Bidding Models Analysis on Ship Repair Projects ( Friedman and Ackoff & Sasieni Models )

The existence of a ship project carried out with a tender system by the LPSE allows all shipyard industries to bid on the project, this causes the chances of winning to become smaller, the determination of the tender price greatly determines the size of the profit that can be obtained and the percentage of the possibility of winning the project in a shipping industry. Therefore, the strategy of determining the bid price is very important. The statistical method used is multi discrete distribution, and multi normal distribution, while the bidding model uses Friedman (1956) and Ackoff & Sasieni (1968) models. The results obtained the best bid price strategy to win an auction or tender is the model that produces the lowest optimum mark-up, namely the Friedman model with multi normal distribution, while for Ackoff & Sasieni it produces a higher bid than the Friedman model except in certain company conditions.


I. INTRODUCTION
Currently, the number of shipyards in Indonesia has reached 250 companies, of which five are goverment-owned enterprises. Shipyards in Indonesia are currently capable of building various types and sizes of ships up to 50,000 DWT and repairing ships with a capacity of 150,000 DWT. However, out of 250 national shipyards, only 10 companies have a production capacity of more than 10,000 DWT with the largest graving dock facility of 300,000 DWT located in Batam and Banten (Ministry of Industry, 2015).
Indonesian Association of Shipbuilding and Offshore Facilities (Iperindo)'s data shows that since 2018 the condition of the shipbuilding industry is considered unfavorable because there is almost no procurement of government shipbuilding. The purchasing power of new ships from the private sector is also still weak. The shipyard utility rate throughout the year is only about 30% of the installed capacity of 1.2 million GT. (Bisnis.com, 2019).
The development of the shipping industry in Indonesia has led to increasingly fierce business competition. In an effort to get a job (project) in the construction and ship repair service sector which is the main job in the shipping industry, almost always go through a process called an auction (tender). This process is very important for the shipping industry, because the continuity of its business depends on the success or failure of this process.
The determination of the auction price (tender) is determined by various considerations and sometimes only based on business sense. The determination of the tender price greatly determines the size of the profit (profit) that can be obtained and the percentage of the probability of winning a project in a shipping industry. Therefore, the strategy of determining the bid price becomes very important and strategic. (Ali, 2020) Since 2008 the procurement of goods/services has started using the Electronic Procurement Service (LPSE) system. In this system, each contractor can participate in a tender after the package and tender specifications are announced by the relevant agency as the project owner. Thus the process of determining the winner of the tender becomes open and free from fraud. The more participants who take part in the tender, the smaller the chance to win the tender. If you don't use the right bidding strategy, it will be very difficult to win the tender.
The estimated mark-up value implemented in the project bid can be used as a reference in submitting the bid price, where the mark-up value obtained is the mark-up value calculated through previous bidding data in an area with a certain time span. The mark-up calculation approach model is a tool for the shipping industry in formulating its strategy in dealing with competitive bidding system tenders, so as to find out the best opportunity to participate in tenders or get the optimum opportunity to win projects and obtain optimum profits. (Ali, 2020) In this study, two approaches to statistical methods were used, namely multi discrete distribution, and multi normal distribution. While the bidding model uses the Friedman (1956) and Ackoff & Sasieni (1968) models.

II.
METHODOLOGY The research methology shown at Figure  1.  The following are the stages of the methodology in this research.

Stage 1: Preparation a. Problem Formulation
Every company can join the tender that held by LSPE so the winning probability became smaller to win the tender, so the companie need to make the strategy how to mark up the bid document. b. Literature review This stage is carried out by collecting literature review in order to obtain references that support the research process. The literature used in research is based on journals, books related to risk management and books related to loading and unloading.

Stage 2: Data Collection
The data required is secondary data obtained from the Ministry of Transportation's Electronic Procurement Service (LPSE), which can be accessed online via http://lpse.dephub.go.id/.

Stage 3: Data processing and analysis
• Data processing with a statistical approach is to determine the method used, Multi Discrete Distribution and Multi Normal Distribution Methode. • Calculate the maximum expected profit and determine the optimum mark-up using twobidding models, y the Friedman model, and the Ackoff & Sasieni model.

Stage 4: Conclusions and Suggestions
At this stage a conclusion is drawn according to the problem. While advice is given with the aim to provide recommendations for further research.

III. RESULT AND DISCUSSION A. Companies bidding Data
Based on LPSE's data from 2018 to 2019 that meet the requirements of this research, there are 31 ship repair auctions with 42 contractor companies which are the result of selection based on problem constraints. The terms of the project taken are ship repair work projects with a minimum of 2 (two) competitors contractors and with a minimum project price of Rp. 1.000.000.000,00 (one billion rupiah). Companies selected to participate in tenders at least 2 (two) times in ship repair tenders during 2018-2019. obtained. The number of companies makes the data conditions not ideal to be tested so that data selection is carried out which will only be used for further research.

B. Model Friedman 1. Multi Discrete Distribution
The results of the calculation of the probability of winning and the expected profit with a multi discrete distribution for the Friedman model can be seen in Table 1. Source: Data processed, 2020 From Table 1, the optimum mark-up value is 25% with the expected profit obtained is 16.03256%. The corelation between expected profit and mark up in the multi discrete distribution for the Friedman model shows that the expected profit increases along with the increase in the mark up value applied, which can be seen in Figure 2.

Multi Normal Distribution
The results of calculating the probability of winning and expected profit with multi normal distribution for Friedman's model for Company A can be seen in Table 2. Source: Data processed, 2020 From Table 2, the optimum mark-up value is 4% with an expected profit of 0.22885%. The corelation between expected profit and mark up on the multi normal distribution for the Friedman model can be seen in Figure 3.
Based on Figure 3, it is not recommended if Company A applies a mark up below 0% because it will cause losses to the company. And it is also not recommended if you apply a mark up above 15% because the expected profit generated is very small, even close to 0%.

C. Model Ackoff dan Sasieni
The Ackoff & Sasieni method only takes one data which is the company with the lowest bid during the auction range.

Multi Discrete Distribution
Bidding analysis is performed on company A. The results of the probability of winning and Expected profit are shown in Table 3. Source: Data processed, 2020 From Table 3 it can be seen that the optimum mark up obtained is 25% with an expected profit of 20.80009% which is further illustrated in Figure 4. It can be seen in Figure 4, the corelation between expected profit and mark up in the multi discrete distribution for the Ackoff and Sasieni model shows that the expected profit increases along with the increase in the value of the applied mark up. It is not recommended if Company A applies a mark up below 0% because it will cause losses to the company.

Multi Normal Distribution
In the multi normal distribution, one company is also taken which is the company with the lowest bid. The results of the calculation of the probability of winning and the expected value will be shown in Table 4.
From the calculation results in Table 4, it can be seen that the optimum mark-up value is 9% with an expected profit of 2.59603% at an R value of 1.09. The graph of the corelation between expected profit and mark up for multi normal distribution using the Ackoff & Sasieni model is shown in Figure 5. Source: Data processed, 2020 Figure 5 shows the corelation between expected profit and mark up on the multi normal distribution for the Ackoff and Sasieni model in Company A. It is not recommended if Company A applies mark up below 0% because it will cause losses to the company. And it is also not recommended if you apply a mark up that is too high because it will form a gentle valley on the normal distribution graph or show a very small expected profit, even close to 0%.

D. Analisis Expected Profit
From the analysis of the overall bidding model that has been carried out, it can be concluded that the optimum mark-up value with maximum expected profit at Company A is shown in Table 5.