Category: Forecasting

Research Objective

The research objective is to identify what issues the company needs to address to improve the quality of its service operations. An additional target is to identify effective methods for forecasting service demand for B2B and B2C segments and understand the differences between the two sectors. The second objective is subordinate to the first, as it may address one of the issues causing the service quality underperformance.

Final Report Audience

The final report’s audience will be the company’s management, which will receive suggestions regarding how service may be improved. They expect to see a detailed analysis of the situation that leads to logical conclusions on how the problem may be remedied. Additionally, they would like to see two forecasting models formulated, one for B2B service and another for B2C, with the differences explained.

Main Research Questions

There are five research questions, which are presented below:

1. Do the employees in the service department lack the skills they need to perform operations?
2. Does the service department have adequate workforce to satisfy service demand?
3. Are the demand forecasting models currently used for service accurate?
4. What are the determinants of service demand for businesses that work with Computer Inc.?
5. What are the determinants of service demand for Computer Inc.’s customers?

Dependent and Independent Variables

The dependent variable would be the proportion of service demand that is met. Meanwhile, the independent variables are the number of service sector employees and their competency levels.

Causal Model and Regression Equation

Service Department Performance = α + β1 (number of employees) * β2 (employee skill level) + β3 (forecasting effectiveness) + e

Data Collection Methods

Customers would be surveyed about their issues, and companies would be asked to provide data about issues with their devices. Employees would be interviewed in an open form with questions prepared in advance about their concerns.

Sampling Frame

The study’s research audience will be its customers, both individuals and businesses. Twenty companies will be contacted and asked about their service needs, and a market research firm will be hired to study the B2C market, as, for forecasting, it is necessary to encompass as many people as possible (Schindler, 2019). An additional audience will be Computer Inc. employees in the service department, all of whom will be included in the study provided they agree.

Collected Data ANOVA Test

The ANOVA test can be used for groupings of companies that work with Computers Inc. and expect it to provide service. They can be separated based on their industries, and the researchers will try to determine whether these differences inform variety in electronic device usage.

Potential Areas of Error

The first area of error can be sampling bias for partner companies, as it may not necessarily be representative of the whole. The second would be interview design, as it may include questions with an implicit bias. The third is the social desirability bias, as employees may avoid giving answers that may be seen as critical of the company. Finally, the fourth would be measurement bias when evaluating employee abilities to provide care.

Questions to Ask

1. Do the service department workers have enough resources to fulfill service requests?
2. Do you get assigned tasks that you are not qualified for often?
3. Have you received any advanced training or opportunities to participate in it from the company?
4. Do you experience pressure to finish tasks quickly and proceed to the next one often?

Reference

Schindler, P. S. (2019). Business research methods (13^th ed.). McGraw-Hill Education.

Gnomial Functions Inc. wants to determine the most reliable forecasting technique for determining the sales level for the next eighteen months. The sales trend reflected a consistent increase over the last 18 months with minimal seasonal variation. However, the sales for recent months reflected had more significance on the following months sales (Wisniewski, 2016). Consequently, Gnomial Functions Inc. should use the weighted moving average to forecast the most likely level of sales.

The weighted moving average used four weights to determine the likely level of sales in the next month. These weights ranged from 0.1 to 0.4 for the earliest and most recent months respectively. The most recent data received a higher weight due to its higher relevance to the future level of sales. Consequently, the weighted moving average generates accurate forecast compared to the simple moving averages.

The company can realize even more accurate forecast by adjusting the weights for the moving average. The management should give higher weight to the recent monthly sales to reduce the forecasting error. Specifically, the mean absolute deviation and mean standard error for the initial weights (0.1, 0.2, 0.3, and 0.4) was 6.27 and 47.29. In contrast, when the new weights that give more importance to the recent performance (0, 0, 0.4, and 0.6) are applied, the mean absolute deviation declined to 4.81, and the mean standard error declines to 27.28. This performance is better than the simple moving average.

Gnomial Functions Inc. should use the weighted moving average to determine the likely level of sales in the next year. This method is simple, effective, and reliable. In addition, it can be adjusted to reflect the changes in sales trend by adjusting the weights to give the requisite importance to the recent months. Therefore, the company should use weighted moving average to estimate the likely performance in the coming year.

Reference

Wisniewski, M., 2016. Quantitative methods for decision makers. Pearson.

Introduction

Best Homes is a company that builds and sells new houses in various areas of the US. Starting Business in 1945, they have been able to expand from the East Coast to Midwest, West Coast and even the south. Their pricing and housing quality ranges greatly, allowing all kinds of individuals to purchase housing that conforms to their income level. Housing is a field that directly deals with one of the most vital needs of individuals – living space. Despite the population numbers continuously expanding, construction companies have to consider both positive and negative changes in demand during their work. The ability of individuals to purchase homes, as well as their willingness to spend a considerable portion of their income on a home depends largely on present political and economic circumstances (Rodrigues et al., 2020). Furthermore, the quality, placement, and management of housing can affect the willingness of people to buy it.

As a result, construction companies have to consider and manage multiple factors of influence, many of which are outside of their immediate control. In a modern competitive landscape, organizations cannot afford to make mistakes, or lose profits. Failing to consider how outside factors influence one’s business means opening it up for potential instability, which can quickly wear down even the most profitable organizations. For this reason, companies in the housing business have to engage in forecasting. The term refers to the practice of predicting future company metrics and business-related events depending on existing evidence. Forecasting can concern a singular organization, an industry, or the entire economy. With the availability of data from Best Homes’ storied past, it is possible to use forecasting to enhance the organizations’ capabilities. This work will focus on discussing potentially effective forecasting methods for Best Homes, as well as applying some of them using the data presented in the case study.

