scholarly journals An econophysics approach to forecast bulk shipbuilding orderbook: an application of Newton’s law of gravitation

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Quazi Mohammed Habibus Sakalayen ◽  
Okan Duru ◽  
Enna Hirata
Keyword(s):  
Raw Materials ◽  
Moving Average ◽  
Research Result ◽  
Bulk Carrier ◽  
Content Type ◽  
Predictive Variables ◽  
Smooth Flow ◽  
Analysis Technique ◽  
Average Analysis ◽  
Forecasting Method

Purpose Bulk shipping mostly facilitates the smooth flow of raw materials around the globe. Regardless, forecasting a bulk shipbuilding orderbook is a seldom researched domain in the academic arena. This study aims to pioneer an econophysics approach coupled with an autoregressive data analysis technique for bulk shipbuilding order forecasting. Design/methodology/approach By offering an innovative forecasting method, this study provides a comprehensive but straightforward econophysics approach to forecast new shipbuilding order of bulk carrier. The model has been evaluated through autoregressive integrated moving average analysis, and the outcome indicates a relatively stable good fit. Findings The outcomes of the econophysics model indicate a relatively stable good fit. Although relevant maritime data and its quality need to be improved, the flexibility in refining the predictive variables ensure the robustness of this econophysics-based forecasting model. Originality/value By offering an innovative forecasting method, this study provides a comprehensive but straightforward econophysics approach to forecast new shipbuilding order of bulk carrier. The research result helps shipping investors make decision in a capital-intensive and uncertainty-prone environment.

Inventory management in food companies with statistically dependent demand

2016 ◽  
Vol 29 (4) ◽  
pp. 450-485 ◽  
Author(s):  
Fernando Rojas ◽  
Victor Leiva
Keyword(s):  
Inventory Management ◽  
Raw Materials ◽  
Moving Average ◽  
Differential Evolution Algorithm ◽  
Statistical Dependence ◽  
Food Services ◽  
Inventory Models ◽  
Content Type ◽  
Dependent Demand ◽  
Dependence Structures

Purpose The objective of this paper is to propose a methodology based on random demand inventory models and dependence structures for a set of raw materials, referred to as “components”, used by food services that produce food rations referred to as “menus”. Design/methodology/approach The contribution margins of food services that produce menus are optimised using random dependent demand inventory models. The statistical dependence between the demand for components and/or menus is incorporated into the model through the multivariate Gaussian (or normal) distribution. The contribution margins are optimised by using probabilistic inventory models for each component and stochastic programming with a differential evolution algorithm. Findings When compared to the non-optimised system previously used by the company, the (average) expected contribution margin increases by 18.32 per cent when using a continuous review inventory model for groceries and uniperiodic models for perishable components (optimised system). Research limitations/implications The multivariate modeling can be improved by using (a) other non-Gaussian (marginal) univariate probability distributions, by means of the copula method that considers more complex statistical dependence structures; (b) time-dependence, through autoregressive time-series structures and moving average; (c) random modelling of lead-time; and (d) demands for components with values equal to zero using zero-inflated or adjusted probability distribution. Practical implications Professional management of the supply chain allows the users to register data concerning component identification, demand, and stock levels to subsequently be used with the proposed methodology, which must be implemented computationally. Originality/value The proposed multivariate methodology allows it to describe demand dependence structures through inventory models applicable to components used to produce menus in food services.

