assignment 3 forecasting

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3 Forecasting

Learning Objectives

• What is forecasting and why is it important?
• Understand the differences between qualitative and quantitative forecasting.
• Describe types of demand patterns exhibited in product demand.
• Calculate forecasts using time series analysis and seasonal index.
• Determine forecast accuracy.

Forecasting  is the process of making predictions of the future based on past and present data. This is most commonly by analysis of trends. A commonplace example might be estimation of some variable of interest at some specified future date. Prediction is a similar, but more general term. Both might refer to formal statistical methods employing time series, cross-sectional or longitudinal data, or alternatively to less formal judgmental methods. Usage can differ between areas of application: for example, in hydrology, the terms “forecast” and “forecasting” are sometimes reserved for estimates of values at certain specific future times, while the term “prediction” is used for more general estimates, such as the number of times floods will occur over a long period.

Risk and uncertainty are central to forecasting and prediction; it is generally considered good practice to indicate the degree of uncertainty attached to specific forecasts. In any case, the data must be up to date in order for the forecast to be as accurate as possible. In some cases, the data used to predict the variable of interest is itself forecasted. [1]

As discussed in the previous chapter, functional strategies need to be aligned and supportive to the higher level corporate strategy of the organization. One of these functional areas is marketing. Creating marketing strategy is not a single event, nor is the implementation of marketing strategy something only the marketing department has to worry about.

When the strategy is implemented, the rest of the company must be poised to deal with the consequences. An important component in this implementation is the sales forecast , which is the estimate of how much the company will actually sell. The rest of the company must then be geared up (or down) to meet that demand. In this module, we explore forecasting in more detail, as there are many choices that can be made in developing a forecast.

Accuracy is important when it comes to forecasts. If executives overestimate the demand for a product, the company could end up spending money on manufacturing, distribution, and servicing activities it won’t need. Data Impact, a software developer, recently overestimated the demand for one of its new products. Because the sales of the product didn’t meet projections, Data Impact lacked the cash available to pay its vendors, utility providers, and others. Employees had to be terminated in many areas of the firm to trim costs.

Underestimating demand can be just as devastating. When a company introduces a new product, it launches marketing and sales campaigns to create demand for it. But if the company isn’t ready to deliver the amount of the product the market demands, then other competitors can steal sales the firm might otherwise have captured. Sony’s inability to deliver the e-Reader in sufficient numbers made Amazon’s Kindle more readily accepted in the market; other features then gave the Kindle an advantage that Sony is finding difficult to overcome.

The firm has to do more than just forecast the company’s sales. The process can be complex, because how much the company can sell will depend on many factors such as how much the product will cost, how competitors will react, and so forth. Each of these factors has to be taken into account in order to determine how much the company is likely to sell. As factors change, the forecast has to change as well. Thus, a sales forecast is actually a composite of a number of estimates and has to be dynamic as those other estimates change.

A common first step is to determine market potential, or total industry-wide sales expected in a particular product category for the time period of interest. (The time period of interest might be the coming year, quarter, month, or some other time period.) Some marketing research companies, such as Nielsen, Gartner, and others, estimate the market potential for various products and then sell that research to companies that produce those products.

Once the firm has an idea of the market potential, the company’s sales potential can be estimated. A firm’s sales potential is the maximum total revenue it hopes to generate from a product or the number of units of it the company can hope to sell. The sales potential for the product is typically represented as a percentage of its market potential and equivalent to the company’s estimated maximum market share for the time period.  In your budget, you’ll want to forecast the revenues earned from the product against the market potential, as well as against the product’s costs. [2]

Forecasting Horizons

Long term forecasting tends to be completed at high levels in the organization. The time frame is generally considered longer than 2 years into the future. Detailed knowledge about the products and markets are required due to the high degree of uncertainty.  This is commonly the case with new products entering the market, emerging new technologies and opening new facilities. Often no historical data is available.

Medium term forecasting tends to be several months up to 2 years into the future and is referred to as intermediate term. Both quantitative and qualitative forecasting may be used in this time frame.