Forecasting Suggestions

The primary purpose of prediction methods is not to determine the exact demand, the number of products sold, or other indicators. Forecasting is always wrong; the probability of predicting anything in business up to the last digit is almost zero; in this regard, indicators of forecast error are introduced (Schroeder, 2020). Forecasts are based on the need to make various decisions that will have an effect on the future. According to these decisions, the degree of influence, and calculation methods, these methods are classified into several groups. Such a classification is needed to adapt forecasts for various applied problems.

The fundamental division of forecasting methods is quantitative and qualitative approaches. Quantitative ones consider analytical indicators with which it is possible to perform mathematical and statistical calculations. These approaches include the Best Homes method, which works with sales statistics for the last five years and by region to obtain an excellent forecast. Of the advantages of this method, straightforward solutions stand out, of the disadvantages – the need for interpretation and non-obvious dependence on external factors. Demand regulators in the real estate market can include various mechanisms from global economic situations to geopolitical situations that are not subject to the company’s influence (Kim et al., 2020; Gaca, 2019). However, at Best Homes, this quantitative method worked well, driving the company’s sales.

Qualitative methods are ranked according to the different tasks they are aimed at in organizations. In the case of Best Homes, the company needs to pay attention to these approaches since, at this stage, they are not yet implemented in the organization. These methods are more versatile and provide more flexible mechanisms for working with customers, sales, and products but cannot give accurate quantitative estimates (Schroeder, 2020). Best Homes may consider using the life-cycle analog, Delphi, and market surveys (Schroeder, 2020). The life-cycle analogy will allow each product to be viewed through the prize of its life cycle, making it possible to implement in historical sales data and data by region the most frequent factors influencing external and internal factors on the purchase of a house. In addition, this method is designed for a long sales cycle, which contains the construction and further sale of a house (Schroeder, 2020). Market surveys are more difficult to implement, but they can clarify such points as the solvency of the target client group, key aspects when choosing real estate, and the needs of various client groups for their further segmentation.

This approach will be appropriate within a specific region and sales period. Customers in surveys will be able to clarify the reasons for seasonal demand with the region’s specifics. In the long run, this approach loses its effectiveness (Schroeder, 2020). In this regard, Best Homes may implement a similar forecasting method before launching a new development project in a particular region. With data coming from customers, Best Homes can improve the life-cycle analog and have a practical but partial interpretation of quantitative forecasting methods. Finally, one of the longest-running but most detailed quantitative methods, Delphi, can help a company solve complex issues, usually related to external factors. Technological development has picked up a fast pace and now needs to match this pace to remain competitive in terms of resources such as quality and build time (Ullah et al., 2018). A group of experts within this approach can express an outside point of view, enabling Best Homes to find a solution to a problem or a vector for the nearest development. Implementing all three methods at once may be too resource-intensive for Best Homes. On the other hand, the company already has experience in implementing several forecasting methods at once to solve the internal problems of the organization.

Forecast Decomposition

Table 1. Decomposition of Best Homes Sales 2016

The decomposition method in sales forecasting is usually applied to monthly or quarterly data when the seasonal nature of demand is evident and when the manager wants to forecast sales for a year and smaller periods. It is essential to determine when the change in sales reflects general, fundamental processes and when it is associated with the seasonality of demand. Just as the demand for sunscreen increases significantly in the regions during the sunniest months, the real estate market has its mechanisms of seasonal demand. Table 1 provides Best Homes 2016 sales to find the average monthly demand for that year. As a rule, such an analysis is carried out for a more significant number of years to identify the seasonality factor to take into account its quantitative indicators for future years.

The nature of changes in the real estate market can be different. First, Best Homes’ trend of increasing sales contributes to a gradual and long-term growth rate. Secondly, seasonality reflects the fluctuations in the time series associated with the change of seasons. This factor usually appears the same every year, although the exact sales pattern may vary from year to year. Thirdly, cyclicity as a factor is not always present since this factor reflects ups and downs with the exclusion of seasonal and erratic fluctuations. These ups and downs usually occur over a long period, perhaps two to five years. New buildings are just included in the goods group that are subject to similar dynamics (Ionașcu et al., 2020). Finally, a random factor is singled out – a component that remains after excluding the trend, cyclicality, and seasonal factor.

Regional Sales Projections Role

These forecasts for the geographic location of new buildings may reflect the influence of factors that are difficult to consider or are not entirely taken into account in the sales statistics of previous years. These include the opening of a new production facility in the region and the influx of people looking for new housing. In addition, regional estimates can show the solvency of the target audience of customers in terms of the number of sales. Based on these data, the company can conclude the geographic location of the launch of a new project. In addition, the scale of such regional assessments is essential since even within the same city, depending on factors such as infrastructure and distance from the center, the price of equally equipped houses can change significantly. Finally, these forecasts will be combined with historical statistical data and the work of the HR department, which must seek and hire experts to build a house according to a particular established algorithm.

Conclusion

This paper provides an analysis of Best Homes in the context of business development planning. The main forecasting methods were considered, their pros and cons were given as part of the implementation to this organization. Regional differentiation and the decomposition method are used as tools for estimating future sales from existing information. As a result, data was obtained for forecasts that are specific to this business and, in particular, to Best Homes.

References

Gaca, R. (2019). Price as a measure of market value on the real estate market.Real Estate Management and Valuation, 26(4), 68-77. Web.

Ionașcu, E., Mironiuc, M., Anghel, I., & Huian, M. C. (2020). The Involvement of Real Estate Companies in Sustainable Development—An Analysis from the SDGs Reporting Perspective.Sustainability, 12(3), 798. Web.

Kim, Y., Choi, S., & Yi, M. Y. (2020). Applying comparable sales method to the automated estimation of real estate prices. Sustainability, 12(14), 5679.

