Assessment of Organizational Performance in ...

Assessment of Organizational Performance in Dripline Manufacturer Company using Balanced Scorecard and TOPSIS Hamidreza Feili Assistant Professor, Department of Industrial Engineering, Karaj Branch, Islamic Azad University, Karaj, Iran ([email protected])

Mojtaba Qomi B.Sc., Department of Industrial Engineering, Karaj Branch, Islamic Azad University, Karaj, Iran ([email protected])

Amir Hesam Dasdar B.Sc. Student, Department of Industrial Engineering, Karaj Branch, Islamic Azad University, Karaj, Iran ([email protected])

Hossein Lashgari B.Sc. Student, Department of Industrial Engineering, Karaj Branch, Islamic Azad University, Karaj, Iran ([email protected])

Sowgand Jafari B.Sc. Student, Department of Industrial Engineering, Karaj Branch, Islamic Azad University, Karaj, Iran ([email protected])

Abstract Nowadays what is considered about most of the organizations is the assessment of organizational performance. One of the tools to do it is Balanced Scorecard (BSC). This technique helps that organization to reach its goals by focusing on the organization strategies and making logical and true relations with all the organizational aspects. In this research, a method of decision making along with BSC has been used. In order to use experts' ideas, TOPSIS method has been used. In this research, a dripline manufacturer company in Iran has been studied. Dripline is a form of irrigation that saves water and fertilizer. BSC and TOPSIS methods has been used to improve organizational performance in dripline manufacturer company. Finally, total performance score will be calculated by multiplying the global weights and scale values of performance indicators and then by summing the resulting performance levels. These results can be effective in assessing organizational performance in the mentioned company. Keywords: Dripline manufacturer company, Balanced Scorecard, TOPSIS.

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Introduction There are many strategic control techniques and methods aimed at evaluating – from a strategic management perspective – the results of the activities carried out by a business (Eren, 2002; Dinçer, 2004; Ülgen and Mirze, 2004). One of the methods enabling periodical and systematic system controls is the Balanced Scorecard (BSC) system developed by Kaplan and Norton (1992, 1996a). Balanced Scorecard enables expression of the vision and strategies of a business in terms of performance indicators and thus ensures establishment of the framework required for strategic measurement and management system. While underlying that traditional financial indicators are important, BSC suggests that financial indicators prove to be insufficient in explaining the business performance when they only contain the information related with the incidents that have taken place in the past. In the light of this thought, Kaplan and Norton (1996b) proposed BSC system that enables integration of the measurements regarding the past business performance with the measurements regarding the elements that will bring future performances. Kaplan and Norton (1996a) presented four perspectives that need to be balanced in performance measurement: financial, customer, internal business process and learning and development perspectives. On the basis of this approach proposed by BSC, not only financial lagging indicators but also leading indicators such as customer, internal business process and learning and development perspectives are taken into consideration in strategic management process. Therefore, BSC acts as a strategic management system rather than an operational system that gives tactics only (Kaplan and Norton, 1996a). However, it is discussed that BSC approach has some deficiencies on a methodological basis (Abran and Buglione, 2003; Leung et al, 2006; Lee et al, 2008; Yüksel and Dağdeviren, 2007). These deficiencies are in the method to be used in consolidating BSC perspectives or the performance indicators which act as different measurement units under each BSC perspective; the method to be adopted in determining the contribution to be made by each perspective on the performance (Abran and Buglione, 2003; Lee et al, 2008); the relative weights or importance of the performance indicators under each perspective and; the method to be used in calculating the business performance with a holistic quantitative approach (Leung et al, 2006). There are some studies, though limited in number, that focused on such discussions related with the methodological aspect of BSC and tried to suggest possible answers for these discussions with the help of multicriteria decision-making techniques (Sohn et al, 2003; Ravi et al, 2005; Leung et al, 2006; Yüksel and Dağdeviren, 2007; Lee et al, 2008; Feili et al, 2016b). In this research, a dripline manufacturer company in Iran has been studied. A comparison of subsurface dripline with sprinklers (Table 1) shows that dripline has two important advantages (amongst others) over sprinklers: higher water-use efficiency and reduced health risks (Byrne, 2003). Table 1. Benefits and limitations of sub-surface dripline irrigation compared to sprinkler irrigation (Byrne, 2003) Benefits Limitations Higher water efficiency Drippers can clog from iron precipitates Reduced weeds Root intrusion into drippers Reduced plant disease Doesn’t protect from frost like sprinklers can Flexible watering times Leaks/blockages more difficult to see More saline water tolerated by plants (less leaf burn) Unsuitable for seed germination Flexible installation in odd/narrow irrigation areas Installation is not a robust process. Correct installation and maintenance are essential Runs on low pressure- gravity feeding can be possible *Higher level of filtration required than sprinklers (to prevent drippers clogging) Reduced vandalism costs *Reduced public health risk *Characteristics specific to effluent irrigation