Deep learning-based container throughput forecasting: a triple bottom line approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sonali Shankar ◽  
Sushil Punia ◽  
P. Vigneswara Ilavarasan
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Moving Average ◽  
Short Term ◽  
Bottom Line ◽  
Content Type ◽  
Autoregressive Integrated Moving Average ◽  
Forecasting Method ◽  
Memory Network ◽  
Long Short Term Memory

PurposeContainer throughput forecasting plays a pivotal role in strategic, tactical and operational level decision-making. The determination and analysis of the influencing factors of container throughput are observed to enhance the predicting accuracy. Therefore, for effective port planning and management, this study employs a deep learning-based method to forecast the container throughput while considering the influence of economic, environmental and social factors on throughput forecasting.Design/methodology/approachA novel multivariate container throughput forecasting method is proposed using long short-term memory network (LSTM). The external factors influencing container throughput, delineated using triple bottom line, are considered as an input to the forecasting method. The principal component analysis (PCA) is employed to reduce the redundancy of the input variables. The container throughput data of the Port of Los Angeles (PLA) is considered for empirical analysis. The forecasting accuracy of the proposed method is measured via an error matrix. The accuracy of the results is further substantiated by the Diebold-Mariano statistical test.FindingsThe result of the proposed method is benchmarked with vector autoregression (VAR), autoregressive integrated moving average (ARIMAX) and LSTM. It is observed that the proposed method outperforms other counterpart methods. Though PCA was not an integral part of the forecasting process, it facilitated the prediction by means of “less data, more accuracy.”Originality/valueA novel deep learning-based forecasting method is proposed to predict container throughput using a hybridized autoregressive integrated moving average with external factors model and long short-term memory network (ARIMAX-LSTM).

scholarly journals Peramalan Produksi Menggunakan Metode Autoregressive Integrated Moving Average (ARIMA) di PT. XYZ

2019 ◽  
Vol 2 (1) ◽  
pp. 27
Author(s):  
Mohammad Buchori ◽  
Tedjo Sukmono
Keyword(s):  
Raw Materials ◽  
Moving Average ◽  
Arima Model ◽  
Production Costs ◽  
Planning And Control ◽  
Forecasting Method ◽  
Time Series Method ◽  
Long Term Forecasting ◽  
Short Term Forecasting ◽  
And Control

In production planning and control the first step is to forecast to determine how much production, the company forecasting is still not optimal, because forecasting has an important role in a company. PT. XYZ is a food company that produces chicken meatballs and chicken dumplings. So from that this study uses the forecasting method Autoregressive Integreted Moving Average (ARIMA). ARIMA is often also called the Box-Jenkins time series method. ARIMA is very good for short-term forecasting, while for long-term forecasting the forecasting accuracy is not good. The purpose of this research is to get a good ARIMA model, used to forecast production in the company. So that the production becomes optimal and not excessive which can cause waste of raw materials, which will make production costs a lot. Data processing is done with the help of an Eviews computer program to determine a good ARIMA model, from processing data obtained by ARIMA (1.0,0). With the results obtained forecasting in the period 37 to period 48. 

scholarly journals Profil Industri Mebel Di Kelurahan Pasia Nan Tigo Kecamatan Koto Tangah Kota Padang

JURNAL BUANA ◽  
2019 ◽  
Vol 3 (1) ◽  
pp. 29
Author(s):  
Rizkamdial Aris ◽  
Fitriana Syahar
Keyword(s):  
Data Analysis ◽  
Large Scale ◽  
Raw Materials ◽  
Research Result ◽  
Raw Material ◽  
Scale Production ◽  
Furniture Industry ◽  
Production Factors ◽  
Analysis Technique ◽  
Quantitative Descriptive

The purpose of this study is (1) Learn about the profile of the furniture industry specifically on production factors in terms of capital, raw materials and labor. (2) Knowing the obstacles faced by industry. (3) Knowing the range of services area and marketing of the furniture industry. The method that used in this study is a Quantitative Descriptive method. Data collection methods used were questionnaires and research documentation. The data analysis technique used (1) descriptive data analysis (2) analysis of service area using buffer. Research result shows that the profile of the furniture industry in the production factors seen in terms of capital, raw materials and labor in Pasia Nan Tigo Village is only one industry that has capital constraints. In the case of raw materials, industry owners state that the raw material is still sufficient in the production process. The furniture industry owner also stated that the number of permanent workers is still insufficient. Furniture industry in Pasia Nan Tigo Village is included in the small and medium industry. The obstacles faced by the furniture industry in the production factors are capital, raw materials and labor. From the capital factor, the obstacles faced by furniture industry owners are large-scale production which still requires additional capital. In the case of raw materials the obstacles faced by the owners of the furniture industry are the distance from the origin of raw materials. In the range of services area, the furniture industry in Pasia Nan Tigo Village is on average within the province.