Short term forecasting is daily up to months in the future. These forecasts are used for operational decision making such as inventory planning, ordering and scheduling of the workforce. Usually quantitative methods such as time series analysis are used in this time frame.

Categories of Forecasting Methods

Qualitative forecasting.

Qualitative forecasting techniques are subjective, based on the opinion and judgment of consumers and experts; they are appropriate when past data are not available. They are usually applied to intermediate- or long-range decisions.

In the following, we discuss some examples of qualitative forecasting techniques:

Executive Judgement (Top Down)

Groups of high-level executives will often assume responsibility for the forecast. They will collaborate to examine market data and look at future trends for the business. Often, they will use statistical models as well as market experts to arrive at a forecast.

Sales Force Opinions (Bottom up)

The sales force in a business are those persons most close to the customers. Their opinions are of high value. Often the sales force personnel are asked to give their future projections for their area or territory. Once all of those are reviewed, they may be combined to form an overall forecast for district or region.

Delphi Method

This method was created by the Rand Corporation in the 1950s. A group of experts are recruited to participate in a forecast. The administrator of the forecast will send out a series of questionnaires and ask for inputs and justifications. These responses will be collated and sent out again to allow respondents to evaluate and adjust their answers.  A key aspect of the Delphi method is that the responses are anonymous, respondents do not have any knowledge about what information has come from which sources. That permits all of the opinions to be given equal consideration. The set of questionnaires will go back and forth multiple times until a forecast is agreed upon.

Market Surveys

Some organizations will employ market research firms to solicit information from consumers regarding opinions on products and future purchasing plans.

Quantitative Forecasting

Quantitative forecasting models are used to forecast future data as a function of past data. They are appropriate to use when past numerical data is available and when it is reasonable to assume that some of the patterns in the data are expected to continue into the future. These methods are usually applied to short- or intermediate-range decisions. Some examples of quantitative forecasting methods are causal (econometric) forecasting methods, last period demand (naïve), simple and weighted N-Period moving averages and simple exponential smoothing, which are categorizes as time-series methods. Quantitative forecasting models are often judged against each other by comparing their accuracy performance measures. Some of these measures include Mean Absolute Deviation (MAD), Mean Squared Error (MSE), and Mean Absolute Percentage Error (MAPE).

We will elaborate on some of these forecasting methods and the accuracy measure in the following sections. [3]

Causal (Econometric) Forecasting Methods (Degree)

Some forecasting methods try to identify the underlying factors that might influence the variable that is being forecast. For example, including information about climate patterns might improve the ability of a model to predict umbrella sales. Forecasting models often take account of regular seasonal variations. In addition to climate, such variations can also be due to holidays and customs: for example, one might predict that sales of college football apparel will be higher during the football season than during the off-season.

Several informal methods used in causal forecasting do not rely solely on the output of mathematical algorithms, but instead use the judgment of the forecaster. Some forecasts take account of past relationships between variables: if one variable has, for example, been approximately linearly related to another for a long period of time, it may be appropriate to extrapolate such a relationship into the future, without necessarily understanding the reasons for the relationship.

One of the most famous causal models is regression analysis . In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships among variables. It includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables (or ‘predictors’). More specifically, regression analysis helps one understand how the typical value of the dependent variable (or ‘criterion variable’) changes when any one of the independent variables is varied, while the other independent variables are held fixed.

Common Forecasting Assumptions: 

• Forecasts are rarely, if ever, perfect. It is nearly impossible to 100% accurately estimate what the future will hold. Firms need to understand and expect some error in their forecasts.
• Forecasts tend to be more accurate for groups of items than for individual items in the group. The popular Fitbit may be producing six different models. Each model may be offered in several different colours. Each of those colours may come in small, large and extra large. The forecast for each model will be far more accurate than the forecast for each specific end item.
• Forecast accuracy will tend to decrease as the time horizon increases. The farther away the forecast is from the current date, the more uncertainty it will contain.

