SUPPLY CHAIN PERFORMANCE EVALUATION THROUGH EVA IN HOSPITALITY

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1 SUPPLY CHAIN PERFORMANCE EVALUATION THROUGH EVA IN HOSPITALITY Yasser Tawfik Halim a (Ph.D), Mohamed Samy El-Deeb b (Ph.D), Emad Elwy Habib c (Ph.D), and Mohga Ahmed Bassim d (Ph.D) a Modern Sciences and Arts University, Egypt, yassertawfik@hotmail.com b Modern Sciences and Arts University, Egypt, moh.eldeeb2@hotmail.com c Modern Sciences and Arts University, Egypt, emad_elwy@hotmail.com d Modern Sciences and Arts University, Egypt, mohgabassim@hotmail.com ABSTRACT Hospitality industry operates their businesses in an increasingly changeable and unpredictable environment, where competition assumes a global scale; that requires the generation of new ideas, tools and methods. In this paper we propose a model to help hotels measure and evaluate the performance of their supply chains components. Applying both tangible and intangible assets measurements and also measuring supply chain components performance internally and externally. The proposed model develops an enhancement for maximizing shareholders value creation through measuring and evaluating the supply chain components performance, targeting customers satisfaction. RESEARCH LIMITATIONS The results of the research based on limited survey that have been distributed to number of hotels in great Cairo region. Our research was targeting two hundred and fifty through five star hotels functional managers. It is recognized that further research is necessary to establish the exact nature of the causal linkages between performance measures and strategic intent in order to gain insights into practice elsewhere. KEY WORDS: Hospitality-Supply Chain-EVA-Performance measurements RESEARCH PROBLEM Supply chain activities play a prominent role through rendering various products and services to the targeted customers in order to achieve customers and shareholders satisfaction, especially in the services sector. Sector focuses on intangible products that have difficulties in delivering consistent standardized products. The research problem is to evaluate and improve the performance of supply chain components in hospitality industry through the economic value added measures (EVA) for maximizing shareholders value.

2 OBJECTIVES This study seeks to evaluate the supply chain components performance through EVA measurements especially from the viewpoint of business unit controlling of hospitality industry. To assess the implication of EVA measures on hospitality organization s performance. To improve the performance of the supply chain components through the EVA measurements. To enhance the performance of hotels through applying the proposed model. The study recommends ways of using EVA as a management tool bringing together the theoretical and practical issues. HYPOTHESES EVA measures improve hospitality organization s performance. The use of the EVA measurements improves the supply chain performance. The proposed model enhances the performance of the hotels. INTRODUCTION Managing supply chain effectively is a critical issue of successful business in all industries, especially in hospitality industry. Nowadays, it is emphasized by managers that the functional capabilities such as product development, manufacturing and marketing are necessary but no longer lead to competitive advantages. On the contrary, their replication in time can dissatisfy customers and shareholders. That s why managers believe that the integrated supply chain management becomes a mean of competitive advantages achievement. A supply chain management considers all facilities, functions, and activities that are involved in producing and delivering product or service in an organization. Every business organization is part of at least one supply chain, and many organizations are part of multiple supply chains and it is necessary to maximize the value for hotel chains. Performance Measurement in Supply Chain According to Chan (2003), performance measurement is the information or feedback on activities correlated with meeting clients expectation and strategic objectives. It indicates the need for improvement in areas that