scholarly journals IMPLEMENTASI PERAMALAN PENJUALAN IKAN LAUT UNTUK OPTIMASI PERSEDIAAN BAHAN BAKU (Studi Kasus di UD Harum Bungah Gresik)

2021 ◽  
Vol 6 (2) ◽  
pp. 59
Author(s):  
Khanifatus Sa'diyah ◽  
Narto Narto
Keyword(s):  
Manufacturing Industry ◽  
Raw Materials ◽  
Moving Average ◽  
Demand Forecasting ◽  
Raw Material ◽  
Forecasting Method ◽  
National Economic ◽  
Marine Products ◽  
Sea Fish ◽  
Weighted Moving Average

Indonesian marine waters have high marine resource resources. One of Indonesia's seafood commodities is fish. With proper management and utilization, marine products become one of the promising business opportunities for the community, so that fisheries become one of the supporting sectors of national economic development. UD Harum is one of the businesses engaged in the fisheries sector as a supplier of marine fish raw material needs to meet the needs of the manufacturing industry. To optimize production planning to meet industry demand, forecasting of sea fish sales data forecasting in the previous period is needed to anticipate a shortage of raw materials. The purpose of this forecasting is to implement forecasting using the Single Moving Average (SMA), Weighted Moving Average (WMA) and Centered Moving Average (CMA) methods in forecasting sea fish sales at UD Harum and to find out the best forecasting results to increase sea fish sales at UD Harum. Forecasting results show forecasting using the Single Moving Average (3-monthly) and (5-monthly) methods respectively 8107.67 kg and 8399.4 kg. For the Weighted Moving Average (3-monthly) and (5-monthly) methods, the results of forecasting are 7268,963 kg and 7443,452, respectively. As for the Centered Moving Average (3-monthly) method with forecast results of 8107.67 kg. The forecasting method chosen to optimize sales is the Centered Moving Average method with a forecast value of 8107.67 kg and has the smallest forecasting error compared to other forecasting methods with a MAPE value of 0.30875 and MPE of -0.1720.

What caused the decrease in RevPAR during the recession?

2014 ◽  
Vol 26 (8) ◽  
pp. 1225-1242 ◽  
Author(s):  
Tianshu Zheng
Keyword(s):  
Moving Average ◽  
Lodging Industry ◽  
Future Market ◽  
Market Demand ◽  
Content Type ◽  
Hotel Performance ◽  
Analysis Technique ◽  
Daily Rate ◽  
The Us ◽  
The Usa

Purpose – This study aims to attempt to examine whether the increase in hotel room capacity in the USA had a significant impact on nationwide aggregated weekly revenue per available room (RevPAR) during the recession of 2007-2009 and forecast average RevPAR, Occupancy and Average Daily Rate (ADR) for 2013 and 2014. Design/methodology/approach – Using Autoregressive Integrated Moving Average with Intervention analysis technique, this study examined the significance of the fluctuations in weekly RevPAR, room capacity and market demand through the recent recession and forecasted hotel performance for 2013 and 2014. Findings – The results of time series analysis suggest that the fast growth of room capacity during the recession was one of the main causes of the decrease in RevPAR. The 9,878 more than expected increase in average weekly number of rooms probably caused at least $0.10 more than expected decrease in average weekly RevPAR. The findings of this study also suggest that the US lodging industry has been facing more severe oversupply since the recession and fully rebound of RevPAR cannot be expected in the very near future. Practical implications – The findings of this study will help stakeholders make more informed decisions to cope with possible future economic downturns. By quantifying the capacity increase and forecasting future market demand, this study provides hotel investors with empirical evidence on the overdevelopment and insights into expected overall hotel performance in next two years. This study has also discussed the cyclical patterns of hotel development during the past two recessions. Originality/value – By identifying overdevelopment as one of the main causes of RevPAR decrease during the recession, this study contributes to the literature by adding an alternative explanation of RevPAR fluctuations and deepens the understanding of the adverse effects overdevelopment has on the lodging industry. The findings of this study will help hotel investors develop more informed future expansion plans.

