Forecasting and Analysis 1
Forecasting and Analysis
School of Business, Liberty University
Author Note
I have no known conflict of interest to disclose.
Correspondence concerning this article should be addressed to
Introduction
Having the correct workforce is key to operational success in service businesses,
particularly those that interact directly with customers, such as banks. At the Indiana University
Credit Union's Eastland Plaza Branch, manager James Chilton frequently ran into a problem: the
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number of teller positions did not match the number of customers. This suggested that
employees were either not working or were overwhelmed with customers. James struggled to
find the core causes and implement a data-driven solution since he knew nothing about
quantitative analysis. Fortunately, a year's worth of data on when customers came, as well as
characteristics such as the day of the week, paydays, and proximity to holidays, permitted the
use of forecasting methods. The purpose of this study is to develop prediction algorithms that
can accurately forecast how many clients will visit each day. This will help James ensure that he
has enough employees to meet the demand (Albright & Winston, 2020).
James Chilton oversaw Indiana University Credit Union's Eastland Plaza branch. He had
to deal with a reoccurring operational problem: staffing numbers that did not correspond to the
daily patterns of customer arrival. There were times when the business had too many
employees, causing staff to be idle and unproductive. At times, the store was overcrowded,
resulting in long lines and bad service. James felt he needed a more organized strategy, so he
looked for a reliable way to forecast how many clients would come in each day. This would allow
him to adjust the number of staff members required to meet those demands. Even though he
had no formal quantitative analytic knowledge, the data he had access to daily: customer
arrivals over the course of a year, as well as additional factors such as paydays, holidays, and the
day of the week was enough for establishing a solid foundation for predictive modeling (Albright
& Winston,
2020).
Analysis
To begin addressing the staffing problems at the Eastland Plaza Branch, a line chart was
created to show how many customers came in each day of the year (Figure 1). This figure shows
that traffic always increases at the same times, particularly on Fridays and around paydays. On
the other hand, traffic always decreases on Tuesdays and Wednesdays. These patterns indicate
an ongoing pattern in customer behavior over time, which can be used to make better staffing
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decisions. Figure 2 is a column chart showing how the average number of arrivals varies by day
of the week. This graph shows that Fridays remain the busiest days, followed by Mondays.
Tuesdays and Wednesdays are the least busy days. This information is needed to efficiently
schedule workers and ensure that the number of staff meets the estimated demand (Albright &
Winston, 2020).
To determine the impact of paydays, a bar chart was created, showing the average
number of arrivals on staff and teacher paydays against non-paydays (see Figure 3). The findings
show there are much more customers on paydays, making it even more necessary to have more
staff on those days. Similarly, a combination chart (see Figure 4) was used to investigate how
holidays influenced the number of customers that came in. There is always a lot of traffic in the
days before or after holidays, although not as much as on paydays (Albright & Winston, 2020).
These findings suggested a few staffing recommendations. Because there are so many
more clients on Fridays and paydays, the number of tellers should be increased by 30% to 50%.
Staffing levels might be reduced during the week, particularly on Tuesdays and Wednesdays,
lowering labor expenses while maintaining service levels. Mondays and close to holidays need
some additional staffing due to the increased business. Using a flexible schedule system, in
which teller shifts are changed every week depending on anticipated arrivals, would improve
the usage of resources and customer satisfaction (Albright & Winston, 2020).
Research
Recent research clearly supports James' idea of using forecasting tools carefully and
purposefully. According to Goodwin et al. (2023), prediction approaches have improved
significantly, particularly in terms of accuracy and adaptability, but many companies providing
services are still reluctant in using them. Their research, discovered that correct forecasts can
help with inventories, staffing, and customer service. However, many leaders are hesitant to use
them because they haven't had enough training or don't trust the models or because their
organizations don't want to change. Goodwin et al. (2023) utilized surveys and discussions to
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discover practical approaches to help businesses use forecasting more efficiently. These include
improved communication, more detailed explanations, and leadership support.