Demand Patterns

When we plot our historical product demand, the following patterns can often be found:

Trend – A trend is consistent upward or downward movement of the demand. This may be related to the product’s life cycle.

Cycle – A cycle is a pattern in the data that tends to last more than one year in duration. Often, they are related to events such as interest rates, the political climate, consumer confidence or other market factors.

Seasonal – Many products have a seasonal pattern, generally predictable changes in demand that are recurring every year. Fashion products and sporting goods are heavily influenced by seasonality.

Irregular variations – Often demand can be influenced by an event or series of events that are not expected to be repeated in the future. Examples might include an extreme weather event, a strike at a college campus, or a power outage.

Random variations – Random variations are the unexplained variations in demand that remain after all other factors are considered. Often this is referred to as noise.

Time Series Methods

Time series methods use historical data as the basis of estimating future outcomes. A time series is a series of data points indexed (or listed or graphed) in time order. Most commonly, a time series is a sequence taken at successive equally spaced points in time. Thus, it is a sequence of discrete-time data. Examples of time series are heights of ocean tides, counts of sunspots, and the daily closing value of the Dow Jones Industrial Average.

Time series are very frequently plotted via line charts. Time series are used in statistics, signal processing, pattern recognition, econometrics, mathematical finance, weather forecasting, earthquake prediction, electroencephalography, control engineering, astronomy, communications engineering, and largely in any domain of applied science and engineering which involves temporal measurements. [4]

In the following, we will elaborate more on some of the simpler time-series methods and go over some numerical examples.

Naïve Method The simplest forecasting method is the naïve method. In this case, the forecast for the next period is set at the actual demand for the previous period. This method of forecasting may often be used as a benchmark in order to evaluate and compare other forecast methods.

Simple Moving Average In this method, we take the average of the last “n” periods and use that as the forecast for the next period. The value of “n” can be defined by the management in order to achieve a more accurate forecast. For example, a manager may decide to use the demand values from the last four periods (i.e., n = 4) to calculate the 4-period moving average forecast for the next period.

Some relevant notation: D t = Actual demand observed in period t F t = Forecast for period t

Using the following table, calculate the forecast for period 5 based on a 3-period moving average.

Solution Forecast for period 5 = F5 = (D4 + D3 + D2) / 3 = (40 + 34 + 37) / 3 = 111 / 3 = 37

Here is a video explaining simple moving averages.

https://www.linkedin.com/learning/forecasting-using-financial-statements/simple-moving-average

Weighted Moving Average This method is the same as the simple moving average with the addition of a weight for each one of the last “n” periods. In practice, these weights need to be determined in a way to produce the most accurate forecast. Let’s have a look at the same example, but this time, with weights:

S olution Forecast for period 5 = F5 = (0.5 x D4 + 0.3 x D3 + 0.2 x D2) = (0.5 x 40+ 0.3 x 34 + 0.2 x 37) = 37.6

Note that if the sum of all the weights were not equal to 1, this number above had to be divided by the sum of all the weights to get the correct weighted moving average.

Here is a video explaining weighted moving averages.

https://www.linkedin.com/learning/forecasting-using-financial-statements/weighted-moving-average

Exponential Smoothing This method uses a combination of the last actual demand and the last forecast to produce the forecast for the next period. There are a number of advantages to using this method.  It can often result in a more accurate forecast. It is an easy method that enables forecasts to quickly react to new trends or changes. A benefit to exponential smoothing is that it does not require a large amount of historical data. Exponential smoothing requires the use of a smoothing coefficient called Alpha (α). The Alpha that is chosen will determines how quickly the forecast responds to changes in demand. It is also referred to as the Smoothing Factor.

There are two versions of the same formula for calculating the exponential smoothing.

Here is version #1 :

F t = (1  – α) F t-1 + α D t-1

Note that α is a coefficient between 0 and 1

For this method to work, we need to have the forecast for the previous period. This forecast is assumed to be obtained using the same exponential smoothing method. If there were no previous period forecast for any of the past periods, we will need to initiate this method of forecasting by making some assumptions. This is explained in the next example.