Application of lean practices in small and medium-sized food enterprises

2013 ◽  
Vol 116 (1) ◽  
pp. 125-141 ◽  
Author(s):  
Manoj Dora ◽  
Dirk Van Goubergen ◽  
Maneesh Kumar ◽  
Adrienn Molnar ◽  
Xavier Gellynck
Keyword(s):  
Food Processing ◽  
Lean Manufacturing ◽  
Raw Materials ◽  
Current Status ◽  
Food Sector ◽  
Statistical Process ◽  
Content Type ◽  
Lean Practices ◽  
The Status ◽  
Manufacturing Techniques

Purpose – Recent literature emphasizes the application of lean manufacturing practices to food processing industries in order to improve operational efficiency and productivity. Only a very limited number of studies have focused on the implementation of lean manufacturing practices within small and medium-sized enterprises (SMEs) operating in the food sector. The majority of these studies used the case study method and concentrated on individual lean manufacturing techniques geared towards resolving efficiency issues. This paper aims to analyze the status of the lean manufacturing practices and their benefits and barriers among European food processing SMEs. Design/methodology/approach – A structured questionnaire was developed to collect data. A total of 35 SMEs' representatives, mostly CEOs and operations managers, participated in the survey. The study investigated the role of two control variables in lean implementation: size of the company and country of origin. Findings – The findings show that lean manufacturing practice deployment in food processing SMEs is generally low and still evolving. However, some lean manufacturing practices are more prevalent than others; e.g. flow, pull and statistical process control are not widely used by the food processing SMEs, whereas total productive maintenance, employee involvement, and customer association are more widespread. The key barriers encountered by food SMEs in the implementation of lean manufacturing practices result from the special characteristics of the food sector, such as highly perishable products, complicated processing, extremely variable raw materials, recipes and unpredictable demand. In addition, lack of knowledge and resources makes it difficult for food processing SMEs to embark on the lean journey. Originality/value – The gap in the literature regarding the application of lean manufacturing in the food sector is identified and addressed in this study. The originality of this paper lies in analyzing the current status of the use of lean manufacturing practices among food SMEs in Europe and identifying potential barriers.

Loss reserve variability and loss reserve errors

2014 ◽  
Vol 15 (3) ◽  
pp. 248-263 ◽  
Author(s):  
Enoch Nii Boi Quaye ◽  
Charles Andoh ◽  
Anthony Q.Q. Aboagye
Keyword(s):  
Insurance Companies ◽  
Content Type ◽  
Main Research ◽  
Loss Reserve ◽  
Smooth Flow ◽  
Regulatory Standards ◽  
Sources Of Variation ◽  
Regulatory Bodies ◽  
Property And Liability Insurance ◽  
Industry Trends

