Social Media Big Data Analytics for Demand Forecasting

Social media big data offers insights that can be used to make predictions of products' future demand and add value to the supply chain performance. The paper presents a framework for improvement of demand forecasting in a supply chain using social media data from Twitter and Facebook. The proposed framework uses sentiment, trend, and word analysis results from social media big data in an extended Bass emotion model along with predictive modelling on historical sales data to predict product demand. The forecasting framework is validated through a case study in a retail supply chain. It is concluded that the proposed framework for forecasting has a positive effect on improving accuracy of demand forecasting in a supply chain.

Reading Between the Stars: Understanding the Effects of Online Customer Reviews on Product Demand

Problem definition: Many studies have examined quantitative customer reviews (i.e., star ratings) and found them to be a reliable source of information that has a positive effect on product demand. Yet the effect of qualitative customer reviews (i.e., text reviews) on demand has been less thoroughly studied, and it is not known whether (or how) the sentiment expressed in text reviews moderates the influence of star ratings on product demand. We are therefore led to examine how the interplay between review sentiment and star ratings affects product demand. Academic/practical relevance: Consumer perceptions of product quality and how they are shared via customer reviews are of extreme relevance to the firm, but we still do not understand how product demand is affected by the quantitative and qualitative aspects of customer reviews. Our paper seeks to fill this critical gap in the literature by analyzing star ratings, the sentiment of customer reviews, and their interaction. Methodology: Using 2002–2013 data for the U.S. automobile market, we investigate empirically the impact of star ratings and review sentiment on product demand. Thus, we estimate an aggregated multinomial choice model after performing a machine learning–based sentiment analysis on the entire corpus of customer reviews included in our sample. We take advantage of a quasi-exogenous shock to establish a causal link between online reviews and product demand. Results: We find robust empirical evidence that (i) review sentiment and star ratings both have a decreasingly positive effect on product demand and (ii) the effect (on demand) of their interaction suggests that the two components of reviews are complements. Positive sentiments in text reviews increase the positive effect of ratings when the effect of ratings is decidedly positive while they also compensate for the tendency of consumers to discount extremely high star ratings. Managerial implications: The firm should pay greater attention to quantitative and qualitative customer reviews to better understand how consumers perceive the quality of its offerings.

Penerapan Metode Double Exponential Smoothing dan Moving Average pada Peramalan Permintaan Produk Gasket Cap di PT. Nesinak Industries

PT. Nesinak Industries is a company which focuses on the manufacturing process of an electronic component as well as automotive components (vehicle). In business activities, such as production, a strategy is required to survive in competition. Planning and forecasting are a strategy that can be implemented to accomplish these goals. In this study, the data used are previous sealing application data from January 2019 to March 2021. The objective of this study is to forecast product demand over the next period in order to be able to respond to customer demand. Data processing in this study utilize the Brown exponential double smoothing method and the moving average is then determined with the lowest MAPE (Mean Absolute Percentage Error) value to be used for the company’s product demand prediction calculations. The value of taken from Brown's exponential dual smoothing method is the value of with the two lowest error values from 0.1 to 0.9, whose value with the least error value is = 0.8 and = 0.9. In terms of the moving average method, the researchers tested a period of three months and a period of four months. In the MAPE calculation, the results of exponential double smoothing = 0.8 of 26.92 %, exponential double smoothing = 0.9 of 26.22 %, moving average n = 3 of 32.46%, and moving average n = 4 of 34.77%.

Manufacturing/remanufacturing based supply chain management under advertisements and carbon emission process

One of the most successful ways to get the word out about a product's popularity across all types of customers is through advertising. It has a valuable direct influence on increasing product demand. The supply chain model is developed for manufacturer and retailer, where advertisements are dependent on demand. The advertisement rate has been considered a function that has enhanced at a diminishing rate concerning time, although the growth rate slowed. During the manufacturing cycle, the market's demand is a function of advertisement, and the customer's demand is a linear function of time. The production rate exceeds the demand rate during manufacturing and remanufacturing; shortages are not faced. It involves a manufacturing/remanufacturing process that quickly delivers consumer products and less waste. To keep the environment clean, the cost of carbon emissions is incorporated into the manufacturer's and supplier's holding and degrading costs. The model's primary purpose is to minimize the overall cost of manufacturing and remanufacturing. The overall cost during the manufacturing cycle is higher than that during the remanufacturing cycle. This study confirms that the increasing cost of advertising provides the continuous increasing value of the total cost. A numerical example is provided, graphical representation and sensitivity analysis determine the function's behavior and test the model.

