Demand Forecasting- Outline

Thesis Statement: Demand forecasting is pivotal in business planning for both tactical and strategic production and managerial decisions.

1. Introduction

1. Demand forecasting is a crucial element of decision-making for businesses regarding supply and estimation of sales.
2. Different forecasting techniques are used in demand forecasting and these will be discussed and applied in this paper.

2. Difference in Short and Long-term Forecasts

1. Demand forecasting could be short term or long-term depending on the objectives and scope of the forecast.
2. Therefore, while the two approaches focus on distinct managerial decisions, they are equally important in making decisions depending on predicted demand.

3. Forecasting Techniques

1. There are numerous forecasting techniques each with its benefits and challenges.
2. Qualitative forecasting methods include but are not limited to the Delphi method, market research, and sales team opinions.

4. Forecast Situation

1. An example of demand forecasting is in a car manufacturing company.
2. The two methods were selected because they are fairly reliable in this situation and also easy to use for short-term demand forecasting.

5. Conclusion

1. Demand forecasting is a crucial business process around which operational and strategic decisions are based.
2. Demand forecasting is important for business planning and managers should use it as an essential planning tool.

Demand Forecasting

Demand forecasting is a crucial element of decision-making for businesses regarding supply and estimation of sales. Forecasting applies concepts of predictive analysis to estimate customers’ future demand based on historical data and prevailing conditions. Through the process, businesses optimize inventory and can also control operating costs to maximize returns and business profitability. Different forecasting techniques are used in demand forecasting and these will be discussed and applied in this paper. Demand forecasting is pivotal in business planning for both tactical and strategic production and managerial decisions.

Difference in Short and Long-term Forecasts

Demand forecasting could be short term or long-term depending on the objectives and scope of the forecast. Short-term forecasting is often conducted for a period not longer than a year and is concerned with the ad hoc activities for day-to-day purchasing, pricing, sales, and financing decisions (Doganis, 2019). These are tactical decisions for short-term planning. On the other hand, long-term forecasting is used for crucial strategic decisions such as expanding production capacity, manpower planning, and introduction of new products in the market (Doganis, 2019). When compared to short-term forecasting, long-term forecasting presents a higher risk of inaccuracies due to the uncertainty of market trends. However, it is necessary to include consumer trends, emerging technologies, and market trends in determining investment and managerial decisions past the one-year mark. Therefore, while the two approaches focus on distinct managerial decisions, they are equally important in making decisions depending on predicted demand.

 

 

Forecasting Techniques

There are numerous forecasting techniques each with its benefits and challenges. One of the most common is the use of quantitative statistical analyses such as barometric and econometric models. Barometric forecasting uses past demand to predict future demand using various indicators while econometric forecasting uses aggregate time series among other current and past data with statistical equations to predict demand (Dwyer et al., 2020). Both approaches are examples of statistical forecasting which has various benefits and challenges. On the one hand, statistical models are effective in creating causal relationship assumptions and hence upholding logical consistency and coming across as highly reliable. The use of data in decision-making is especially crucial in making adjustments in the future. On the other hand, most statistical methods do not consider or work well with extreme changes in prices and trends in the market. Therefore, overreliance on data and overlooking expert judgment may lead to poor forecasting results.

Qualitative forecasting methods include but are not limited to the Delphi method, market research, and sales team opinions. In the Delphi method, a group of experts is brought together and each person independently produces a forecast and presents it. The experts are influenced by each other’s opinion and they repeat the process until near-consensus is reached (Moon, 2018). While the method is important in capturing expert opinion, it also presents the risk of group-think which is harmful to making informed predictions and decisions (Moon, 2018). In market research, customer surveys are conducted. The main benefit is that the method captures customer needs and integrates them in demand forecasting. Elsewhere, the approach presents many sources of bias including social desirability and neutral responding which may misrepresent the actual demand and lead to inaccuracies (Moon, 2018). Lastly, the sales-team opinion technique is the simplest in that each sales representative evaluates their products and regions and makes a recommendation to the manager. The manager aggregates the forecast information from each and makes a final forecast. This method is fairly easy to implement. However, it heavily relies on each team member’s expertise and skills. Therefore, it has a risk of low reliability. While qualitative forecasts introduce expert judgment in demand forecasting, they present high levels of biases compared to statistical models.

Forecast Situation

An example of demand forecasting is in a car manufacturing company. The manufacturer has three cars, A, B, and C. In the last 12 months, sales for A have increased by 12%, B remained the same, and C declined by 5%. A market survey shows that A is preferred by more market segments than B and C for its fuel-efficiency and reliability. One of the prevailing public conversations is in reducing fuel consumption for environmental sustainability. Based on these conditions and assessment, the sales manager predicts that A will sell more in the next 12 months and more investment should be on production line for A with lower investment on C. The method used in this demand forecasting is a combination of market surveys and sales team opinion. Using market surveys has allowed the sales manager to understand past trends. The sales manager further uses their judgment based on prevailing social trends and patterns to forecast how consumers might behave in the future. The two methods were selected because they are fairly reliable in this situation and also easy to use for short-term demand forecasting.

Conclusion

Demand forecasting is a crucial business process around which operational and strategic decisions are based. Short-term forecasting paves the way for tactical decisions while long-term forecasting supports strategic planning. Different approaches include quantitative which mainly use statistical equations and qualitative which rely on judgment. Choosing a reliable and feasible forecasting method is crucial in boosting accuracy of the forecasting process. Demand forecasting is important for business planning and managers should use it as an essential planning tool.