Rodrigues, P., Lourenço, R., & Hill, R. (2020). House price forecasting and uncertainty: Examining Portugal and Spain.Banco de Portugal. Web.

Schroeder, R. G. (2020). Operations management in the supply chain: decisions and cases. McGraw-Hill US Higher Ed USE. Web.

Ullah, F., Sepasgozar, S. M., & Wang, C. (2018). A systematic review of smart real estate technology: Drivers of, and barriers to, the use of digital disruptive technologies and online platforms. Sustainability, 10(9), 3142. Web.

Inventory management and demand forecasting are fundamental to supply chain management. Good integration of these two tasks is critical to organizational performance (Balachandra et al., 2020). Within this context, forecasting approaches range from qualitative techniques through time series analysis and, eventually, informal models. However, qualitative and causal forecasts are considered to be ineffective as inputs to inventory and scheduling decisions.

An effective forecasting process allows the company to prepare for all possibilities, estimate the likelihood of attaining forecast values, and assess the risk to the organization of failing to achieve them. In inventory management, it is necessary to assess demand uncertainty, which is often represented in estimates of demand throughout the lead time period (Kourentzes et al., 2020). Predictions in qualitative demand forecasting are based on professional knowledge of how the market functions (Chapman, 2021). Meanwhile, causal forecasting incorporates the past and considers relationships and unique events (Diezhandino, 2022). The ineffectiveness of these approaches in inventory and scheduling decisions can be described by their inability to determine exact data. In other words, as only assumptions can be projected in these approaches, it will not help with inventory and scheduling decisions. Moreover, because it takes a long time to analyze these forecasts and find answers, they cannot be used as inputs for inventory and scheduling choices. Additionally, the data obtained must rely on each other when individual data collection is not possible, so data analysis using the forecast technique cannot be used as an input to inventory.

Forecasting is essential for planning, scheduling, and enhancing supply chain coordination. Proper demand forecasting gives businesses vital information about their prospects in their present and other markets, allowing managers to make educated pricing, corporate expansion strategies, and market potential decisions. However, qualitative and casual forecasting are not useful as inputs to inventory and scheduling management mainly because they cannot predict the exact data and it takes a long time to analyze the obtained information.

References

Balachandra, K., Perera, H., N., & Thibbotuwawa, A. (2020). Human factor in forecasting and behavioral inventory decisions: A system dynamics perspective. Proceedings of the 7^th International Conference, 516-526.

Chapman, M. (2021). An introduction to quantitative & qualitative inventory forecasting models.Eazystock. Web.

Diezhandino, E. (2022). Importance and benefits of forecasting customer demand.Keepler. Web.

Kourentzes, N., Trapero, J., R., & Barrow, D., K. ( 2020). Optimising forecasting models for inventory planning. International Journal of Production Economics, 225.

The quantitative elements selected include revenue generated per year by Apple Inc. and the total number of stores the company has in total. Apple Inc. is planning to unveil new products in the market, such as the fifth generation iPad, which uses 5G and M1 cheap, and the third generation iPhone SE, which uses an A15 chip with a 5G network. The company’s expansion requires insight into future demand to ensure that the company prepares well for the upcoming changes which may affect the supply chain, products, and the company finance.

Subjective Data and Actual Demand Outcome

Subjective data is based on other people’s opinions, feelings or attitudes on a particular topic. This data is essential for Apple Inc. as it will enable the company to identify areas that require improvement. Apple Inc.’s market share is smaller than its main competitor, Samsung (Dea, 2022). In 2021 the company had a total of 511 stores worldwide with a revenue generation of $365,817 million, implying that the increase in the number of stores has a positive impact on Apple Inc.’s revenue growth. Figure 1.0 below Apple Inc.’s revenue generation in the last seven years.

Forecasting Method

The operation plan uses regression analysis as the preferred forecasting method. The regression model predicts Apple Inc.’s revenue based on parameter analysis. This method only applies to quantitative data where dependent (Yi) and independent variables (Xi) are present. This method shows the relationship between two variables i.e. dependent and independent. The regression model used in this analysis is Y_i = β₀ + βx_i + e_i. In this analysis, the Y_i is the revenue forecasted by the independent variable xi that represents the number of stores.

Trends and Seasonality

The trend in the Apple Inc. data shows an increasing revenue as the number of stores also increases. For the last five years, Apple Inc. has been experiencing an upward trend in its revenue. For example, in 2017, the company’s revenue was $233,715.00, and in 2021 it was $365,817.00. The seasonality is portrayed by the smooth jump in Apple revenue. This implies that increasing the number of Apple stores has a positive impact on the sales of Apple Inc. products.

Future Demand Forecasting

The data below shows the actual data of Apple Inc. from 2015 to 2021 and the regression analysis for the predicted demand forecast.

Table 1.0: Apple Inc. stores and revenue

Decisions

Based on the regression analysis results, Apple Inc. needs to open new retail stores to increase its revenue. The forecast in figure 2.0 above shows that the company’s revenue will increase with an increased number of retail stores. This will enable the company to compete effectively with its competitors, such as Samsung. Adding more retail stores promotes Apple Inc.’s supply chain, enabling them to serve offline clients.

References

Apple Income Statement 2009-2022 | AAPL. (2022). Macrotrends. Web.

Dea, S. (2022). global-market-share-of-apple-iphone. Statista. Web.

The oil industry has kept a prominent place in the world’s economics for over a century. Sales forecasting in this field has remained somewhat stable and has developed a number of reliable methods. Nevertheless, the changing trends and permanent conditions of economic mechanisms significantly influence oil sales forecasting. This paper will observe how the law of supply and demand and financial market trends impact sales forecasting for oil in the world.