Dripline is considered "efficient" because it loses almost no water to evaporation, run-off or overspray. A comparison of sprinklers with sub-surface dripline to apply treated effluent water on Bermuda grass (Jnad et al, 2000), indicated that there were no significant differences between the two irrigation systems in Bermuda grass health. However, root distribution and activity appeared to be more restricted when irrigated by (surface) dripline than sprinklers in a trial using tomato and peanut plants

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(Ben-Asher and Silberbush, 1992). Driptape (low flow, low pressure) trialled in California was also considered suitable for use with activated sludge secondary effluent (Hills and Brenes, 2001). Therefore using dripline, rather than sprinklers, for irrigation with treated wastewater should not compromise turf health. Performance of dripline with effluent is mainly limited by dripper (emitter) clogging e.g. with organics or salts (Capra and Scicolone, 2004). The system uniformity degrades, which in turn lowers the irrigation efficiency. Appropriate filtration of the water supply can help minimise blockages. Dripper flow rates also affect the likelihood of clogging. Drippers at several flow rates were trialled with lagoon wastewater and the experimenters found that although the larger flow drippers (≥1.5 L/h/ emitter) experienced little decrease from the original flow over time, while the smaller dripper sizes (0.91 L/h/emitter or less) "may be risky" (Camp et al, 1997). The latter are generally not recommended by dripline manufacturers for effluent use (Taylor et al, 2006). In this research, BSC and TOPSIS methods has been used to improve organizational performance in dripline manufacturer company.

Research Methodology Balanced Scorecard (BSC) The Balanced Scorecard (BSC) is a rather recent development in managerial accounting (Ittner and Larcker, 2001). Kaplan and Norton (1992) introduced the BSC as a performance measurement and reward system that helps link operational performance measures to the implementation and monitoring of strategy. The BSC typically consists of four sets of measures: financial, customer, internal processes, and learning and growth. Though the BSC is a compelling innovation because it incorporates strategy, process, and managers to provide an integrated system of planning and control (Atkinson et al, 1997), there is really very little credible academic evidence that it delivers enhanced performance, even on its own value-terms (see Nørreklit, 2000). Researchers have examined various explicitly cognitive biases found in the use of the BSC. Lipe and Salterio (2000) identified a common measures bias in the BSC where superiors ignore unique performance measures in favor of measures common to subordinates being evaluated. Subsequent studies have examined approaches to mitigate the common measures bias such as the use of strategically linked performance measures and strategy maps (Banker et al, 2004; Humphreys and Trotman, 2011), knowledge and training (Dilla and Steinbart, 2005), assurance and process accountability (Libby et al, 2004), and disaggregation of the assessment process (Roberts et al, 2004). Other biases examined when the BSC is used for performance evaluation include a ‘selective attention to strategy effectiveness’ bias (Wong-On-Wing et al, 2007), likeability bias (Kaplan et al, 2008), bias from BSC information organization (Cardinaels and van Veen-Dirks, 2010; Lipe and Salterio, 2002), and evaluator ambiguity intolerance (Liedtka et al, 2008). Cognitive biases have also been considered in the use of the BSC for strategy development and evaluation. Tayler (2010) examines the influence of motivated reasoning on projects evaluated under the BSC (Upton and Arrington, 2012; Feili et al, 2016c). TOPSIS Method The technique for order performance has similarity to the ideal solution (TOPSIS) method which is a method used to solve the multi-criteria decision making (MCDM) problems, which were first developed by Hwang and Yoon in 1981 (Olson, 2004; Wu and Olson, 2006; Jahanshahloo et al, 2006; Hung and Chen, 2009; Tsai et al, 2008; Balli and Korukoglu, 2009; Karimi et al, 2010; Abdul Rahman, 2012). The primary concept of the TOPSIS method is that the preferred alternative should not only have the shortest distance from the positive ideal solution (PIS), but also have the farthest distance from the negative ideal solution (NIS) or nadir. TOPSIS is one of the MCDM approaches used where the weighting criteria for the choice of the most adequate is exogenously defined (Feng and Wang, 2001; Pirdavani et al, 2010; Hassan et al, 2013; Bilbao-Terol et al, 2014; Chen et al, 2014; Wang et al, 2014; Barros and Wanke, 2015; Wanke et al, 2015). Its basic principle assumes that the chosen alternative should simultaneously have the shortest distance from the positive-ideal solution and the farthest distance from the negative-ideal solution (Hwang and Yoon 1981; Ertuğrul and