3 shows unsatisfactory performance. Consequently, efficiency and quality can be enhanced & improved. Moreover, it is known by firms that their supply chain performance upstream directly influences in-stream performance, and vice versa that s why aligning internal operations decisions with supply chain objectives is extremely important. Moreover, firms can t work alone and must rely on other firms to perform an integrated complex chain of interdependent activities from source-ofsupply to the end-user achieving a competitive market-place advantage (Cook, DeBree, and Feroleto, 2001). There is a great need to develop measures for meeting down-stream requirements aligning firm performance with supply chain objectives. Developing such measures is of big challenge to supply chain management (Pohlen, 2003), (Hofmann, E.Locker, A.2009). Performance has to be measured simultaneously across streams, in which the measures must illustrate how each stream performance affects others and value delivered to the end-user. Moreover, supply chain performance measures must transform nonfinancial performance into financial terms that enhance shareholders value (Ellram and Liu, 2002). Supply chain management will affect more than cost. Managers have to sell value created to senior executives, trading partners, and shareholders. Although most managers are aware of the importance of designing metrics and rewards, they are not familiar with the framework for developing suitable performance measures (Kallio, 2000, Simatupang and Sridharan, 2002). Economic Value Added (EVA) The financial theory proposes that every company s ultimate target is to maximize its shareholders wealth. This is normal since shareholders are the company owners' and as potential investors expecting good long-term yield on their investment. Matrices like Return on Investment and Earnings per share are most commonly used as the most known performance measures in a lot of companies, although they do not theoretically integrate with the shareholder value creation very well. That s why the so-called value based measures have received a lot of attention in the recent years (Esa Mäkeläinen, 1998), (Ralph Palliam, 2006). These new performance matrices seek to measure the periodic performance in terms of change in value. Maximizing value has the same meaning of maximizing long-term yield on shareholders investment. Currently the most popular Value based measure is Economic Value Added, EVA. (Esa Mäkeläinen, 1998)

4 It is suggested by Stewart (1991) that EVA is considered a fundamental measure of corporate performance that emphasizes how efficiently management is transforming investors capital into profits, i.e., creating wealth. Moreover, O Byrne (1996) made a detailed analysis on industrial companies and observed that EVA is superior to earnings in explaining firm value. Adapting EVA to the Hospitality Industry The economic and business environments have changed tremendously during the past years. The U.S. hospitality industry has experienced both the terrorist attacks of September 11, 2001, and the recession in the economy (France, 2003). Hospitality firms top executives face increasingly competitive markets and complex environments. Therefore, common accounting performance measures have become insufficient in light of the new realities (Abdeen & Haight, 2002). Consequently, given the changes in hospitality industry, financial managers need to be fully aware of more powerful and sophisticated corporate performance measures (Dimitrios, et al, 2009). Because of the hospitality industry sector characteristics, that includes several units of businesses in charge of compiling and delivering product to end user. Therefore, it is not always possible to be successful by only considering the performance of individual operations. A well-developed Metric of Measuring System (EVA) can be a source of superior and sustainable performance development. (Woo Gon Kim, 2006) This research explores the development of a value-based performance measurement model that shows a direct link between supply chain components and shareholders value creation presented in the economic value added (EVA). The research offers a performance measurement model that matches the supply chain components through key performance indicators. These performance indicators are obtained through analysing certain value drivers of the ultimate measurement of the hotel value generation.

5 Table 1: Supply Chain Indicators Components and EVA Measures Model SC Indicators 1.Purchasing Goal : Maximizing the value from purchasing functions a. Suppliers Performance Supplier delivery performance, Material/co mponent quality, and Material stock outs Components Purchasing Components Measures b. Supplier relationship management c. Cost/Unit Purchased Increase Price higher quality, Sales volume (improved order fill rates), and productivity. Improve mix services costs, manufacturing processes plant productivity, Align performance across processes and between firms, asset utilization and rationalization, investment planning and deployment. Reduce purchase work in process inventories finished inventories Reduce direct material, Goods Sold, staffing, freight, indirect,, general overhead, administrative, information system, labor/warehousing, customer service & order management human resources costs, Optimize physical network/facilities, Supplier Relationship Management s Impact, and leverage new and/or alternative distribution channels 2. Manufacturing Goal: Optimizing plant utilization through the efficient conversion of orders into products. Manufacturing Components Measures a. Plant Utilization b. Set-ups and changeover times Unplanned stockroom issues, Plant and space, utilization, and Source-to-make cycle time Product quality, WIP inventories, Adherenceto-schedule, Yields, Cost per unit produced and Setups/Changeovers,Setup/Changeover, Bill-of-materials accuracy, Unplanned stockroom issues Routing accuracy, Plant space utilization Source-to-make cycle time, Overtime usage, Production cycle time, Manufacturing productivity, Master schedule stability. c. Wastes and Scrap Forecast accuracy, Percent perfect orders, New product time-to-market New product time-tofirst make, Planning process cycle time, and Schedule changes d. Process, Cross- Functional Measures Percent scrap/ rework, and Material usage variance