The first factor affecting sales forecasting for the oil industry is based on the fundamental rule of supply and demand that defines the prices on the market and interactions between sellers and buyers. The paper by Arezki et al. (2017) has confirmed that the depletion of oil sources results in supply shortages and an increase in prices. The paper also comments on the possibility of new oil sources being discovered, immediately affecting the demand and supply ratio (Arezki et al., 2017). With this in mind, it is possible to alter sales forecasts based on the information about newly found or depleted oil fields when considering them as suppliers.

The other key element that influences oil sales is its relationship with financial market trends. According to Miao, Ramchander, Wang, and Yang (2017), three elements correlate with oil prices: U.S. interest rates, exchange rates, and the stock market. Since sales forecasting is directly dependent on global oil prices, all of those factors are widely used to obtain short-term prospects. For example, Miao et al. (2017) explain that due to producers trying to maintain the same purchasing power of the export revenue, the weakening of the U.S. dollar would lead to increased oil prices. Also, one could predict reduced demand for oil in countries whose currencies are dependent on the U.S. dollar, illustrating how financial market trends can be used in oil sales forecasting.

To summarize, several global factors have an impact on oil sales. This paper has observed two of them: the depletion of oil sources which activates supply shortage and higher demand, and the changes in the financial market, which forced producers to raise export prices. With the information on just these two elements of sales forecasting, one is able to develop a strategy for understanding and predicting oil price variations.

References

Arezki, R., Jakab, Z., Laxton, D., Matsumoto, A., Nurbekyan, A., Wang, H., & Yao, Z. (2017). Oil Prices and the Global Economy. International Monetary Fund. Web.

Miao, H., Ramchander, S., Wang, T., & Yang, D. (2017). Influential Factors in Crude Oil Price Forecasting. Energy Economics, 68. Web.

Abstract

The users (both internal and external) of financial information are always concerned about the future holding of the firm that what has occurred in the past because the past performance and occurrence are not capable to change the history. Here financial forecasting is a good technique and can be used for planning and budgeting purpose. The forecast offers to the user, to predict or expect an output based on historical performance, data, and facts.

Role of financial forecasting

The success of a firm depends much upon the forecasting function. Especially public limited companies make their budget and plan based on prediction and budgeted expectations. However, here are some roles which forecasting plays:

1. A financial forecast says or identifies how much finance (both internal and external) and assets are needed.
2. By the financial process and organization can set up its goals and estimate cost.
3. A financial forecast helps the firm to find a way how to meet the different needs.
4. It points to the interaction among the different elements in the organization.
5. Financial forecast helps the company to decide on which finance option will be favorable and which will not.
6. Financial forecasting also helps the company to avoid unexpected situations or surprises because it incorporates interest rate and inflation/deflation rate.

Forecasting mainly answers the question of five “WH” questions; these are:

• How many profits will the firm make
• What is the demand level of the product or service
• How much per unit cost for the product and services
• How much many the company need and how much to borrow
• How long the borrowed firm can be used and when it will be repaid

Importance of forecasting in an organization

Financial forecasting is now a vital fact for an organization. Bushman (12 April 2007) has mentioned several importance of forecasts, these are:

• Financial forecasting enables management to change operations at a right time to reap the greatest benefit.
• It says the company what inventories are needed to reach their business targets.
• It is important when a firm goes for launching new products or services.
• Forecasting saves time and money which a company may spend on developing, marketing a product or service which likely to fail.

Forecasting process

Forecasting is a continuous and integrated process. Forecasting is the continually changeable thing that depends on the impact of the actual performance of measurement and it also is updated and modified when it is needed. Forecasting is not a vanishing point of measurement of performance.

The integrated process is shown in figure:

Financial Planning

Financial planning is like the blood circulation for an organization. It works as a roadmaps and guides control and coordinates the overall actions of an organization. However financial planning is concerned with two main aspects like:

1. Cash planning
2. Profit planning

Cash planning is concerned with preparing the cash budget whereas profit planning is concerned with preparing a Pro-forma income statement. Cash budget and Pro-forma income statement, both are used for internal financial planning. Generally, a budget is used to implement as a statement of an organization’s preplanned cash inflows and put flows. The budget is also important for the project. The cash is needed for the short term and also pay attention to cash surplus or cash shortage. The study intends to show the pro forma of the income statement that was taken from ST 3-3 from the book Principles of Managerial Finance (Gitman, 2005, p.129).

Requirement

Preparation of Pro-forma income statement and comments:

Comment

The ‘percent-of-sales’ method may underestimate actual ‘2004 pro forma income’ by assuming that all costs are variable. If the firm has fixed costs, which by definition would not increase with increasing sales, the 2004 pro forma income would probably be underestimated.

Conclusion

Around ninety percent of managerial decisions are taken based on forecasts. Every decision taken by the managers comes to operation in course of time that is why the decision should be taken on the forecast of the future situation. Finally, no organization can run properly in the present condition without forecasting, because it is vital in every shape of an organization.

References

Arsham, Hossein. (1994). Time-Critical Decision Making for Business Administration. In Chapter-2. 9^th Edition. Web.

Bushman, Melissa. (2007). Why forecasting is important to an organization. Associated Content. Web.

Gitman, J. Lawrence. (2005). Principles of Managerial Finance. 11th edition, Addison Wesley.

All Business, (n.d.). Differences between Common and Preferred Stock. Web.

Bizterms.net. (n.d.). Risk-Adjusted Return on Capital (RAROC). Web.

Center for Private Equity and Entrepreneurship. (n.d.). Private Equity Glossary. The Trustees of Dartmouth collage.

Cooper. N. Richard (November 2005). Living with Global Imbalances: A Contrarian View. Policy Briefs in International Economics. I-10.

Gitman, J. Lawrence. (2005). Principles of Managerial Finance. 11 edi., Addison Wesley.

Harvey, R. Campbell. (2003). Financial Glossary. Web.