Purpose – The purpose of this study is to assess the level and variability of Ghanaian property and liability insurer’s reserve estimates to examine its sources and ascertain if reserve errors are random or not (i.e. manipulated or not). Design/methodology/approach – It uses information on insurer claim reserve provisions, claims outstanding, claims incurred and claims paid for the period of 2000-2010. Categorizing the sources of variation as endogenous and exogenous, the authors use the panel correlated standard error regression model to determine sources and magnitude of industry reserve error. Findings – The study finds that size, age, lag of loss reserve error, inflation rate and real gross domestic product are significant in determining the degree of reserve error variation. Type of ownership (domestic or foreign) is, however, not a significant source of variation. Further, the authors found that industry reserve errors are random (not manipulated) across firms, suggesting that sampled insurers act independently on reserve error decision making and are not influenced by industry trends and competition. Research limitations/implications – The main research study limitation is the difficulty involved in obtaining annual statements from insurance companies in Ghana. Reluctance of companies to make statements available impeded on the smooth flow of the study during data collection. Practical implications – Policy-wise, this suggest that regulatory bodies can uniquely set reserve error levels for existing firms with little influence on competition. Further, the Ghanaian insurance regulator does not to focus on the type of ownership (foreign or local) when setting regulatory standards. However, size of the company and age (length of operation) should be considered. Originality/value – This paper is the first empirical study to examine the loss reserve error and loss reserve variability of Ghanaian property and liability insurance companies.

The international strength of the euro will grow

2021 ◽  
Keyword(s):  
Euro Area ◽  
Financial Services ◽  
Raw Materials ◽  
Domestic Demand ◽  
Content Type ◽  
The Us ◽  
Us Dollar ◽  
Strategic Autonomy ◽  
Geopolitical Risks ◽  
The Eu

Significance The proposals identified areas where the euro could potentially become more dominant, such as the issuance of green bonds, digital currencies, and international trade in raw materials and energy. Ambitions to enhance the international leverage of the euro are being driven by the aim to strengthen EU strategic autonomy amid rising geopolitical risks. Impacts Developing its digital finance sector would be an opportunity for the EU to enhance its strategic autonomy in financial services. Challenging the US dollar would require the euro-area to rebalance its economy away from foreign to domestic demand. Member state division will prevent the economic reconfiguration the euro-area needed to make the euro a truly global currency.

Vegan cheeses vs processed cheeses – traceability issues and monitoring countermeasures

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Moawiya A. Haddad ◽  
Sharaf S. Omar ◽  
Salvatore Parisi
Keyword(s):  
Food Supply ◽  
Design Methodology ◽  
Raw Materials ◽  
Production Method ◽  
Animal Origin ◽  
Information Load ◽  
Supply Chain Risk ◽  
Supply Networks ◽  
Content Type ◽  
Traceability System

PurposeThe purpose of this study comes from the need of defining improved durability values and the realization of a good traceability management for selected vegan cheeses has suggested the comparison between a processed cheese and its analogous version without animal-origin raw materials. The durability should be studied at a well-defined temperature, probably agreed among the food producer and the food processor. In addition, the traceability system should consider many components and related suppliers.Design/methodology/approachA supply chain risk assessment analysis has been carried out with relation to two different products: an analogue cheese and a vegan cheese-like preparation. Raw materials and ingredients have been evaluated (production method and origin; geographical identification), with the aim of identifying simplified food.FindingsAn assessment of food supply networks has been carried out. In the first situation (analogue cheeses), the ingredient “cheeses” shows an important complexity: five suppliers with a related six-interconnection hub. On the other side, vegan cheeses are obtained from 11 ingredients (a challenging hub); four of them may be produced from 2–5 components of different origin (five total hubs). Tested processed cheeses are represented by means of a linear food supply network with two hubs (cheeses and “arrival” show degrees 6 and 9, respectively). Networks concerning vegan cheeses include five different hubs: four complex raw materials (degree: 2, 3, 4 and 5) and the “arrival” step (degree: 12).Originality/valueThe information load of vegan cheeses (two hubs, degrees >> average degree) appears high if compared with processed cheeses (two hubs), although the complexity of networks appears similar. Vegan cheeses may seem technologically simpler than processed cheeses and be sometimes questioned because of important traceability issues. Adequate traceability countermeasures in terms of preventive monitoring actions should be recommended when speaking of vegan cheeses. Anyway, a centralized manager would be always required.

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