In this example, period 5 is our next period for which we are looking for a forecast. In order to have that, we will need the forecast for the last period (i.e., period 4). But there is no forecast given for period 4. Thus, we will need to calculate the forecast for period 4 first. However, a similar issue exists for period 4, since we do not have the forecast for period 3. So, we need to go back for one more period and calculate the forecast for period 3. As you see, this will take us all the way back to period 1. Because there is no period before period 1, we will need to make some assumption for the forecast of period 1. One common assumption is to use the same demand of period 1 for its forecast. This will give us a forecast to start, and then, we can calculate the forecast for period 2 from there. Let’s see how the calculations work out:

If α = 0.3 (assume it is given here, but in practice, this value needs to be selected properly to produce the most accurate forecast)

Assume F 1 = D 1 , which is equal to 42.

Then, calculate F 2 = (1 – α) F 1 + α D 1 = (1 – 0.3) x 42 + 0.3 x 42 = 42

Next, calculate F 3 = (1 – α) F 2 + α D 2 = (1 – 0.3) x 42 + 0.3 x 37 = 40.5

And similarly, F 4 = (1 – α) F 3 + α D 3 = (1 – 0.3) x 40.5 + 0.3 x 34 = 38.55

And finally, F 5 = (1 – α) F 4 + α D 4 = (1 – 0.3) x 38.55 + 0.3 x 40 = 38.985

Accessible format for Figure 3.3

Here is a video explaining exponential smoothing using EXCEL.

https://www.linkedin.com/learning/search?keywords=exponential%20smoothing&u=2169170

Here is version #2: 

F t = F t-1 + α(D t-1 – F t-1 )

Example Assume you are given an alpha of 0.3, F t- 1 = 55

Accessible format for Figure 3.4

Seasonal Index Many organizations produce goods whose demand is related to the seasons, or changes in weather throughout the year. In these cases, a seasonal index may be used to assist in the calculation of a forecast.

Using these calculated indices, we can forecast the demand for next year based on the expected annual demand for the next year.  Let’s say a firm has estimated that next year annual demand will be 2500 units.

Forecast Accuracy Measures

In this section, we will calculate forecast accuracy measures such as Mean Absolute Deviation (MAD), Mean Squared Error (MSE), and Mean Absolute Percentage Error (MAPE). We will explain the calculations using the next example.

Example The following actual demand and forecast values are given for the past four periods. We want to calculate MAD, MSE and MAPE for this forecast to see how well it is doing. Note that Abs (e t ) refers to the absolute value of the error in period t (e t ).

Step 1: Calculate the error as e t = D t – F t (the difference between the actual demand and the forecast) for any period t and enter the values in the table above.

Step 2: Calculate the absolute value of the errors calculated in step 1 [i.e., Abs (et)], and enter the values in the table above.

Step 3: Calculate the squared error (i.e., e t 2 ) for each period and enter the values in the table above.

Step 4: Calculate [Abs (e t ) / D t ] x 100% for each period and enter the value under its column in the table above.

Calculations for Accuracy Measures:

MAD = The average of what we calculated in step 2 (i.e., the average of all the absolute error values)

= (5 + 6 + 7 + 6) / 4 = 24 / 4 = 6

MSE = The average of what we calculated in step 3 (i.e., the average of all the squared error values)

= (25 + 36 + 49 + 36) / 4 = 146/4 = 36.5

MAPE = The average of what we calculated in step 4

= (7.94% + 10.17% + 12.96% + 9.23%) / 4 = 40.3/4 = 10.075%

Here is a video on Mean Absolute Deviation using EXCEL

https://www.linkedin.com/learning/search?keywords=mean%20absolute%20deviation%20&u=2169170

End of Chapter Problems

Below are monthly sales of light bulbs from the lighting store.