Islam. M. N. Sardar., and Watanapalachaikul, Sethapong. (2004). Empirical Finance: Modelling and Analysis of Emerging Financial. Physica-Verlag Heidelberg.

MiMi.hu. (n.d.). Financial institution. Web.

Rose, S. Peter., and Marquis, H. Milton (2006). Money and Capital Markets: Financial Institutions and Instruments in a Global Marketplace. 9th edition. McGraw-Hill Irwin.

Introduction

BDA, which refers to big data analytics, is receiving much more attention in the supply chain management field. The reason for this is that BDA has a variety of applications in the mentioned field, which include trend analysis, customer behavior analysis, and demand prediction (Seyedan & Mafakheri, 2020). The paper reports on a survey that was conducted to examine the predictive BDA applications in supply chain demand forecasting to suggest a categorization of the applications. The authors have classified the algorithms and the applications in SCM into time time-series forecasting, K-nearest neighbors, clustering, neural networks, support vector regression, regression analysis as well as support vector machines. They also point to the idea that the literature is specifically insufficient on the applications of big data analytics: in the event of closed-loop supply chains and accordingly point out the avenues for future study.

Demand forecasting is an area in predictive analytics that attempts to comprehend as well as foresee consumer demand to optimize supply decisions by corporate supply chain plus business management. The methods used in this procedure are broken into qualitative and quantitative. The two methods are based on experts’ opinions and historical sales information. This procedure can be utilized in inventory management, production planning, as well as assessment of future capacity requirements.

Research Conducted

This is a survey that aims to examine predictive big data analytics: in supply chain demand forecasting to recommend or suggest a categorization of the applications. The survey is conducted to help identify the gaps in the available literature as well as offer knowledge for future studies.

Journal Background & Literature Survey

Currently, businesses are adopting ever-increasing accuracy promotion efforts to maintain competitiveness plus grow or maintain their profit margin. Forecasting models have been utilized in accuracy promotion to identify as well as fulfill the customer’s needs plus preferences using predictions obtained from customer information as well as transaction records to manage products SC accordingly. Demand forecasting refers to predicting the demand of materials to ensure the right products are delivered in the ideal quantity to fulfill customer demands without creating a leftover. Errors in forecasting can lead to leftover or excess, which is not only wasteful but also costly (Seyedan & Mafakheri, 2020). The process is essential to the supply chain since it provides helps in developing operational strategies. It is an underlying hypothesis for strategic business processes plus the start point for the majority of the supply chain activities such as purchasing, raw material planning, inbound logistics, and manufacturing. Demand forecasting also enables critical business tasks such as financial planning, risks assessment, production planning, plus the purchase of raw materials.

Forecast accuracy enables retailers to avoid overstocking and stock-outs, better the production lead times, reduce costs, improve operational efficiency as well as better the customer experience. It is essential to understand that sales forecasting is more than utilizing sales data from before to establish customer demand in the present or future (Seyedan & Mafakheri, 2020). The procedure can be divided into quantitative and qualitative forecasting, both dependent on various resources as well as data sets to infer valuable sales data. The former method is utilized in the case of available historical sales information on particular items plus a pre-determined demand. It needs mathematical formulae as well as data sets such as sales, fiscal reports, website analytics, and revenue figures (Nunes et al., 2020). Qualitative forecasting depends on new technologies, product lifecycle, pricing and availability changes, product upgrades, and the experience and intuition of individuals responsible for forecast planning.

Findings and Conclusions

A key finding from the review of the available literature is that there is a very preliminary study done on the big data analytics applications in reverse logistics and CLSC. There are benefits as a result of adopting a knowledge-driven strategy for design plus management of CLSCs. Because of the increasing awareness of the environment as well as incentives from governments, a tremendous amount of returned items are collected, received, and organized in various points of collection. The uncertainties have a direct influence on the cost-efficiency of manufacturing procedures, the last price of the refurbished products, plus their demand. The design, as well as operation of CLSCs, brings out a case for BDA from both the demand as well as supply forecasting standpoints.

The increasing need for analyzing consumer behavior as well as demand prediction is fueled by globalization plus a rise in market competition and the surge in supply chain digitization practices. The authors conducted a detailed review of the big data analytics applications in supply chain demand prediction in the study. They overviewed the big data analytics methods used and offered a comparative classification. They collected and evaluated the available studies regarding the procedures and mechanisms utilized in demand forecasting. Several mainstream mechanisms were found and researched with their advantages and disadvantages. The NN (neural networks), as well as regression analysis, are viewed as the two mainly used mechanisms (Law et al., 2019). The analysis also discovered that optimization models could be applied in improving forecasting precision via formulating as well as optimizing a cost function for the fitting of the forecasts to data.

Summary of Learning

The journal enables a reader to understand that the data available in the supply chains contain valuable information. Evaluation of such information can allow forecast trends of markets, customer behavior, as well as prices. This can be helpful to organizations as it can facilitate their adapting to competitive surroundings (Nunes et al., 2020). To predict demand in a supply chain, various predictive big data analytics algorithms have to be applied. The algorithms could offer predictive analytics using time-series strategies, associative forecasting methods as well as auto-regressive methods. The forecasts from the algorithms could be incorporated with item design attributes and online search traffic mapping to combine both price and customer information.

The journal also provides knowledge concerning forecast accuracy, which allows a retailer to avoid issues of overstocking and stock-outs. It also helps them better their production lead times, reduces costs, improves operational efficiency as well as better the consumer experience (Law et al., 2019). It shows that it is critical to understand that sales prediction is more than utilizing sales data from before to decide on consumer demand in the current time or future (Nunes et al., 2020). The authors also relay that the procedure of demand forecasting can be grouped into quantitative and qualitative forecasting, both being dependent on various resources as well as data sets to infer valuable sales data. The former method is utilized in the case of available historical sales information on particular items plus a pre-determined demand. At the same time, the latter is dependent on new technologies and product lifecycle.