6 3. Logistics : Goal :efficient synchronization of transportation, storage, funds and information 4. Customer Services/Sales Goal: Increase customers' loyalty. 5. Value Added Goal : Maximizing value added of Supply chain components Logistics Components Measures a. Inventory b. Transportation Costs c. Warehouse Productivity End-of-life, Obsolete, Shrinkage, and Cost of carrying inventory On-time delivery, Lines picked/hour, Damaged shipments, Pick accuracy, Shipment accuracy, On-time shipment, Delivery times, Transportation costs, Container utilization, Truck cube utilization, Premium freight charges Finished goods inventory turns, days of supply, Warehouse and space utilization, End-of-life shrinkage inventory, Warehousing costs, and Warehouse receipts a. Customer Satisfaction Order, line Item and Quality Fill Rate, Backorders /stock-outs, Customer satisfaction Customer Services/Sales Components Measures b. Marketing c. Customer Relationship management Marketing Related Measures, Market share, Percent of sales from new products, Time-tomarket, %of products representing 80% of sales, and Repeat versus new customer sales Retain and strengthen relationships with profitable customers, Increase sales volume, Sell higher margin products, Reduce safety stock, Make to order, mass customization of inventories, Reduce accounts receivable through faster payment, Improve share of customer, mix of services and cost, plant productivity, demand planning, Improve asset utilization and rationalization, product development planning, deployment,and asset investment. Value Added Components Measures d. Customer Order Cycle Time % Resolution on first customer call, Customer returns, disputes, Order track and trace performance Order entry accuracy, and times a. Net Income after Tax b. Shareholders Equity c. EVA d. Cost of Capital RETURN: Annualized compound rate of return to shareholders GROWTH: Average annual compound rate of capital growth, ROC: Average return on capital. EVAPS: Average EVA per share, STDEVA: Change of standardized EVA, SPREAD: Average spread between ROC and the cost of capital. WACC: weighted average cost of capital

7 EMPERICAL STUDY The questionnaire developed in this paper was intended to construct a model of current practices of five sectors: purchasing, manufacturing, logistics, customer service and economic value added; that examined the research objectives through five indicators, where each indicator represents one of the questionnaire sectors. The Statistical Package for Social Sciences (SPSS 17) and AMOS 18 have been used. Reliability Test: Cronbach s Alpha showed that the values for the five indicators namely Purchasing, Manufacturing, Logistic, Customer Service and Economic Value Added Indicators are 0.818, 0.891, 0.83, and respectively, which are acceptable as Cronbach's test suggested that the percentage should not be less than The value of 0.6 is considered also acceptable in social sciences. Confirmatory Factor Analysis (CFA): The confirmatory factor analysis is conducted to test how well the measured variables represent the constructs. Table (2) shows the variables and the four constructs used in the analysis which were initially considered to express the EVA. The construct validity is the extent to which a set of measured items actually measures the construct. This has been computed in the (CFA). Variables shown in table (2) were found to be valid, which confirm the acceptance of the first hypothesis. Table 2: Confirmatory Factor Analysis (CFA) Latent Constructs Purchasing (P) Manufacturing (M) Logistics (L) Customer Services (C) Economic Value Added (EVA) Variables 1. Align performance across processes and between firms. 2. General overhead / management / administrative costs. 3. Purchase goods inventories. 4. Asset utilization and rationalization (warehousing & plant). 1. Production cycle time. 2. Master schedule stability. 3. Unplanned stockroom issues. 4. Plant space utilization. 5. Plant utilization. 6. Source-to-make cycle time. 1. Obsolete inventory. 2. On-time delivery. 3. Shipment accuracy. 7. Forecast accuracy. 8. Percent of Completed orders. 9. Planning process cycle time. 10. Schedule changes. 11. Percent scrap/rework. 12. Material usage variance. 4. On-time shipment. 5. Delivery times. 1. Sales volume. 3. Market share. 2. Demand planning. 1. RETURN: Annualized compound rate of return to shareholders. 2. GROWTH: Average annual compound rate of capital growth. 3. ROC: Average return on capital. 4. EVAPS: Average EVA per share. 5. STDEVA: Change of standardized EVA. 6. SPREAD: Average spread between ROC and the cost of capital.