. Forecast sales for June using the following

• Naïve method
• Three- month simple moving average
• Three-month weighted moving average using weights of .5, .3 and .2
• Exponential smoothing using an alpha of .2 and a May forecast of 350.
• (357 + 319 + 360) / 3 = 345.3
• 360 x .5 + 319 x .3 + 357 x .2 = 347.1
• 350 + .2(360 – 350) = 352

Demand for aqua fit classes at a large Community Centre are as follows for the first six weeks of this year.

. You have been asked to experiment with several forecasting methods.  Calculate the following values:

• a) Forecast for weeks 3 through week 7 using a two-period simple moving average
• b) Forecast for weeks 4 through week 7 using a three-period weighted moving average with weights of .6, .3 and .1
• c) Forecast for weeks 4 through week 7 using exponential smoothing. Begin with a week 3 forecast of 130 and use an alpha of .3

Sales of a new shed has grown steadily from the large farm supply store. Below are the sales from the past five years. Forecast the sales for 2018 and 2019 using exponential smoothing with an alpha of .4. In 2015, the forecast was 360. Calculate a forecast for 2016 through to 2020.

Below is the actual demand for X-rays at a medical clinic. Two methods of forecasting were used. Calculate a mean absolute deviation for each forecast method. Which one is more accurate?

• Wikipedia contributors. (2019). Forecasting. In Wikipedia, The Free Encyclopedia. Retrieved November 4, 2019, from https://en.wikipedia.org/w/index.php?title=Forecasting&oldid=933732816 ↵
• Saylor Academy. (2012). Principles of Marketing. Forecasting. Retrieved on November 4, 2019, from https://saylordotorg.github.io/text_principles-of-marketing-v2.0/s19-03-forecasting.html ↵
• Wikipedia contributors. (2019). Forecasting. In Wikipedia, The Free Encyclopedia. Retrieved on November 4, 2019, from https://en.wikipedia.org/w/index.php?title=Forecasting&oldid=933732816 ↵
• Wikipedia contributors. (2019). Time series. In Wikipedia, The Free Encyclopedia . Retrieved on November 4, 2019, from https://en.wikipedia.org/w/index.php?title=Time_series&oldid=934671965 ↵

Introduction to Operations Management Copyright © by Mary Drane and Hamid Faramarzi is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License , except where otherwise noted.

An integral part of any site analysis is developing a land inventory and estimating development capacity under existing zoning. This assignment builds from our discussion of forecasting and land supply monitoring. It requires you to use County Assessment data, aerial photos, and field verification to develop a land use inventory for your study area and to estimate development capacity.

Your analysis should be presented in the form of a memorandum to the Technical Advisory Committee (TAC) and should include appropriate analysis, narrative, and tables. The TAC expects you to submit your analysis in class on Monday, November 3.  

The Assignment

Part i: forecasting.

This assignment is designed to allow you to apply your projection, forecasting, and data interpretation skills. You will gather data for Lane County and Eugene-Springfield and evaluate the forecasts prepared by the state Office of Economic Analysis and the Lane Council of Governments.

Start by downloading the long-term population and economic forecasts by county from the Office of Economic Analysis (OEA):

http://www.oea.das.state.or.us/population.htm

The OEA forecasts extend until 2040 and are the official county-level control totals. In other words, if you were to develop a set of sub-county forecasts for Lane County, they would need to sum to within ± 5% of the county control total.

Specifically, you should:

1.          Using the OEA data, complete the table below showing population, annual percent change, and average annual growth rate (AAGR) using historical County and state data for the period between 1980 and 1990 and the period between 1990 and 2000.

  What conclusions can be made from the results? What factors may have affected the population trends?

2.          Using the LCOG data posted on the Web Site ( click here ), create a table showing the ratio of population for Lane County and Eugene-Springfield to Lane County for each 5-year period shown in the MetroPopForecast sheet. Discuss the implications of the data.

3.          Project population for Lane County and Eugene Springfield using the straight-line method and the compound method. Use the years 1990 and 2000 to derive the growth rates. Present the results in 10-year increments out to 2040.