References

Law, R., Li, G., Fong, D. K. C., & Han, X. (2019). Tourism demand forecasting: A deep learning approach.Annals of Tourism Research, 75, 410–423. Web.

Nunes, L., Causer, T., & Ciolkosz, D. (2020). Biomass for energy: A review on supply chain management models. Renewable and Sustainable Energy Reviews, 120, 109658. Web.

Seyedan, M., & Mafakheri, F. (2020). Predictive big data analytics for supply chain demand forecasting: Methods, applications, and research opportunities. Journal of Big Data, 7(1). Web.

Introduction

• In the business world, firms activities are normally based on risks and uncertainties present in the market.
• Therefore, to reduce the risk that comes with these uncertainties, organizations use various forecasting models to determine the status of their future operations.
• Forecasts are applied to various aspects of the business, for example in production, sales, market share, budgeting ,and research and development.
• There are different types of forecasting methods that can be used for different areas of the business.
• The type of forecasting method to be used depends on the type of business organization and the stage of the product life cycle.
• The forecasting techniques range from simple to highly complex methods.

Forecasting forms the basis for planning and budgeting in any business organization. It is also necessary for capital expenditure analysis and analysis of mergers and acquisitions.

When choosing forecasting methods, one has to consider factors like preparation time, costs involved, accuracy required and the duration of the forecast.

Types of Forecasting Methods

• There are two broad categories of forecasting methods; qualitative and quantitative. (Taylor, 2010)
• Qualitative methods are judgmental in their approach.
• They are subjective- represent the opinions of experts or the general public.
• Applicable in situations where there is no past data.
• Appropriate for short and intermediate term forecasts.
• Can be used to supplement the projections made using any of the quantitative methods.
• Quantitative methods use mathematics and are based on past data.
• They are objective in their approach.
• Are consistent as long as there is quantifiable data.
• Their consistency also relies on the external environmental remaining unchanged (Taylor, 2010).

Qualitative forecasting methods are, in essence, educated guesses. Relies on the professional opinions of experts, or the general opinions of the consumers to predict future markets.

Quantitative methods use a time series model in which a forecast is generated from a time series data, or a causal model that explores the cause-and-effect relationship.

Qualitative methods (Examples)

1. Executive Opinions;
2. Delphi Method;
3. Sales-Force Polling;
4. Market Research/Consumer Surveys.

These are the most common qualitative methods that are used by many business organizations.

Executive Opinions

• Involves a group of industry experts coming together to discuss and come up with forecasts.
• The experts are drawn from various departments like production, sales, finance and administration (Madura, 2010).
• This method is quick and easy, no elaborate data to apply.
• Valuable when there is inadequate data.
• The downside is the possibility of “group think”.

When a group meets, there are inherent problems that it is faced with. High cohesiveness stifles any dissent, strong leadership pushes of unanimity of opinions, and insulation of the group shields them from external opinion. All these can lead to “group think”.

Delphi Method

• Industry experts are questioned individually rather than meeting in a group.
• This eliminates the chances of dominant personality factors fueling consensus (Granger & Timmermann, 2006).
• This method is very effective when long-range forecasts are needed.
• The advantage is that it might be very hard to find consensus.

The results from the individual interviews are analyzed and grouped. The questions are reformulated and posted back to the experts. This is repeated in a bid to find consensus amongst the experts.

Sales-force Polling

• Relies on the insight of salespeople who are in continual contact with the customers.
• Their closeness to customers is believed to give them a good opportunity to be able predict future markets.
• There is the risk of the sales people being overly optimistic or pessimistic in their predictions.
• The sales people may not be aware of larger economic events beyond their control.

Sales people are in direct contact with the consumers on the ground. They listen to their opinions and concerns, and are therefore well placed to gather data from them.

Market Research/Consumer Surveys

• Businesses conduct surveys amongst their customer base to get their opinions on various products or services.
• Data is collected through personal interviews, questionnaires, emails or phone calls.
• The results of the surveys are analyzed and presented using various statistical methods.
• Valuable for getting direct feedback from the customers themselves.

This method is very good for collecting data about specific products. Usually gives good insight into how consumers regard the products.

Quantitative Methods (Examples)

Time series methods

1. Naïve methods;
2. Moving average;
3. Trend analysis;
4. Exponential smoothing;
5. Decomposition of time series.

Associative (causal) forecasts

1. Simple regression;
2. Multiple regression;
3. Econometric modeling.

These are some of the most commonly applied quantitative methods of forecasting.

Naïve Methods

• Based on the assumption that the forecasts for a given period are equal to the actual values for the previous period.
• Used as the basis for comparisons with more sophisticated methods.
• The most cost-effective of the forecasting methods (Madura, 2010).
• Highly efficient and objective.
• Appropriate for level patterns.

Naïve methods usually need to be used alongside other more complex methods to generate better forecasts.

Moving Average

• Different subsets of the full data set are created and their averages taken.
• The line that results after the connection of all fixed averages is referred to as the moving average.
• Ideal for time series with a slowly changing mean.
• Used for projecting long-term trends or cycles.
• Also applicable in smoothing out short-term fluctuations.

The average of the first subset is taken, then for the second subset, the first value of the first subset is dropped, and the next value after the first subset is included and the average taken. The trend is repeated for all the values in the series.

Trend Analysis

• Used to describe the characteristics and behavior of observed data (Granger & Timmermann, 2006).
• Requires data that is in the form of a time series.
• Demonstrates whether a set of data show an increasing or decreasing pattern over time.

Can be used to make forecasts over a long period of time as long as there is adequate past data.

