the Sourcing Department’s potential and capability to assist vendor during the product development or in Order processing. Three types of the parameters which need to be checked during the product development are Technological Assistance, Financial Assistance and Timeliness of information and the Extent of 10 [...]... planning balances demand and supply, internal and external objectives, all in a constantly changing environment Mastering supply chain planning can provide a major competitive advantage [6] Adherence to Production Target Many times the planned targets are not met due to non availability of raw-material (as raw-material did not arrive on time) or due to decision pending (like fit-approval delays, material... about what should be the weight of different KPI in calculating overall supply chain performance of an organisation, an informal survey was undertaken among industry experts to weight different KPI on a scale of 1-10 based on their importance Of total 15 responses average weight was calculated and listed against each KPI Overall supply chain performance for organisation ABC (Weightage average for 12... available to share Capability Plan Adherence to Production Target 90.05 6.13 Only sewing production measured Sample Conversion Rate 5.6 Data not available to measure Material Utilization 8.8 Data not available to measure Cost Adherence Planned T &A v/s Actual T &A Data not available to measure 70.86 5.73 47.78 Measured based on planned cut date schedule 8.13 Make Capacity Utilization Production Cost Efficiency. .. other organizations, sea-shipment should not be compared against air-shipment Measuring efficiency of ABC Enterprise The pilot study was undertaken by Sharad Diwan (Diwan 2006) as part of his masters thesis under the guidance of the researcher The objective was to test the measurement framework in practical environment, measurability of each KPI, and data availability and confidentiality issue in measuring. .. planning department and raise a bill of material After the material is arrived and consumed its utilization record need to be compiled to determine accuracy of planning (the quantity parameter) Where material had arrived of right quantity at right time, its actual utilization percentage is calculated over a period of 3 months 100% utilization gets the highest rating and so on You can cover as many raw... measurement framework was applied to three manufacturing organizations In organisation ABC we were able to measure a total of 13 parameters Only 7 and 6 parameters were measurable in two other organizations respectively Poor and inconsistent record keeping, and confidentiality of information was the main reason behind not being able to measure all KPI’s In absence of any international benchmark about... like material consumption, labour cost, overhead cost etc and apportioning value against each parameter Due to unforeseen and unavoidable circumstances actual cost incurred on a order may vary from the planned one This KPI is the ratio of planned cost upon the actual cost incurred expressed in percentage Data is collected over a minimum 3 representative months and averaged [10] Planned T & A Vs actual... to hover around 200% It is also important to note that any organisation may be strong in one area, but weak in other area For example, organisation A was found to be strong in plan and deliver with average KPIs of 81 and 95 respectively The weak area was source and make with average KPIs of 62 and 44 respectively Conclusion While the summarized table shows the utility of these efficiency measurement... models, it is important to note that out of 20 KPI only 12 we were able to be measured The data secrecy, data ownership, poor record maintenance, and unauthentic data were the main reasons behind not all KPI being measurable It was also realized that computerized data record maintenance is more reliable and better to retrieve than any manual method of maintaining registers and files This measurement process... was 69.77 Supply Chain Efficiency Measurement KPI KPI value Weightage Remark, if any Source Inward Material Quality 50 8.53 Even in accepted material there are a. b.c.d grades, which are not accounted for Quantity and Timely Delivery 70 8.13 Data not available for delay in delivery, if any Procurement Unit Cost 83.5 7.87 Material Inventory Level 42.10 6.47 Vendor Development 6.67 16 Data not available . discuss a case study of practically measuring supply chain efficiency of an apparel manufacturing organization and associated complications and nuances. Introduction: What can’t be measured can’t. Measuring Efficiency of a Supply Chain -I by Prabir Jana, Prof. A. S. Narag and Dr. Alistair Knox December 2007 The two part article is based on doctoral research by Prabir Jana at Nottingham. Efficiency of a Supply Chain -II by Sharad Diwan, Prabir Jana, Prof. A. S. Narag and Dr. Alistair Knox December 2007 The two part article is based on doctoral research by Prabir Jana at Nottingham Trent