4.          List the assumptions implicit in the projections.   Indicate your level of confidence in the accuracy of your projections.

5.          Compare the results of your projections for Lane County with the OEA long-term forecast. What differences do you observe? What factors may have contributed to those differences?

Part II: Land Capacity Analysis

The assignment is to complete the land use inventory and land capacity analysis. Specifically, your analysis should include:

Data Available/Assumptions

As you initiate work on this project, you’ll find inconsistencies in the data: tax lot boundaries may not follow the study area boundaries; zoning may not be correct or may be split on a parcel. Unfortunately, you don’t have time to address all of these inconsistencies. You have a number of options available: make assumptions; adjust your data, etc. Do what you feel is reasonable given the data and available time.

GIS files of your study area are available here in zip format:

You’ll need the Assessment data to complete the assignment. I’ve made that data available in Excel format for download here .

You can find the metadata for the GIS files at: http://www.rlid.org/cfdocs/rlid/reg_Metadata/MetaCategories.cfm

Density Assumptions

Use the following assumptions about density:

[ Home | Syllabus | Schedule | Assignments | Project |  Links ]

This page maintained by Bob Parker , ©2002 October 21, 2003

Mets' Jorge Lopez blasts team as the ‘worst in MLB' using NSFW language

The mets reportedly will dfa lopez as a result., by sanjesh singh • published may 29, 2024 • updated on may 29, 2024 at 10:05 pm.

And he's gone.

New York Mets pitcher Jorge Lopez stunned the baseball world by calling the team "the worst in probably the whole f------ MLB" following a 10-3 loss to the Los Angeles Dodgers on Wednesday.

"I think I've been on the worst team in probably the whole f--king MLB." - Jorge López pic.twitter.com/NB0cDJ5w0i — SNY (@SNYtv) May 30, 2024

24/7 New York news stream: Watch NBC 4 free wherever you are

There was initially confusion on whether Lopez meant to say he was the worst teammate rather than team, but Anthony DiComo of MLB.com reported, citing a source, it was a combination of both.

As a result of Lopez's comments, the Mets reportedly plan to designate him for assignment.

Regarding confusion over whether López said he was on "the worst team" or was "the worst teammate," I'm told López later explained his comments as a combination of both: the worst teammate on the worst team. — Anthony DiComo (@AnthonyDiComo) May 30, 2024

Get Tri-state area news and weather forecasts to your inbox. Sign up for NBC New York newsletters.

Lopez, 31, is a right-handed pitcher who has previously suited up for the Milwaukee Brewers, Kansas City Royals, Baltimore Orioles , Minnesota Twins and Miami Marlins .

12 years later, American Olympic hurdler Lashinda Demus will get gold medal at ceremony in Paris

US women's water polo roster announced for Paris as team seeks record 4th straight gold

During Wednesday's game versus Los Angeles, Lopez was ejected for arguing a check swing call, then proceeded to toss his gloves into the stands on his way out of the field.

A bizarre ejection, a bizarre (player) reaction to the ejection, and the commentary on from the SNY Mets crew. "What has been a bad day all the way around has just gotten worse. (Jorge) López responds by tossing his glove up the over netting and into the crowd." ⚾️🎙️ pic.twitter.com/6bWb7fAOga — Awful Announcing (@awfulannouncing) May 30, 2024

Lopez said after the game he did not regret the move as the Mets dropped to 22-33 on the season, fourth in the NL East and 16 games behind the Philadelphia Phillies .

A Puerto Rican native, Lopez appeared in 28 games for the Mets this season (26.1 innings), recording a 3.76 ERA, 1.37 WHIP, 19 strikeouts and allowing 25 hits. He had a 1-2 record.

It's not yet clear what's next for Lopez once he hits the open market.

This article tagged under:

Aquarius June 2024 Horoscope: Read Your Monthly Predictions

By Sophie Saint Thomas

Read your sign's 2024 horoscope to see what's in store for you this year, or check out the   Aquarius personality profile .