Exponential Smoothing

• The whole past is considered but more emphasis is put on recent data than on the less recent data.
• Involve simple calculations. Only the estimates for the previous period and current data are needed to calculate the new estimates (Roper & Wiley, 2011).
• Ideal for a time series that has a slowly changing mean.

If there is no forecast for the last period, the naïve method is applied. Alternatively, an average of the last few periods is taken.

Simple Regression

• Uses one explanatory variable to predict a scalar dependent variable.
• Linear models are used in the making of predictions (Peters, 2009).
• The models are fitted using least squares methods.
• The predictor variables are assumed to be fixed values and not random variables.

Correlation coefficient (r) measures the direction and strength of the linear relationship between two variables. The closer the r value is to 1.0 the better the regression line fits the data points.

Multiple Regression

• Uses multiple independent / predictor variables to describe a dependent variable.
• Assumes that the relationship between the variables is linear.
• Also assumes that the residual values are distributed normally.
• Can only be use to indicate relationships but cannot demonstrate the causal mechanism.

It is just an extension of linear regression but there are multiple independent variables involved.

Econometric Modeling

• Used to predict future developments of the economy.
• Involves analysis of past relationships of economic variables like interest rates, unemployment, inflation and household incomes (Roper & Wiley, 2011).
• Used to forecast how changes in certain variables will impact on the other variables.

These models are essential for economic planning. The model can predict growth or a recession so the authorities know what measures to take.

Choosing the right forecasting method

• Consider the amount and type of data available. Some methods require more data than others.
• What is the degree of accuracy required? More data means increased accuracy.
• Look at the duration of the forecast. Use a method that will give you the most accurate results over that time duration.
• Look out for data patterns. If you use a forecasting method meant for linear patterns on trends, it will cause lagging (Shim & Siegel, 1999).

Many business organizations employ more than one forecast method to any set of given data to enhance the chances of getting more accurate predictions.

Measuring the accuracy of forecasting methods

• Mean Absolute Deviation (MAD)- Used to measure the total error in a forecast without considering the sign.
• Cumulative Forecast Error (CFE)- Used for measuring any biases in a forecast.
• Mean Square Error (MSE)- Used for highlighting larger errors.
• Tracking Signal- Used for testing if the model is working.

The most accurate results are seen with situations where there is adequate data and the duration of the prediction is short.

Conclusion

• All the functional departments of a business organization require forecasting for better management (Shim & Siegel, 1999).
• Generally, forecasting methods can be grouped into:
  • Qualitative methods;
  • Quantitative methods.
• The qualitative methods are based on subjective opinions while the quantitative methods are based on mathematical models.
• The various forecasting models are suited for different situations. You have to choose the right forecasting method for any given situation for accuracy.

References

Elliott, G., Granger, C. W. J., & Timmermann, A. (2006). Handbook of economic forecasting. Amsterdam: Elsevier North-Holland.

Madura, J. (2010). International Financial Management. Australia: South-Western Cengage Learning.

Peters, M. (2009). Business Analysis. New York: Sage

Roper, A. T., & Wiley InterScience (Online service). (2011). Forecasting and Management of Technology. Hoboken, N.J: John Wiley & Sons.

Shim, J. K., & Siegel, J. G. (1999). Operations Management. Hauppauge, NY: Barron’s Educational Series.

Taylor, B. M. (2010). Introduction to Management Science. Upper Saddle River, NJ: Pearson/Prentice Hall.

Introduction

In recent times, the business environment has increasingly become more unpredictable. This has made it very important for organizations across the globe to become vigilant when it comes to the issue of forecasting. In fact, for any organization to be successful in today’s business world, its methods of predicting the future in the key areas of its main business has to be improved continually. Otherwise, it faces the threat of becoming obsolete. Forecasting is therefore, a tool to be highly appreciated by the businesspersons of this century.

Forecasting is therefore the process of estimating or predicting the future outcome of different business aspects by use of historical data. These business aspects include; sales, revenue, market share, profits, expenses and many more. Forecasting is formed from two words, “fore”-which implies forward or the future- and “cast”-which implies to shape. In other words, forecasting implies the process of shaping the future (Taylor).

Sales forecasting is among the most basic aspects of a business entity. This is mainly because different departments of the business entity, namely the manufacturing unit, the sales and marketing unit, human resource and finance departments, feel its effect. The sales forecast helps the manufacturing unit of the business in determining the amount of the product in question to produce in a given time.

The same forecasts enable the human resource department to determine the amount of labor to hire/employ. The finance department also uses them in planning the allocation of funds. The top management uses the sales forecasts in setting both short-term and long-term strategies. It is therefore paramount for any business entity to have accurate sales forecasts.

Factors affecting forecasting of sales

Internal factors

These factors may arise from within the organization. Firstly, laborers may present problems from time to time and as a result hinder the smooth flow of production. Consequently, this may affect the amount of sales made seeing, as there may be shortages in production (lost demand).

Secondly, new products present the hardest task in predicting their sales. This is mainly so because they do not have past data/records of past sales. Estimating these kinds of sales solely relies on guesses and hope. Finally, organizations keep changing their production capacity e.g. through automation of process and expansion of the plant and workforces. These actions render previous sales forecasts useless and new forecasts have to be made (Hiller and Hiller,25).

External factors

These factors emanate from outside the organization. Firstly, the ever-changing consumers’ tastes and preferences present a problem that requires the constant revision of the sales forecasts. Secondly, the volatile nature of the world economy has become a hurdle in the recent past in the business of forecasting. This is because it heavily affects the consumers’ spending power and in return affects the amount and type of products the purchase.

