This paper first introduces the forecasting problems faced by large retailers, from the strategic to the operational, from the store to the competing channels of distribution as sales are aggregated over products to brands to categories and to the company overall. Aggregated forecasting that supports strategic decisions is discussed on three levels: the aggregate retail sales in a market, in a chain, and in a store. Product level forecasts usually relate to operational decisions where the hierarchy of sales data across time, product and the supply chain is examined. Various characteristics and the influential factors which affect product level retail sales are discussed. The data rich environment at lower product hierarchies makes data pooling an often appropriate strategy to improve forecasts, but success depends on the data characteristics and common factors influencing sales and potential demand. Marketing mix and promotions pose an important challenge, both to the researcher and the practicing forecaster. Online review information too adds further complexity so that forecasters potentially face a dimensionality problem of too many variables and too little data. The paper goes on to examine evidence on the alternative methods used to forecast product sales and their comparative forecasting accuracy. Many of the complex methods proposed have provided very little evidence to convince as to their value, which poses further research questions. In contrast, some ambitious econometric methods have been shown to outperform all the simpler alternatives including those used in practice. New product forecasting methods are examined separately where limited evidence is available as to how effective the various approaches are. The paper concludes with some evidence describing company forecasting practice, offering conclusions as to the research gaps but also the barriers to improved practice.

Retail forecasting: research and practice

Final Version: ..2022 Abstract: In the cloud computing market (CCM), computing services are traded between cloud providers and consumers in the form of the computing capacity of virtual machines (VMs). The Amazon spot market is one of the most well-known markets in which the surplus capacity of data centers is auctioned off in the form of VMs at relatively low prices. For each submitted task, the user can offer a price that is higher than the current price. However, uncertainty in the market environment confronts the user with challenges such as the variable price of VMs and the variable number of users. An appropriate strategy should both maximize the user’s utility and determine the best bid for him/her. In this paper, we aim to solve the problem of joint minimizing the cost of processing tasks and maximizing user satisfaction. Our proposed method, which we call bi-level Markov decision-making process (BLMDP), works on two levels. At the top level, it selects the most appropriate user bids and adjusts the spot price to minimize the cost of VMs on the cloud provider side. At a low level, it decides to admit tasks in such a way as to maximize the user side satisfaction. Performance evaluation based on real data collected from Amazon web services shows that BLMDP manages to minimize cloud provider’s costs and maximize user gain more effectively compared

BLMDP: A new bi-level Markov decision process approach to joint bidding andtask-scheduling in cloud spot market

Information Technology (IT) is an enabler for Indonesian tourism industry Before Covid-19 pandemic, the industry already felt the effect of IT The pandemic changes the global industry situation This paper presents mathematical model on the number of tourism start-up, the number of local and international tourists, and national Gross Domestic Product (GDP) The results of this study show that while there are problems because of Covid-19 pandemic in the following year, the future income of tourism industry shows steady growth The model can be used by other industry for better analysis

/pdf/analysis-of-future-income-forecast-for-indonesian-tourism-1tt32h1xbu.pdf

Analysis of Future Income Forecast for Indonesian Tourism Industry – A Neural Network Research on Tourism Digital Economy

This paper reports on quantification and management of inherent systemic risk/performance tradeoff in the cloud computing model. We view Cloud as a Complex System and associate the systemic risks with a possibility of system phase transition to the undesirable persistent state. Our analysis under mean-field and fluid approximations suggests a shift in cloud architecture design and operation paradigm from maximizing the economic benefits to management and optimization of the inherent systemic risk/benefit tradeoffs. We argue that economics makes this tradeoff more pronounced by driving Cloud service providers towards the boundary of the operational regime, and thus increasing risk of overload when the system does not have sufficient capacity for sustaining the exogenous demand.

Systemic Risks in the Cloud Computing Model: Complex Systems Perspective

The development of information technology has grown exponentially and various of data collections and its method has been obtained. In the era of big data, data has now become an asset that held important values, while in the implementation of data delivery, it clearly is not always safe. One of the method to secure data delivery is data encryption using Cryptography. Cryptography provides an encryption service to secure data delivery by transforming it to random values so that it can no longer be read. The goal in this study was to produce an application that could be used to encrypt data, using ElGamal's cryptography method and hash checking using the SHA256 algorithm. After encryption, to ensure the encrypted data is still the original data without any changes or manipulation by unauthorized 3rd party then done by checking the hash generated using SHA256 algorithm. The data used in this study was a sample of raw data from the ATPWTP survey (ability to pay and willing to pay) conducted by the BPS Cirebon (Central Statistics) in 2019 and the data was in the form of Excel and txt files. The encryption process resulted in a cipher larger than the plaintext and takes longer for the data encryption process than during the data cipher decryption process.

/pdf/raw-data-security-by-using-elgamal-and-sha-256-public-key-icwmi4l3z9.pdf

Raw data security by using elgamal and sha 256 public key algorithm

Cloud computing can be described as a network technology among users to share data, resources and even services. Because million users have the same rights to use network for data transmission, data is vulnerable to be hacked by irresponsible person. The concentration of security on current system can only be found on data storage on cloud, meanwhile less concentration is found on data transmission. As we determine security can be potential issue, the proposed system (encryption) will secure data transfer. The proposed system, Advanced Encryption Standard (AES) algorithm, will anticipate every irresponsible person to hack cloud system. The proposed algorithm will encrypt and decrypt information on data transmission and storage. To improve published research on security of data transmission by using AES algorithm is the paper purpose. In order to do that, the research will use several mathematical methods. There are Markov Chain and Forecasting methods. Research results show that there are possibilities to research on AES algorithm for security of data transmission in the near future by 29% in 2023.

D Sianturi Tigor Franky

Papers

Forecast Analysis of Research Chance on AES Algorithm to Encrypt during Data Transmission on Cloud Computing