Happy June, Aquarius . You ideally had a pretty chill May , and in many ways, June just works a little bit harder to remind you that downtime is important. The month begins with the communication planet Mercury entering Gemini on Monday, June 3 , which activates your 5th House of Pleasure. While, yes, this transit could indicate dirty talk , it's also epic for creativity. Whether you're an author, stay-at-home parent, or have a corner office, this transit reminds you to be your weird self. The vibes of self-expression continue into the dark new moon in Gemini on Thursday, June 6 , which offers a new opportunity to feel good about being the alien of the zodiac. This is a day for plotting new business ventures.

On Saturday, June 8 , the warrior planet Mars moves into gentle Taurus, emphasizing the importance of creating solid work-life boundaries . This transit encourages you to fight for your personal time, ensuring that your life isn't all work and no play. You deserve downtime and leisure, Aquarius.

Pretty Venus transitions into Cancer and your 6th House of Health on Sunday, June 16 , which also happens to be Father’s Day. If you have a good relationship with your dad, it's a great day to connect. However, the overarching theme for this transit is wellness. Venus urges you to focus on self-care, which is also underscored when Mercury joins Venus in Cancer on Monday, June 17 . These placements help you assert your need for rest and relaxation.

If you're dating, dear Aquarius, your sex life takes center stage when Vesta, the asteroid of sacred spirituality, moves into bold Leo on Wednesday, June 19 , blessing your partnerships with make-you-see-God orgasms. Not so into other people right now? Consider celebrating this transit with a sex toy .

The Summer Solstice on Thursday, June 20 , is the longest day of the year, the start of Cancer season, plus the witchy holiday Litha. Your weird soul will love this: They say this holiday is when the fairies like to come hang out with humans! Grab your flower crown; this holiday is also known as Midsummer. While the sun is in Cancer, your most important assignment is to prioritize yourself. Carve out plenty of time for rest and cute beauty routines like hair treatments and summer manicures .

The following day, Friday, June 21 , brings a bright full moon in hardworking Capricorn. A friend might reach out to you to help with something on this day. If it's an easy ask, and you feel called to serve, go ahead and be your generous self. But, if you catch yourself feeling spread too thin, remember that it's very okay to say no. Your true friends will respect your boundaries. This may hit pretty close to home, Aquarius, but you do have a reputation for being the sign who tries to save everyone else (as in, the entire world) while neglecting your own well-being. You can't take care of anyone if you're feeling exhausted and on edge. Full moons are known to leave folks feeling strained, so please advocate for self-care on this date.

June concludes with Saturn, the planet of structure and discipline, going retrograde in dreamy Pisces on Saturday, June 29 . This prompts you to reconsider your financial strategies and budgeting. It's an ideal time to make sure that your savings plan aligns with your long-term goals. You're also thinking about ways that you can make your life richer, whatever that means to you. Unfortunately, astrologers can't make promises in this economy, but we can tell you what the magical stars have in mind. Saturn retrograde says that you deserve to have savings and be paid your worth, and you are capable enough to plot a way to make that happen. We'll see you in July.

Monday, June 3: Mercury enters Gemini  Thursday, June 6: New moon in Gemini  Saturday, June 8: Mars enters Taurus Sunday, June 16: Venus enters Cancer Monday, June 17: Mercury enters Cancer Wednesday, June 19: Vesta enters Leo Thursday, June 20: Sun enters Cancer Friday, June 21: Full moon in Capricorn  Saturday, June 29: Saturn goes retrograde

To see monthly predictions for another zodiac sign, check out our full list of June 2024 horoscopes .

Read up on astrological events:

What's in store for your sign this year? Read our 2024 horoscope predictions to find out.

What is a twin flame, and how is it different from a soul mate?

Discover the 12 zodiac signs & their personality traits

A guide to angel numbers and what they mean

The ultimate Mercury retrograde survival guide

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Lompoc man arrested Wednesday on weapons violations, some connected to April shooting

LOMPOC, Calif. – A 34-year-old Lompoc man was taken into custody Wednesday for multiple weapons violations, some of which are connected to a shooting in Lompoc in April of this year.