8 Figure 1 is a path diagram of the measurement model. In this diagram, P represents the latent construct purchase, M represents the latent construct manufacturing, L Figure 1 Path Diagram represents the latent construct of logistics, C represents the latent construct of customer service, and V represents the economic value added. Each of the measured variables is represented by a square. From the proposed model the measured variables in table (2) are the only variables that showed significant relationship with the latent constructs. Other measured variables were excluded. The E represents the errors. According to Confirmatory Factor Analysis results the researchers accepted the second hypothesis. Table 3 the Confirmatory Factor Analysis used to measure the validity of the model. All standardized regression weights (factor loading) are greater than 0.5 which means that all measured variables are statistically significant. T-test for all measured variables is significant at a level of significance less than (0.001). All the insignificant measured variables are excluded from the model. Purchasing and Customer Services Indicators are the only two latent variables that showed significant relation with the EVA Indicator. Measuring the Goodness of Fit of the model: The model showed a significant correlation among all latent variables except purchasing Table 3: Confirmatory Factor Analysis to Measure the Validity of the model Estimate Standard T- Se Coefficients test P V P *** V C P7 P *** *** P8 P *** P9 P *** P10 P *** M *** M *** M *** M *** M *** M *** M *** M *** M *** M *** M *** L33 L *** L35 L *** L36 L *** L37 L *** L38 L *** C *** C *** C *** V *** V *** V *** V *** V *** V ***

9 and customer services indicators. The mean variance extracted for all latent variables in the model. Purchasing, manufacturing, logistics, customer service, and economic value added mean variance extracted is 0.442, 0.428, , 0.589, and respectively. The mean variance extracted for all latent constructs is Table 4 clarifies that there is adequate convergent validity, and all goodness of fit tests of the model showed significant results. Table 4: The Goodness of Fit Indices in the Confirmatory Factor Analysis Chi-Square Normed Fit Index (NFI) Degree of Freedom 369 Relative Fit Index (RFI) Level of Significance Incremental Fit Index (IFI) Normed Chi-Square Tacker Lewis Index (TLI) Root Mean Square Residual (RMR) Comparative Fit Index (CFI) Goodness of Fit Index (GFI) Root Mean Square Residual Adjusted Goodness of Fit Index (AGFI) Approximation (RMSEA) Multiple Regression Model: A multiple regression analysis has been conducted using the five indicators introduced previously, using the variables which resulted from the confirmatory factor analysis. EVAI = PI MI LI CI c Se = (0.072) (0.075) (0.064) (0.043) (0.265) t = (2.223) (0.745) (4.573) (2.098) (6.759) Sig = (0.027) (0.457) (0.000) (0.037) (0.000) R 2 = F= Sig = The previous model clarifies that there is a significant relationship at the 0.05 level, between each of the indicators of Purchasing, Logistics, Customer Services Indicators and the EVA Indicator, except for the Manufacturing Indicator. In order to improve the model, a stepwise regression is used. The insignificant Manufacturing Indicator is eliminated. EVAI = PI LI CI c Se = (0.058) (0.057) (0.043) (0.265) t = (3.287) (5.512) (2.089) (6.768) Sig = (0.001) (0.000) (0.038) (0.000) R 2 = F= Sig = The previous model is significant. Holding other variables constant if the Purchasing Indicator is increased by 1% the Economic Value Added Indicator will increase by 19.1%. Similarly, if the Logistics Indicator is increased by 1% the Economic Value Added Indicator will increase by 31.3%. Also, if the Customer Services Indicator is increased by 1% the Economic Value Added Indicator will increase by 9%. Holding all variables constant the EVA Indicator is about The R 2 means that 28.4% of the variation in the EVA Indicator is explained by the variations in PI, LI, and CI. Multiple regression model confirmed the acceptance of

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