Abstract Paper aims This study reviews the available literature regarding big data analytics applications in supply chain management and provides insight on topics that received a good deal of attention and topics that still require investigation. This review considers the expansion of big data analytics in supply chain management from 2010 to 2019. Originality Beyond displaying the increasing frequency of using big data analytics in supply chain management, the authors also aim to develop a useful categorization of applying business analytics in supply chain management and define opportunities for future research in the field. Research method This paper briefly discusses big data applications in supply chain management. Four common steps in review papers are performed: collecting articles (Thomson Reuters Web of Science), descriptive analysis, defining categories, and evaluating the material. Main findings According to both information technology development trends and the availability of data, more companies are using big data analytics in their supply chains. About 60% of the research on big data applications in supply chain management were published after 2017. These publications have increasingly focused on big data applications in predictive analysis, rather than in the other three types of data analysis: descriptive analysis, diagnostic analysis, and prescriptive analysis. Implications for theory and practice This review shows that the collected data by many companies can be analyzed using big data analytics methods to develop the business growth plan, market direction forecast, manufacturing process simulation, delivery optimization, inventory management, and marketing and sales processes, among many other activities in a supply chain. The number of articles using case studies in the literature is greater than the number of theoretical publications. This shows that big data analytics has now been properly developed for practical applications, rather than just being a theoretical concept.
Abstract Paper aims Mixing the Make-To-Stock (MTS) and Make-To-Order (MTO) strategies to benefit from the both manufacturing systems in an environment with impatient customers. Originality This is the first research article which uses the queuing theory to find the best place of the Order Penetration Point (OPP) in a production line with impatient customers. Research method Two scenarios are studied in this paper. 1- Semi-finished products are produced and stored in a buffer. No semi-finished product will be completed until a specific order comes to the system. This strategy leads to idle cost, but there is savings obtained in terms of eliminating investment in finished goods inventory and its holding cost. We can calculate the total cost of the system and find the optimal machine for the buffer of semi-finished products. 2- We use both MTS and MTO for completing the semi-finished products. When there is no customer in the system, semi-finished products are completed based on the MTS strategy and finished products are sent to a warehouse. But, when an order comes in for customization, a semi-finished product get assigned to that order and after finishing the current MTS job on each machine, this MTO job starts to complete the semi-finished product. To calculate system performance indexes, we use the Matrix Geometric Method (MGM) after modeling the systems with queuing theory concepts. Main findings Numerical examples show the convexity of total cost in terms of product completion percentage and number of customization lines after the OPP. Also, increasing the production rate leads to higher expected number of semi-finished products in the buffer. Implications for theory and practice Positioning OPP in manufacturing systems to compare different production strategies (MTS/MTO) has not been widely studied yet. This paper shows how manufacturing companies can apply the OPP to obtain benefits from both MTS and MTO strategies according to various cost parameters of the production lines. Using queuing theory concepts to model the problem under study helps to consider the external factors such as impatient customers and demand arrival uncertainty that can affect the performance measures of the system besides the internal factors such as production rate and inventory related costs. The idea of expanding the production line after a specific station and have more customization lines to improve customer satisfaction is studied in this paper as well.