Marketable dan Merketed Surplus Beras di Kabupaten Banyumas

Efforts to increase rice production must be integrated from the upstream sub-system, the farming sub-system, the downstream sub-system and the support sub-system. Upstream sub-system development is very important and cannot be ignored. Ownership of land by farmers is very small with an area of less than 0.5 hectares, most farmers are actually only landless farm laborers who depend on their work as agricultural laborers. Rice farmers in Banyumas Regency cannot be clearly defined as a producer when looking at the consumption side of the farmer. This study aims to determine the performance of rice farming, determine the magnitude of marketable surpluses and marketed surpluses of rice and analyze the factors that influence the marketed surpluses of rice in Banyumas Regency. Data analysis uses marketable analysis and marketed rice surplus, multiple linear analysis. The results showed an average area of land owned by 0.21 ha, with a productivity of 4,823.69 kg / Ha (GKP), 4,149.34 kg / Ha (GKG), 2,603.30 kg / Ha (Rice). The marketable value of rice surplus is 22,624.20 kg / MT or 85.67 percent of the total product and the marketed value of rice surplus is 13,422.83 kg / MT or 59.33 percent of the marketable surplus value. Product allocation is more used to pay for harvest labor and family food consumption. The results of multiple linear regression analysis with the model obtained Y = -146.89 + 0.87X1-1.94X2 + 4.94X3 + 1X4-83.80X5-0.009X6 + 2.95X7-7.23X8-1.6X9 + 34 , 3X10-0.007X11 + e. Marketed rice surpluses in Banyumas Regency are influenced by the variable amount of products and farming costs in nature.

Turning the Tables in Research and Development Licensing Contracts

Research and development (R&D) collaborations between an innovator and her partner are often undertaken when neither party can bring the product to market individually, which precludes value creation without a joint effort. Yet, the uncertain nature of R&D complicates the monitoring of effort, and the resulting moral hazard reduces a collaboration’s value. Either party can avoid this outcome by acquiring the capability that is missing and then taking sole ownership of the project. That approach involves two types of risks: one related to whether the other party’s capability will be acquired and one related to how well it will be implemented (if acquired). We find that the extent of these two risks determines the optimality of delaying contracting or of signing contracts with buyout and buyback options, a baseball arbitration clause, or a novel reciprocal option. Baseball arbitration and reciprocal option clauses are unique in two ways. First, unlike typical options with predetermined strike prices, they allow either party to determine the buyout price at the time of their offer. Second, they allow the offer’s recipient to “turn the tables” on the other party. Although baseball arbitration and reciprocal option contracts both address inefficient joint development and product allocation, they exhibit their own inefficiencies that stem from the two parties’ strategic behavior. The best choice of contract is determined by trade-offs between these inefficiencies. Our model explores the similarities between the baseball arbitration and reciprocal option clauses, and we propose a modification to the reciprocal option contract that would increase its profitability. This paper was accepted by Terry Taylor, operations management.

Solving product allocation problem (PAP) by using ANN and clustering

Proper planning of a warehouse layout and the product allocation in it, constitute major challenges for companies. In the paper, the new approach for the classification of the problem is presented. Authors used real picking data from the Warehouse Management System (WMS) from peak season from September to January. Artificial Neural Network (ANN) and automatic clustering by using Calinski-Harabasz criterion were used to develop a new classification approach. Based on the picking list the clients' orders were prepared and analyzed. These orders were used as input data to ANN and clustering. In this paper, three variants were analyzed: the reference representing the current state, variant with product relocation by using ANN, and the variant with relocation by using automatic clustering. In the research over 380000 picks for almost 1600 locations were used. In the paper, the architecture of the system module for solving the PAP problem is presented. Presented research proved that using multi-criterion clustering can increase the efficiency of the order picking process.

Improve the orders picking in e-commerce by using WMS data and BigData analysis

The primary purpose of the research is the improvement of the orders picking process without additional investments for the software, employees, tool and inventories. For problem-solving, the data about picking is exported and preprocessed from WMS. The BigData analysis and product clustering in Tableau software is delivered using the data, where the Product Allocation Problem (PAP) is solved. Picking time for reference scenario and new analysed one is calculated and compared. The presented research proves that standard data collected by WMS could be used for solving PAP for the reduction of total picking time. The method delivered by authors could be in a typical warehouse, where forklifts and employees do the order picking process. The plan after an upgrade could be used for automatic picking, and implemented WMS. For BigData analysis, Tableau is connected to WMS database. Such solution could be used for everyday analysis and planning the allocation of products. The presented method is easy to use; there is no need to invest in expensive software and automation of the picking process to achieve the high performance of the orders picking process. However, its application allows the increase of efficiency rates. Storekeepers can select more products at the same time. The presented research is original because of using simple methods and analysis of specific data, which until now are only used to calculate employee performance indicators.