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Karakaşoğlu, 2009; Feili et al, 2016a). More precisely, the positive-ideal solution is the one that maximizes the benefit and simultaneously minimizes the total costs. On the contrary, the negativeideal solution is the one that minimizes the benefit and simultaneously maximizes the cost (Lai et al, 1994; Wu et al, 2010). The step by step procedure of TOPSIS is given as (Tyagi et al, 2014): Step 1: Generate an evaluation matrix by considering of ‘y’ alternatives and ‘z’ criteria, with the intersection of each alternative and criteria given as xij , we therefore have a matrix (xij ) y×z

(1)

Step 2: Normalize the matrix (Xi j)yxz to convert into the matrix R=(nij)yxz using the normalization formula given as: 𝑛ij =

xij

(2)

y √∑i=1 xij2

Step 3: Determine the weighted normalized decision matrix D = (𝑑𝑖𝑗 )yxz = ( 𝑤𝑗 𝑛𝑖𝑗 )𝑦𝑥𝑧´ 𝑖 = 1.2 … . 𝑦

Step 4: Find out the Positive and Negative ideal solutions 𝐴+ = {(

𝑚𝑎𝑥 𝑚𝑖𝑛 𝑑 |𝑗 ∈ 𝐽) . ( 𝑑 |𝑗 ∈ 𝐽′) 𝑓𝑜𝑟 𝑖 = 1.2. … . 𝑦} = {𝑑1+ . 𝑑2+ . … . 𝑑𝑗+ . … . 𝑑𝑧+ } 𝑖 𝑖𝑗 𝑖 𝑖𝑗

(3)

𝐴− = {(

𝑚𝑎𝑥 𝑚𝑖𝑛 𝑑 |𝑗 ∈ 𝐽) . ( 𝑑 |𝑗 ∈ 𝐽′) 𝑓𝑜𝑟 𝑖 = 1.2. … . 𝑦} = {𝑑1− . 𝑑2− . … . 𝑑𝑗− . … . 𝑑𝑧− } 𝑖 𝑖𝑗 𝑖 𝑖𝑗

(4)

Where, J = {𝑗 = 1.2. … 𝑧|𝑗 𝑎𝑠𝑠𝑜𝑐𝑖𝑎𝑡𝑒𝑑 𝑤𝑖𝑡ℎ 𝑡ℎ𝑒 𝑏𝑒𝑛𝑖𝑓𝑖𝑡 𝑐𝑟𝑖𝑡𝑒𝑟𝑖𝑎} J´ = {𝑗 = 1.2. … 𝑧|𝑗 𝑎𝑠𝑠𝑜𝑐𝑖𝑎𝑡𝑒𝑑 𝑤𝑖𝑡ℎ 𝑡ℎ𝑒 𝑐𝑜𝑠𝑡 𝑐𝑟𝑖𝑡𝑒𝑟𝑖𝑎}

Step 5: Calculate the distance/separation from: Positive Ideal Separation 𝑧 𝑆𝑖+ = √∑𝑗=1 (𝑑𝑖𝑗 − 𝑑𝑗+ )2

, i = 1,2, … , y

(5)

, i = 1,2, … , y

(6)

Negative Ideal Separation 𝑧 𝑆𝑖− = √∑𝑗=1 (𝑑𝑖𝑗 − 𝑑𝑗− )2

Step 6: Calculate the Relative closeness coefficient to the Ideal Solution

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𝐶𝐶𝑖+ =

Sj− (Sj+ + Si− )

.

(7)

0 < 𝐶𝐶𝑖+ < 1. i = 1.2. … . y

𝐶𝐶𝑖+ = 1 𝑖𝑓 𝐴𝑖 = 𝐴+ 𝐶𝐶𝑖+ = 0 𝑖𝑓 𝐴𝑖 = 𝐴−

The rank of considered alternatives can be decide, according to the descending order of CCi∗.

