Passion fruit agro-industry supply chain performance assessment in North Sumatra

In the agro-industry sector, supply chain management activities such as the procurement of raw materials, processing, warehousing, distribution, and transportation networks are of utmost importance. Agricultural commodities are perishable, seasonal, varying, and bulky in nature which cause difficulties in its management compared to non-agricultural products. Supply chain performance is an indicator of the success of a company. Therefore its assessment is needed to control and determine the performance feasibility of the company. This study aims at assessing the performance of the passion fruit syrup agro-industry supply chain in North Sumatra. The performance is assessed using SCOR and AHP models. The results show that the three performance assessment matrices with the highest weight are processing costs (0.165), delivery accuracy (0.146), and perfect goods condition (0.122). The supply chain performance was categorized as average (78.69%).


Introduction
The development of agro-industry in Indonesia is an inseparable part of the national industrial policy framework, evident from its substantial contribution (44.3% in 2017) towards non-oil and gas GDP. The feasibility of industrial development in Indonesia can be seen through the development of natural resource-based industries such as cocoa, rubber, CPO, food and beverage, steel and upstream aluminum, and seaweed (DG Industri agro, 2017). The passion fruit syrup industry is categorized in the food and beverage industry, which had a growth of 4.53% in 2017. As one of the pillars of agribusiness, agro-industry plays an important role in increasing income distribution and economic growth.
In agro-industry, many challenges and problems occur in applying supply chain management (Vorst, 2006). They emerge from the perishable, bulky, and seasonable nature of agricultural commodities. Actors in the supply chain, namely farmers as suppliers' suppliers, collectors as suppliers, transportation services as third-party logistics, processing industries as manufacturers, delivery services, distributors, and retailers will pay thorough attention to these characteristics.
Business competition, especially in agro-industry, has become increasingly tight. To survive, companies must supply the right products at the right time with the right cost. The awareness of cheap, fast, and quality products have generated the new Supply Chain Management concept in the 1990s.
Supply Chain Management (SCM) is an effective business management approach that has been a concern of academics, consultants, practitioners, and business managers in recent  (Wong and Wong, 2007). The evolution of SCM in the last decade has produced many studies concerning its performance assessment as shown by (Najmi et al, 2013).
Company performance is the realization of its goals. Many factors, including company suppliers, internal companies, distributors, and end-user consumers affect supply chain performance which is why it is an important parameter to assess.
In modern management, supply chain management is one of the concepts that can be used as a basis for performance assessment. Performance assessment plays an important role in achieving company goals on account of its functions and roles in planning, controlling, and evaluating the realization of the company goals. It will greatly contribute to performance improvement and other related programs which help to maintain the superiority of the supply chain strategy.
Given the importance of supply chain performance assessment, experts have provided various applicable performance assessment alternatives, one of which is the Supply Chain Operation Reference (SCOR) model. It was introduced by the Supply Chain Council (2008) and can be used as a basis for strategic decision making (Huan Sheoran and Wang, 2004). SCOR is a reference model of supply chain operation which is based on the process approach (process-based approach). It can objectively assess performance based on existing data and identify aspects needing improvements to create competitive advantages (Pujawan, 2015).
This method has 5 scopes, namely: 1) Plan, 2) Source, 3) Deliver, 4) Process, and 5) Return. In addition, SCOR also utilizes several dimensions, namely: 1) Reliability, 2) Responsiveness, 3) Flexibility, 4) Cost, and 5) Asset (Sillanpaa, 2011). Some of these dimensions are decomposed in several Key Performance Indicators (KPIs) that are selfdetermined by related industries. Therefore, in assessing supply chain performance, the determination of KPIs plays a crucial role in measuring the performance of the passion fruit syrup industry supply chain.
Therefore, it is necessary to determine the KPIs prior to assessing the passion fruit syrup agroindustry supply chain performance in order to understand the key issues so that future improvements will be on target. The next step is to provide weighting value for each KPI in order to realize performance improvements.
The passion fruit syrup industry supply chain starts from farmers as raw material suppliers, collectors, juice industries, syrup industries, and retailers. A number of problems were encountered: 1) incorrect number and time-delivery of goods, 2) delivery errors, and 3) decreasing customer demands. The performance of both the company and the suppliers has caused these problems to occur.
In order to further observe the problems occurring in the passion fruit syrup industry, research is needed on the performance assessment of its supply chain. Performance assessment is crucial in determining the efficiency of activities carried out by supply chain actors so that relevant action can be taken. It is also needed to correct problems and prevent further damage, regulate coordination to meet consumer demands (Chopra and Meindl, 2006), create an effective and efficient upstream to downstream integration (Marimin and Maghfiroh, 2010), evaluate supply chain performance in a holistic manner, determine necessary improvements to create competitive advantage (Rachman, 2014), and optimize the supply chain model.

Research Method
This research uses a descriptive observational method. The research steps are as follows: 1. Identifying the Passion Fruit Syrup Agro-industry Supply Chain. Validation is carried out through in-depth interviews with experts and stakeholders in the passion fruit agro-industry chain. 4. KPI Weighting.
Weighting is given to each KPI using the Analytical Hierarchy Process (AHP) model. 5. Assessing Supply Chain Performance.

