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Question 1: Discuss several demand forecasting issues and possible ways of reducing the negative impacts associated with those issues?
Question 2: You are to choose a RECENT article related to one or more of the topics this week and create an initial post. Be sure to have a detailed assessment of your article and explain how it relates to this week's material.
Answer 1
Demand forecasting is done in a way to predict the future demand for the particular goods or services. So it is very crucial to have proper demand forecasting. As it requires a lot of technical and analyzing skill to predict the future demand for the particular product (Raza et al., 2015). It can be possible through analyzing the historical data or by just making a judgment or by analyzing the time series.
Several issues of demand forecasting: issues are related to money and sufficient budget and selecting the appropriate techniques of forecasting.
1. If demand forecasting is not taken seriously before the producing or manufacturing of the product, it may lead to no sell.
2. Demand forecasting is dependent on prediction and probability. With the ever-changing dimensions of the market, sometimes it is very difficult to say what may click with the customers. Sometimes what has worked for them, might not work for them this time.
3. overestimation and underestimated are 2 kinds of forecasting defects. Overestimation may lead to wastage of inventory wrong capacity planning and as a whole wastage of money. Underestimation, as we know, will lead to not meeting up the demands, and it will create dissatisfaction among the customers and loss of loyalty (Daganzo et al., 2014). So both ways, it is harmful.
Possible ways to reduce the negative impacts associated with such issues are:
1. Statistical tools and analysis must be used for forecasting. It involves the use of historical market research and thorough analysis which gives less scope for the gap between actual market and results. Making appropriate decisions based on the learning from the past market trend is a very important parameter to predict the future demands.
2. The decision maker should be open to changes. Sometimes the decision maker is too firm to accept change in the behavior of the customers. The product that has been working for an extended period of time might stop working for them on any given day and time. He/ She has to be open to understand and acknowledge these changes.
Several issues of demand forecasting: issues are related to money and sufficient budget and selecting the appropriate techniques of forecasting.
1. If demand forecasting is not taken seriously before the producing or manufacturing of the product, it may lead to no sell.
2. Demand forecasting is dependent on prediction and probability. With the ever-changing dimensions of the market, sometimes it is very difficult to say what may click with the customers. Sometimes what has worked for them, might not work for them this time.
3. overestimation and underestimated are 2 kinds of forecasting defects. Overestimation may lead to wastage of inventory wrong capacity planning and as a whole wastage of money. Underestimation, as we know, will lead to not meeting up the demands, and it will create dissatisfaction among the customers and loss of loyalty (Daganzo et al., 2014). So both ways, it is harmful.
Possible ways to reduce the negative impacts associated with such issues are:
1. Statistical tools and analysis must be used for forecasting. It involves the use of historical market research and thorough analysis which gives less scope for the gap between actual market and results. Making appropriate decisions based on the learning from the past market trend is a very important parameter to predict the future demands.
2. The decision maker should be open to changes. Sometimes the decision maker is too firm to accept change in the behavior of the customers. The product that has been working for an extended period of time might stop working for them on any given day and time. He/ She has to be open to understand and acknowledge these changes.
Answer 2
Digital supply chain: What’s Your Reality February 22, 2018, I have chosen article featured stories January 2018 Digital Supply Chain: What’s Your Reality.
In this article importance of Supply chain for every organization has been detailed. That is what does the digital supply chain mean for company It will create more integration and better visibility for the company which will lead it to have a competitive advantage.
As this article is related to the course week material that is it is also related to the demand forecasting managing the supply chain is the part of demand forecasting. Managing the supply chain will lead to having better customer services also so this article is related to both that is demand management and delivering the better customer services.
In this article, it has been detailed that many organization is using ERP and TMS software in managing the supply chain and adopting various digital software and platform to provide better services to the potential customer. So an organization can use the digital platform to have the strategic advantages in the ever-changing and dynamic environment.
In this article importance of Supply chain for every organization has been detailed. That is what does the digital supply chain mean for company It will create more integration and better visibility for the company which will lead it to have a competitive advantage.
As this article is related to the course week material that is it is also related to the demand forecasting managing the supply chain is the part of demand forecasting. Managing the supply chain will lead to having better customer services also so this article is related to both that is demand management and delivering the better customer services.
In this article, it has been detailed that many organization is using ERP and TMS software in managing the supply chain and adopting various digital software and platform to provide better services to the potential customer. So an organization can use the digital platform to have the strategic advantages in the ever-changing and dynamic environment.
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Raza, M. Q., & Khosravi, A. (2015). A review of artificial intelligence based load demand forecasting techniques for smart grid and buildings. Renewable and Sustainable Energy Reviews, 50, 1352-1372.
Daganzo, C. (2014). Multinomial probity: the theory and its application to demand forecasting. Elsevier.
Daganzo, C. (2014). Multinomial probity: the theory and its application to demand forecasting. Elsevier.
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