Demand Forecasting of The Automobile Sales Using Least Square, Single Exponential Smoothing and Double Exponential Smoothing

Sales strategies require the right managerial in marketing products with the development of technology and communication, the decision making in product sales supported by complete data and can be analyzed into intelligence business solutions. The research discussed and provided solutions about how to forecast future demand targets from a set of data history by making a predictive model of product demand in the real case. The research study was obtained from automobile sales, which the company probably set the strategy from the forecast result of automobile sales by the system in the future. The research used forecasting methods such as Least Square, Single Exponential Smoothing, and Double Exponential Smoothing to achieve a small percentage of prediction error. The dataset was collected from Mitsubishi Motors Corporation which obtained 60 samples of popular product types such as Pajero, FE and L300 from 2014 to 2018 over a period of months. The experimental results reported that Double Exponential Smoothing has given a better performance than other methods. The forecasting result of Pajero reached the MAPE of 3.26%, FE reached the MAPE of 3.24%, and L300 reached the MAPE of 3.37%. This study indicates that the selection of the forecasting method depends on the actual data pattern and the adjustment of the parameters in predicting future points.

Strategic Development and SWOT Analysis of Canting Batik Industry

The development of canting batik industry has lagged, and the number has decreased compared to the batik industry. This industry is believed to have a potential passive market and can provide job opportunities for local workers, reducing urbanization. Based on this background, this research is needed to analyze the potential and innovation of canting batik industry in Central Java, Indonesia. We choose Central Java, based on the fact that the batik industries grew most in Pekalongan, Central Java. The article uses qualitative research. We use Focus Group Discussion and direct observation to map the competitiveness of SMEs. To see the most significant opportunities and challenges, we also use a SWOT analysis. The results of this study indicate that the growth of the canting stamped batik industry is relatively slow. Some of its causes are relatively expensive raw materials, low product demand, and local government support. SME players can use some strategies to make canting as a souvenir product, looking for alternative ways to non-brass raw materials, and cooperating with complementary industries, including the batik industry.

SARIMA – A Model for Forecasting Product order demand

Product analysis is the most important part for any working manufacturing. It provides the sales record of their currently manufactured product and also it helps to predict its performance in the future. For this analysis, a SARIMAX model has been used with Time series forecasting. This paper will explain the need of such model instead of using a simple regression model to predict the order demand. This study analyses and presents a forecasting model to predict an order demand for the Product over the time period. Demand in Product is a main component for planning all processes in supply chain, and therefore determining Product demand is a great interest for supply chain. Mean forecasting for product order demand was carried out using SARIMA model, by using the past data from the period of 2011 to 2017. The model with the least value of Akaike Information Criterion (AIC) was selected as the appropriate model for forecasting mean Error. Test for normality of residuals were performed to see the adequacy of the chosen model. SARIMA (1, 1, 1) (0, 1, 1) (12) was selected as the best model for mean product order demand forecast. The results obtained will prove that the model could be utilized to forecast the future demand in the Product manufacturing industry. These results will help the manufacturers for manufacturing reliable guidelines in making decisions. Keywords: ARIMA, AIC, S-ARIMA, Regression

Research on Contract Coordination in the Manufacturing Supply Chain Given China’s Work Safety Constraints

With supply chain management’s increasing importance in work safety, this paper establishes the leading enterprise with core enterprises as work safety units. These guide the small and medium-sized enterprises within the supply chain to focus on improving work safety according to the leading position of the supply chain’s core enterprises. Therefore, the Stackelberg game model is applied to build and explain supply chain node-enterprises’ optimal centralized and decentralized operational decisions. This research was conducted in the context of enterprise work safety constraints’ influence on the manufacturing supply chain’s equilibrium results. It also reveals the necessity in supply chain node enterprises’ contract coordination design by comparing the two decision models’ equilibrium results. Ultimately, the manufacturing supply chain’s overall profit and work safety can reach a level that includes centralized decisions through revenue- and cost-sharing contracts. Furthermore, profits to the supply chain’s node enterprises also improve, and a Pareto optimality is achieved. An enlightened management demonstrates the importance of core enterprises’ leading position in the supply chain, and the supply chain node enterprises’ levels of work safety, product demand, and total profit can be promoted through revenue- and cost-sharing contracts.