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Mitigating the Supply Chain Wastages Using Blockchain Technology
Published in Arun Solanki, Vishal Jain, Loveleen Gaur, Applications of Blockchain and Big Iot Systems, 2023
Dhritiman Chanda, Shantashree Das, Nilanjan Mazumdar, D. Ghose
Blockchain’s most significant potential for delivering business value is in the manufacturing sector. Companies traditionally operate under a reactive model when it comes to manufacturing and replenishment. When the demand rises, they escalate the production and output, and when products go out of stock, they place the orders in fulfillment centers. This practice is highly inefficient as companies are always one step behind the market or customer demands. The demand is, however, continually fluctuating, and the number of SKUs (stock-keeping units) is increasing at an accelerating rate, and the lead times of manufacturing also vary automatically from one product to another [42]. The reactive approach of manufacturing leads to various demand-supply imbalances. As a result, companies either end up having excess inventory leading to massive carrying costs or end up having stock-outs leading to lost sales. Implementation of blockchain can significantly help reduce the imbalances and inabilities faced by the manufacturers [20]. The cornerstone to the blockchain technology is the fact that data flows in a seamless manner between all the parties involved in real-time. As a result, demand forecasting is done more accurately, leading to proactive planning for manufacturing and stock replenishment, instead of merely reacting to stock-outs. The companies can thus enhance revenue and profitability while eliminating the possibilities of carrying costs and lost sales.
Demand Management, Order Management, and Customer Service
Published in John M. Longshore, Angela L. Cheatham, Managing Logistics Systems, 2022
John M. Longshore, Angela L. Cheatham
Like any planning aspect of any business process, forecasting plays a crucial role in the smooth functioning of your end-to-end supply chain. Primarily, customer satisfaction relies on your forecasting abilities, the more accurate your production runs are the faster you will be able to fulfill orders and keep your customers happy. Balancing incoming supplies and outgoing orders reduces inventory cost; accurate supply chain forecasting enables better optimization of resources. The closer you can get production levels to capacity, the less machine and employee downtime encountered. Forecast accuracy also affects over and under-runs in production, lowering time and resource waste even further.
Forecasting Methods
Published in John G. Wensveen, Air Transportation, 2018
Every day, at all levels of management within all segments of the air transportation industry, decisions are made about what is likely to happen in the future. It has been said that business action taken today must be based on yesterday’s plan and tomorrow’s expectations. Call them expectations, predictions, projections—it all boils down to one thing, forecasting. Forecasting is the attempt to quantify demand in a future time period. Quantification can be in terms of either dollars, such as revenue, or some physical volume, such as revenue passenger miles (RPMs) or passenger enplanements. Plans for the future cannot be made without forecasting demand. Planning also plays an important role in any aviation enterprise, but it should not be confused with forecasting. Forecasting is predicting, projecting, or estimating some future volume or financial situation—matters mostly outside of management’s control. Planning, on the other hand, is concerned with setting objectives and goals and with developing alternative courses of action to reach them—matters generally within management’s control.
Forecasting in the fashion industry: a model for minimising supply-chain costs
Published in International Journal of Fashion Design, Technology and Education, 2023
Michal Koren, Matan Shnaiderman
Trend forecasting is significant both for matching the introduced collection to the market’s tastes and preferences and for adapting the supply of the produced goods to the demand. In terms of predicting consumer preferences and selecting items marketed to those preferences, items that are not sold during the season may be sold at the end of or after the season at a discount. Therefore, they are either sold at a loss to the retailer or not sold at all. Well-founded forecasts can prevent the costs associated with excess or lack of inventory. Additionally, forecasting is critical for enhancing production and procurement planning, lead times, and inventory management. Forecasting is a difficult task in the fashion industry due to the short life cycle of products and the high volatility of demand (Boone, Ganeshan, Jain, & Sanders, 2019; DuBreuil & Lu, 2020).
Machine learning demand forecasting and supply chain performance
Published in International Journal of Logistics Research and Applications, 2022
Environmental uncertainty is one of the main drivers of organisational design and performance (Galbraith 1973). A significant component of environmental uncertainty is demand uncertainty augmenting supply-demand mismatch risk. According to Milgrom and Roberts (1988), there exist two ways of accommodating with demand uncertainty in a production system: (1) inventory buffering (i.e. Make-to-Stock production process) and (2) obtaining customer information and better communication (i.e. Make-to-Order production process and substituting inventory by information). These two mechanisms are substitutes, and the companies by designing/redesigning the production process could determine the differentiation point. They could specify which portion of the production/distribution process should run using inventory and which portion by obtaining information from and communicating with customers (Lee 1996). Other than mechanisms in the supply side to accommodate demand uncertainty, firms could improve their demand forecasting capability to mitigate supply-demand mismatch and improve supply chain efficiency.
A new method of time series forecasting using intuitionistic fuzzy set based on average-length
Published in Journal of Industrial and Production Engineering, 2020
Surendra Singh Gautam, S. R. Singh
Forecasting is the process of making predictions of the future values based on past and present observations in the available data set. Time series forecasting plays everywhere a major role in human life, especially sales, finance, and in making future decisions such as Sensex forecasting, economics, sales forecasting, weather forecasting, production planning, and railway demand forecasting. The classical time series methods cannot deal with forecasting problems in which the observations of time series data are in the form of linguistic terms. In literature, various methods have been developed by several researchers in the field of forecasting such as fuzzy time series (FTS), artificial neural networks (ANNs), genetic algorithm (GO) and fuzzy inference systems (FISs). The FTS is a special kind of time series whose observations are fuzzy set or fuzzy number. Fuzzy set theory was first defined by Zadeh [1] and after that, Intuitionistic fuzzy sets (IFS) has been introduced by Atanassov [2], which is the extension of fuzzy set to make it more appropriate to deal with imprecision. The main advantage of IFS over fuzzy set is that IFS deals independently with the degree of membership and degree of non-membership grades of an element in the set with a scope of including non-determinacy factor.