Liao and Batool (2025) go even further by showing how environmental, social, and
governance (ESG) goals can be directly supported by business analytics (BAC). They investigated
BAC in China's manufacturing and service industries and discovered that it improves flexibility in
organizations and new business development, both of which are important factors influencing
ESG performance. To put it another way, leaders who are adept at utilizing analytics can make
ethical and long-term decisions, respond faster to customer requests, and change the way they
do business. This reinforces the idea that forecasting involves more than just data collection; it is
also about strategy and values (Liao et al., 2025).
Albright and Winston's (2022) explain how to create predictions using regression and
time series models. They demonstrate how categorical elements, such as paydays, weekdays,
and yearly patterns, can be added to models to improve accuracy. Their work is invaluable to
service directors who need to ensure that personnel and resources are appropriate for real-
world demands. When leaders employ these skills, they can make more informed and
compassionate decisions. Making forecasts requires more than simply using a chart. When
paired with biblical principles such as compassion and service, it becomes a leadership tool that
enables organizations to grow in an honest and impactful manner.
Biblical Integration
It's not simply a technical exercise for James to learn how to solve staffing issues; it's also
an example of biblical compassion and servant leadership. When leaders deal with people and
money, the Bible instructs them to be cautious, wise, and caring. According to Proverbs 27:23,
“Be sure you know the condition of your flocks, give careful attention to your herds.” This verse
suggests that being attentive and responsible are the first steps toward being a successful
leader. This level of attention is shown by James' efforts to study customer patterns and make
staffing adjustments. He carefully watches over his “flock.”
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“Whatever you do, work at it with all your heart, as working for the Lord, not for human
masters,” Colossians 3:23 states. This means that even everyday business choices, like when to
schedule tellers, can be acts of worship if they are done with honor and greatness. James wants
to do a good job for his team and his customers because he wants to glorify God with his work.
When done well, forecasting is more than simply a business tool; it's a chance to help others.
James exemplifies biblical virtues such as kindness, order, and care by planning, reducing worker
stress, and improving the customer experience. His leadership shows how faith and facts can
come together to create a workplace that appears to have been created with God's care and
wisdom in mind.
Conclusion
It is important for service organizations, particularly those that deal directly with
customers, to always have an appropriate amount of people on hand. James Chilton's situation
at the Eastland Plaza Branch shows how a lack of personnel can result in challenges, frustration,
and wasted opportunities. But James found a path ahead by carefully observing how customers
arrived during the week, on paydays, and on holidays. James' approach shows that forecasting is
much more than data analysis, as proven by current research and Christian beliefs. It has to do
with making moral decisions that benefit everyone. James can save waste, increase service, and
care for his staff by combining forecasting techniques with flexible scheduling. He builds his
leadership on the Bible, transforming everyday duties into powerful statements of faith. This
case shows that leaders who use data with heart and knowledge do more than solve problems;
they create environments in which people can grow. That is how we should lead so that we can
honor
God and the people we serve.
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References
Albright, S. C., & Winston, W. L. (2020). Business Analytics: data analysis and decision making
(6th ed.). Cengage Learning.
Goodwin, P., Hoover, J., Makridakis, S., Petropoulos, F., & Tashman, L. (2023). Business
forecasting methods: Impressive advances, lagging implementation. PLoS ONE, 18(12),
e0295693. https://doi.org/10.1371/journal.pone.0295693
Liao, J., Batool, M., Kazmi, S. J. A., Feng, F., & Alzuman, A. (2025). Studying the nexus of
business analytics capabilities with ESG performance. How resource orchestration
capabilities, organizational agility, and business model innovation bridge this
relationship. International Entrepreneurship and Management Journal, 21(1).
https://doi.org/10.1007/s11365-025-01104-6
NIV Bible. (n.d.). YouVersion | the Bible App | Bible.com. Retrieved October 9, 2025, from
https://www.bible.com/bible