1 Level Identification in SCOR Model
The SCOR model is decomposed into three processing hierarchies equivalent to the Abolghasemi, et al (2015) model: 1. Level 1 is the highest level that provides a general definition of five important processes: plan, source, deliver, make (process), and return. 2. Level 2 is known as the configuration level, in which the passion fruit syrup agroindustry supply chain can be configured based on its core processes. It can form the current (as is) and the desired (to be) configurations. 3. Level 3 is the process element level containing process elements and references (benchmarks and best practices).
The hierarchy structure in this study was determined as follows:

2 Identifying and Determining Key Performance Indicators (KPI)
Identifying and determining the Key Performance Indicators (KPI) are the framework for the passion fruit syrup industry performance assessment. Identification is carried out through in-depth interviews and questionnaires from 3 experts representing academics, practitioners, and 5 experts representing passion fruit syrup companies based on position, education, and employment time.
The questionnaire is semi-closed questions to select KPIs that are commonly used in the supply chain performance assessments (Ulya, et al. 2017). However, open questions were also presented through in-depth interviews to provoke the emergence of new KPIs as passion fruit agro-industry supply chain performance indicators. All KPIs are transformed into hierarchical forms then weighted based on the AHP model.

3 KPI Weighting through Analytical Hierarchy Process
AHP is a paired comparison matrix, where A1 in the column to the left is compared with A1, A2, A3, and so on in regard to the C property in the upper left corner. This process is repeated for column A2 and so on.  To fill a paired comparison matrix, a number is used to describe the importance of an element with respect to that trait. The most important thing to consider in AHP is inconsistency.
The comparison is "Perfectly Consistent" if and only if aik, akj = aij, where i, j, k = 1,2,3 ........,n. However, this consistency must not be forced even if the high level of inconsistency is undesirable. If the reciprocal matrix is consistent then ƛ max = n. Saaty (1993) defines a measure of consistency as the Consistency Index = Description: CR: Consistency Ratio CI: Consistency Index RI: Random Index CR value of ≤ 0.1 is tolerable, anything above requires a revision. CR = 0 is "perfectly consistent".

1 Metric Weighting for Supply Chain Performance Assessment using AHP
Weighting applies an α value of 0.5 indicating that experts have an average level of trust at the time of assessment and an ω value of 0.5 which indicates that the assessment given was neither optimistic nor pessimistic in accordance with the decision-making concept of AHP (Saaty, 2014). The results of the matrix weighting of passion fruit syrup agro-industry supply chain performance hierarchically are shown in Figure 1 and tabulated in Table 1. The expert assessment consistency index is 0.032, meaning that a consistent assessment was provided.
Of all the performance assessment matrices, the processing cost performance matrix (0.165) was the most weighted followed by the delivery accuracy matrix (0.146). The weighting results indicate that cost is an important factor in providing on-time deliveries.

Passion Fruit Syrup Agro-Industry Supply Chain Performance Assessment
Supply chain performance was assessed using Supply Chain Operation Reference (SCOR) 11 which describes supply chain in four performance attributes, namely reliability, responsiveness, agility, and cost.
The assessment was started by creating the initial hierarchical structure based on the basic supply chain functions (plan, source, deliver, make, and return) focusing on reliability, responsiveness, flexibility, and costs. This initial hierarchy is adjusted according to the conditions of the company and integrated into several performance indicators. The performance was assessed using actual data of each supply chain actor and the weighing results of the matrices shown in Table 1. The results were categorized based on the five criteria of performance standards according to Rotaru, et al (2014). Table 2 shows the performance of each member of the supply chain. The actual value of each performance indicator for each of the 7 agro-industries using the percentage of the target and being integrated with the results of matrix weighting as shown in Table 3. Integration starts from the performance assessment matrix to the business process, resulting in a passion fruit syrup agro-industry supply chain performance assessment as shown in Table 4.   Table 4 shows that PT Brastagi has the lowest supply chain performance (69.30%). This is caused by the low value of production speed flexibility (70%). The company is unable to respond to changes in demand in a timely manner because of its small working capital. The poor quality of its distribution system has also affected its reliability attribute, in which the products are not in accordance with consumer demands. Based on field observations, there was an accumulation of passion fruit syrup in the warehouse of PT Brastagi which indicates an increase in storage (warehouse) costs. In addition to passion fruit syrup, PT Brastagi also produces other types of synthetic-based syrup to anticipate the seasonal unavailability of passion fruit. The unpredictable distribution system and market conditions are factors that affect companies in selling passion fruit syrup so that they also influence the performance of retailers.
The highest performing supply chain actor was PT Sarang Tawon with a score of 82.63%, followed by retailers with a score of 77.68%. One of the factors affecting the performance of retailers is the full sent order matrix score of 75%. In general, retailers in North Sumatra also sell synthetic-based syrup alongside passion fruit syrup. Therefore, it can be concluded that the performance of the passion fruit syrup agro-industry supply chain in North Sumatra is average (78.69).

Conclusions
1. Passion fruit syrup agro-industry supply chain performance in North Sumatra is in the average category, assessed using 9 performance indicators (performance matrices): full sent orders, delivery precision, perfect item conditions, raw material acquirement cycle time, processing cycle time, production speed flexibility, production capacity alteration ability, processing fee, and maintenance costs having an average score of 76.90%. 2. In carrying out passion fruit syrup agro-industry supply chain activities, information flow at the agroindustry-supplier and farmer-supplier nodes is still not well established.