Demand Forecasting Issues

The assignment discusses several demand forecasting issues that can negatively impact a business. These issues include not taking demand forecasting seriously, overestimation and underestimation of demands, and dependence on prediction and probability. To reduce these negative impacts, the article suggests using statistical tools and analysis to forecast demand, being open to changes in customer behavior, and managing the supply chain effectively.

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Question 1 Discuss several demand forecasting issues and possible ways of reducing the
negative impacts associated with those issues?
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.

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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

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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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References
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.