Findings This research has been done according to the following steps: Step 1: A vision was determined by the expert team established at the beginning of the implementation and the business vision was expressed as "Becoming a preferred market brand". Step 2: Strategies required for the achievement of the business vision were determined. At the end of this step, the following strategies were decided to be pursued:  Strategy 1 (S1): The development of research projects and more attention to research and groundworks.  S2: To adopt new technologies to be used in production phase and to continuously increase product quality.  S3: To improve after-sales service quality by widening service network.  S4: Diversifying the facilities, activities and packing services in order to satisfy customers. Step 3: BSC perspectives and performance indicators were defined (Table 2). It is necessary to mention that Balanced Scorecard is a model and it should be developed according to the special modes of an organization. Also, the number of its perspective can be less or more, BSC is not limited to only four perspectives (financial, customer, internal business process, learning and growth). In this research, supply chain perspective has been added. Table 2. BSC perspectives and performance indicators BSC perspectives Performance indicators Financial Return on assets (F1) Return on investment (F2) The rate of capital accumulation (F3) Return on equity (F4) Period cash flow (F5) Customer Customer satisfaction with the brand (C1) The number of taking part in fairs (C2) Market share (C3) Response rate (C4) Sales volume (C5) Internal Business Process Pricing mechanism for products (IBP1) Check times of inventory (IBP2) Staff salary (IBP3) Sale conditions (IBP4) Management efficiency and effectiveness (IBP5) Learning and Growth Staff productivity (LG1) Per capita educational investment (LG2) Being present in key parts of the market (LG3) The ability to use new technologies (LG4) Being flexible against competitors strategies (LG5) Supply Chain Order cycle time (SC1) On time delivery (SC2) Total cost of delivery (SC3) Good responding to order (SC4)

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Time for planning and forecasting (SC5)

Step 4: local weights of the strategies, BSC perspectives and performance indicators are calculated. By using views of experts prioritizing the Strategies is done by using software TOPSIS. Results obtained from TOPSIS software are given in Chart 1 and Table 3.

Chart 1. Strategies local weights (using TOPSIS software)

Strategies Weight

S1 0.257

Table 3. Strategies local weights S2 0.404

S3 0.233

S4 0.106

After the determination of the strategic priorities, BSC perspective weights were defined on the basis of these strategies. Results obtained from TOPSIS software are given in Charts 2-5 and Tables 4-7.

Chart 2. Local weights of BSC perspectives with respect to S1 (using TOPSIS software)

BSC perspectives Local weight

Table 4. Local weights of BSC perspectives with respect to S1 F C IBP LG 0.170 0.252 0.182 0.107

6

SC 0.289




SC 0.326




7

SC 0.230




SC 0.152

Global weights of BSC perspectives were calculated as follows, by multiplying the weights listed in Tables 4-7 with the strategy weights:

𝑊𝐵𝑆𝐶

𝐹 0.223 𝐶 0.161 = 𝐼𝐵𝑃 = 0.206 𝐿𝐺 0.134 [ 𝑆𝐶 ] [0.276]

In the last phase of this step, local weights of the performance indicators were determined. The local weights calculated for performance indicators are given in Charts 6-10 and Tables 8-12.

Chart 6. Local weights of financial (using TOPSIS software)

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F Local weight

F1 0.146

Table 8. Local weights of financial F2 F3 0.290 0.206

F4 0.217

F5 0.141

C4 0.232

C5 0.130

Chart 7. Local weights of customer (using TOPSIS software)

C Local weight

C1 0.103

Table 9. Local weights of customer C2 C3 0.322 0.213

Chart 8. Local weights of internal business process (using TOPSIS software)

IBP Local weight

Table 10. Local weights of internal business process IBP1 IBP2 IBP3 0.193 0.234 0.180

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IBP4 0.271

IBP5 0.122

Chart 9. Local weights of learning and growth (using TOPSIS software)

LG Local weight

LG1 0.203

Table 11. Local weights of learning and growth LG2 LG3 0.260 0.144

LG4 0.256

LG5 0.137

Chart 10. Local weights of supply chain (using TOPSIS software)

SC Local weight

SC1 0.288

Table 12. Local weights of supply chain SC2 SC3 0.169 0.188

SC4 0.170

SC5 0.185

Step 5: In this step, dependence among BSC perspectives were determined. Interdependent weights of the BSC perspectives are calculated and the dependencies among the perspectives are considered. Dependence among the perspectives is determined by analyzing the impact of each perspective on every other perspective using pairwise comparisons. Interdependent weights of the perspectives are computed by multiplying the dependence matrix of the perspectives we obtained with the local weights of perspectives provided in step 4. The interdependent weights of the perspectives are calculated as follows:

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𝑊𝐵𝑆𝐶

𝐹 0.109 𝐶 0.201 = 𝐼𝐵𝑃 = 0.190 𝐿𝐺 0.244 [ 𝑆𝐶 ] [0.256]

Step 6: Using interdependent weights of the perspectives (step 5) and local weights performance indicators, global weights for the indicators are calculated in this step. Global indicators weights are computed by multiplying local weight of the indicators with the interdependent weight of the perspective to which it belongs. Computed values are shown in Table 13. BSC perspectives F

C

IBP

LG

SC

Table 13. Computed global weights of performance indicators Interdependent Performance Weights weights indicators 0.109 F1 0.146 F2 0.290 F3 0.206 F4 0.217 F5 0.141 0.201 C1 0.103 C2 0.322 C3 0.213 C4 0.232 C5 0.130 0.190 IBP1 0.193 IBP2 0.234 IBP3 0.180 IBP4 0.271 IBP5 0.122 0.244 LG1 0.203 LG2 0.260 LG3 0.144 LG4 0.256 LG5 0.137 0.256 SC1 0.288 SC2 0.169 SC3 0.188 SC4 0.170 SC5 0.185

Global weights 0.016 0.031 0.022 0.024 0.015 0.021 0.065 0.043 0.047 0.026 0.037 0.044 0.034 0.051 0.023 0.049 0.063 0.035 0.062 0.033 0.074 0.043 0.048 0.043 0.047

Steps 7: In this stage, performance of the organization is determined by using the global weight values of performance indicators Table 13 and the linguistic measurement scale (Table 14). The calculations are shown in Table 15. Measure the performance indicators. Linguistic variables proposed by Cheng et al (1999) are used in this step. The memberships functions of these linguistic variables are shown on Figure 1 and the average value related with these variables are shown Table 14 (Yüksel and Dağdeviren, 2010).

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Figure 1. Membership functions of linguistic values for performance indicator rating Table 14. Linguistic values and mean of fuzzy numbers Linguistic values The mean of fuzzy numbers Very high (VH) 1 High (H) 0.75 Medium (M) 0.5 Low (L) 0.25 Very low (VL) 0

Performance indicators F1 F2 F3 F4 F5 C1 C2 C3 C4 C5 IBP1 IBP2 IBP3 IBP4 IBP5 LG1 LG2 LG3 LG4 LG5 SC1 SC2 SC3 SC4 SC5

Table 15. Performance measured by using the proposed BSC–TOPSIS model Global weights Linguistic Scale value Performance (gw) evaluations (sv) (gw×sv) 0.016 H 0.75 0.012 0.031 H 0.75 0.023 0.022 M 0.5 0.011 0.024 M 0.5 0.012 0.015 M 0.5 0.007 0.021 M 0.5 0.010 0.065 VH 1 0.065 0.043 H 0.75 0.032 0.047 H 0.75 0.035 0.026 H 0.75 0.019 0.037 L 0.25 0.009 0.044 L 0.25 0.011 0.034 M 0.5 0.017 0.051 H 0.75 0.038 0.023 H 0.75 0.017 0.049 H 0.75 0.037 0.063 M 0.5 0.031 0.035 L 0.25 0.009 0.062 M 0.5 0.031 0.033 L 0.25 0.008 0.074 VH 1 0.074 0.043 H 0.75 0.032 0.048 H 0.75 0.036 0.043 M 0.5 0.021 0.047 M 0.5 0.023

Total performance score was calculated by multiplying the global weights and scale values of performance indicators and then by summing the resulting performance levels.

Conclusions Nowadays what is considered about most of the organizations is the assessment of organizational performance. One of the tools to do it is Balanced Scorecard (BSC). This technique helps that organization to reach its goals by focusing on the organization strategies and making logical and true relations with all the organizational aspects. In this research, a method of decision making along with

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BSC has been used. In order to use experts' ideas, TOPSIS method has been used. In this research, a dripline manufacturer company in Iran has been studied. Finally, Total performance score was calculated by multiplying the global weights and scale values of performance indicators and then by summing the resulting performance levels. The model proposed in the scope of this study was related with a dripline manufacturer company; however, it can also be adapted to different businesses. Modifications may be required on the proposed system due to two reasons: firstly, the components constituting the analytical structure of the proposed model – namely, strategies, BSC perspectives and performance indicators – may vary depending on the business vision. Secondly, relationships or dependencies among BSC perspectives or performance indicators may also vary. Modifications and adaptations to be made due to these two reasons will enable the use of this model in other enterprises.

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