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In recent years, companies around the world need to find new ways to reduce costs, increase productivity, improve product quality, and meet various customer demands. Each Distribution Center (DC) or warehouse is specifically designed to minimize costs in the company’s supply chain. Cross-docking is a logistics technique that eliminates storing and picking up items at warehouses. Cross-docking has several advantages compared to other product distribution strategies from both an economic and an environmental point of view. Cross-docking decisions are influenced by many factors such as the level of product demand, the cost of stock-outs, and the distance from suppliers to customers. This research builds a Decision Support System (DSS) that can help companies to ensure sustainability in the supply chain. This study assumes the demand is deterministic which is indicated by Economic Order Quantity (EOQ). This system can detect the time and quantity of certain items that experience cross-docks accurately. If the customers’demand cannot be fulfilled by the warehouse, then the goods are categorized as out of stock. By knowing the time and quantity of goods at the time of the cross-dock, the warehouse manager can make operational decisions quickly and accurately related to the resources such asoperators and forklifts.

As a government institution that performs tasks in the fields of meteorology, climatology, air quality and geophysics, BMKG (Meteorology, Climatology and Geophysics Agency) does not yet have an integrated information system to support each of its activities, such as instrumentation, engineering and calibration, observation, processing, dissemination, and service. The purpose of this study is to produce enterprise architecture design including business architecture, data architecture, technology architecture, and application architecture, and produce activity solutions and information system solutions that can help BMKG in developing its business processes. In this study, the framework of enterprise architecture design uses The Open Group Framework (TOGAF) with the Architecture Development Method (ADM) method. The results of this study, namely a proposed TOGAF model that is tailored to the business needs process of BMKG in designing enterprise architecture for IS / IT development.

This study aims to find interesting patterns on the database transaction so that it can be used as a recommendation sales promotion and inventory product. Companies have difficulty finding interesting transaction patterns in large databases, so it will be difficult to determine the right product promotions and inventory. To resolve these problems are to use data mining techniques with association rule. In previous studies, most studies adopt Apriori algorithm to analyze the association rules. In this study, the data mining technique used is the association rules algorithm FP-Growth. In the FP-Growth algorithm did generate candidates as in Apriori algorithm and using a development concept Tree in frequent itemset search so that it requires faster than Apriori. Some of the analyzes produced in this study are higher minimum support values and minimum trust used will result in fewer items and association rules. Association rule in this study has a lift ratio value of more than 1.00, meaning that item K and L are actually bought together. The higher the lift ratio produced shows the stronger the association rules are formed. The results of this study are the minimum confidence of 97.63%, the maximum trust is 99.37% and the lift ratio is 1,00013798. These results can be used as recommendations for optimizing product promotions and inventory.

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