Thirdly, the increased competition in almost all areas of business demands that sales forecasts be constantly adjusted in order to better place the organization strategically. Finally, today’s businesses are mostly if not entirely seasonal. This attributable to the fast evolving technology that is the driving force of most businesses. Consequently, sales forecasts have to be made limited to a given timeframe and with allowances to changes (Anderson and Sweeny,90).

Importance of sales forecasting to decision-making process

It is suicidal for any organization to ignore sales forecasting. It would mean that the organization fails to plan. Consequently, the organization will be going into the future blind as a bat. Sales forecasting enables preparation in production in order to meet the estimated demand. This could be done through processes such as; increased marketing activities to reach potential customers, plant expansion to increase capacity, technological update etc.

Secondly, sale forecasting brings to the attention of the management past failures and successes therefore, providing an opportunity for the organization to correct their mistakes and to maximize on their strengths. This may be achieved through SWOT analysis where the organization establishes its strengths, weaknesses, opportunities, and threats. Through SWOT analysis, the organization can forecast its sales based on its strengths and opportunities dealing with its weaknesses and avoiding the eminent threats.

Thirdly, sales forecasting plays a vital role in enabling an organization to maintain constant cash flows. This is important in that it helps the organization remain relevant in the industry it is involved. Sales forecasts are used to estimate and map out future business activities of the organization. This includes covering unforeseen increases and decreases in demand and allowances for seasonal business activities. With the right sales forecasts, the organization is able to cushion itself against such shocks and therefore continue in business.

Thirdly, sales forecasting plays a vital role in enabling an organization to maintain constant cash flows. This is important in that it helps the organization remain relevant in the industry it is involved. Sales forecasts are used to estimate and map out future business activities of the organization. This includes covering unforeseen increases and decreases in demand and allowances for seasonal business activities. With the right sales forecasts, the organization is able to cushion itself against such shocks and therefore continue in business.

Moreover, sales forecasting helps in achieving cost efficiency. It enable the production department to know what amount of the product is to be required at every one time and can therefore employ special methods such as Just-in-Time method of inventory control. Consequently, the organization is able to save both time and cost. The effects of cost efficiency are felt in both the statement of financial position and the statement of income (Render, Stair and Hanna,105).

Finally, sales forecasting enables the firm to achieve customer satisfaction and hence customer loyalty. Due to the effects of sales forecasts on the production process, the organization is able to meet lead time/delivery time. The result is greater customer satisfaction and customer loyalty.

Sales forecasting methods

There are three main methods of forecasting namely,

• Time series models, causal models and qualitative models

Time series models

Time series models are quantitative methods. They employ past records in the determination of future forecasts. They work under the assumption that the future is not so different from the past.

Moving averages

It is mainly applicable in areas where the future is expected to be fairly even over a period. In this method, the averages of past data are found by simply adding the total sale of the periods and dividing by the number of the periods. If it is in months, one adds the total of latest month and subtracts that of the earliest month then divides with the number of the months. With time, the averages become predictable.

Exponential smoothing

It is an example of moving averages method but in this case the parameters may be more than one and the averages use an exponential formula as opposed to simple averages in the standard moving averages method.

Time series also involves trend projection method and decomposition method. It is also applicable in sales forecasts that are affected by seasons. With a seasonality index, one is able to produce forecasts that sensitive to recurring seasons.

Example:

Al Etisal distribution co. is one of the famous food and consumer goods Distribution Company in Baghdad. They have a wider range of products. Prince ice cream is one of their products that they start selling since 2009. Sales have a steady growth and its seasons have a significant impact on the ice cream sales. Management expects total sales for 2012 to be 3200.

Price ice cream sales unit management is required to set their forecast for 2012. They should find the best way in estimating the demand for the ice cream.

Below are the historical sales data for Prince ice cream for the last three years

The above example is highly affected by seasons and therefore the sales forecasts have to incorporate the seasons in their formulation. It is also an example of a quantitative problem. Therefore, time series is applicable in this problem specifically trend projection and by use of a seasonality index (Agee,258).

Average monthly sales = total average sales/ 12 months= 2229/12 =186

Seasonal index = average 3 years sales/ average monthly sales

Causal models

These models employ the use regression models to forecast sales. They come up with a list of variables that have effect on the sales of the product in question and through regression; they plot the various possibilities and therefore come up with reliable forecasts.

Qualitative models

Delphi method uses the views of various professionals or experts in the field who analyse the situation and provide their professional views. Normally, the group of experts includes; key decision makers, staff, and the respondents. The staff and respondents provide assistance based on their areas of expertise to the decision makers, who in turn come up with the forecasts.

Jury of executive methods uses the opinions of a jury made up of high-level managers and key decision makers to make forecasts. The group may however receive support from other technical professionals who provide background information to assist in decision-making.

Other qualitative methods include; sales force composite and consumer market survey. The latter uses consumer opinions while the former employs the opinions of the salespersons to come up with sales forecasts (Pinney,56).

Conclusion

In summary, sales forecasting is an important ingredient to success in the current and future business world. Therefore, management has to put emphasis on it to reap the benefits tied to the application of these tools. Management has to keep improving their approach to this process to remain relevant.

Works Cited

Agee, Marvin H. Quantitative Analysis for Management Decisions. London: Prentice Hall, 2001.

Anderson, David R and Dennis Sweeny. Quantitative Methods for Business. Chicago: South-Western College, 2009.

Hiller, Fredrick S and Mark S Hiller. Introduction to Management Science: A Modeling and Case Studies Approach with Spreadsheets. London: McGraw-Hill Higher Education, 2010.

Pinney, William E. Management Science: An Introduction to Quantitative Analysis for Management. Toronto: Harpercollins College , 2000.

Render, Barry, Ralph M Stair and Michael E Hanna. Quantitative Analysis for Management. London: Prentice Hall, 2011.

Taylor, Bernard R. Introduction to Management Science. London: Prentice Hall, 2009.