On May 29, around 3:15 p.m., detectives located and arrested a 34-year-old Lompoc resident who had an outstanding warrant for weapons violations in the Lompoc area stated the Lompoc Police Department.

Detectives authored a search and arrest warrant for the Lompoc man as he was connected to a shooting on Apr. 19, 2024, in the 400 block of East Airport Avenue explained the Lompoc Police Department.

During the execution of the search warrant, detectives discovered a sawed-off shotgun, a rifle, and a homemade firearm known as a zip gun as well as numerous rounds of ammunition detailed the Lompoc Police Department.

According to the Lompoc Police Department, the 34-year-old is in custody on his warrants and also charged with 33215 PC-Possession of a sawed-off shotgun and 33600 PC-Possession of a zip gun.

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Cubs recall OF Pete Crow-Armstrong after he heats up during minor league assignment

By steve megargee, associated press | posted - may 30, 2024 at 10:03 a.m..

Estimated read time: Less than a minute

MILWAUKEE — Chicago Cubs outfielder Pete Crow-Armstrong is back in the majors after going on a tear during a brief minor league assignment. The Cubs announced before their Thursday afternoon game with the Milwaukee Brewers that they had recalled Crow-Armstrong and optioned infielder Luis Vázquez to Triple-A Iowa. Crow-Armstrong is regarded as an outstanding defender and one of the Cubs' top prospects. He has batted .236 with a .295 on-base percentage, one homer, nine RBIs and five steals in 23 games with Chicago this season. He was named the International League player of the week for the period of May 21-26.

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New york mets pitcher jorge lópez throws glove into crowd after being ejected, then delivers postgame rant.

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New York Mets pitcher Jorge López threw his glove into the crowd following his ejection from a game against the Los Angeles Dodgers on Wednesday – and then followed that up with a post-game rant for the ages that multiple reports say is expected to end his season with the team.

The 31-year-old López gave up a two-run home run to Shohei Ohtani in the eighth inning before being ejected by third-base umpire Ramon De Jesus for shouting at him, over a check swing call that didn’t go his way.

On his way to the dugout, López untucked his jersey and then threw his glove into the Citi Field crowd. The Mets would go on to lose to the Dodgers 10-3.

“I don’t regret it,” an unapologetic Lopez told reporters after the game.

“Whatever happens happens. Whatever they want to do, I’ll be tomorrow here if they want me.

“I’m going to keep doing this thing,” added the Puerto Rican pitcher. “I’m healthy. I’m ready to come back tomorrow if they want me to be here.”

According to MLB.com reporter Anthony DiComo , López, who is a native Spanish speaker, also said a comment in English in front of reporters which was interpreted by those present as either calling the Mets “the worst team in the whole (expletive) MLB” or calling himself “the worst teammate in the whole (expletive) MLB.”

Later in the same postgame interview, López was asked if he called New York “the worst team” in MLB to which he said: “Yeah, probably, it looked like.”

According to DiComo, López later clarified through a clubhouse source that he meant to say that he was the worst teammate on the worst team.

On Thursday, López posted on his Instagram story: “Who ever hear me I said ‘teammate’ and what I said on the situation I been the worst teammate. Thanks media for make it worse.”

He later added in a statement: “First and foremost, I apologize to my teammates, coaches, fans, and front office. I feel that I let them down yesterday, both on and off the field. I also want to clarify my post-game remarks, because I had no intention of disparaging the New York Mets organization. During that interview I spoke candidly about my frustrations with my personal performance and how I felt it made me “the worst teammate in the entire league”. Unfortunately, my efforts to address the media in English created some confusion and generated headlines that do not reflect what I was trying to express. I wish the team the best and hope that God continues to give me strength and guidance in my personal and professional life.”

Mets manager Carlos Mendoza called López’s outburst “unacceptable” and that it would be handled internally.

“It definitely doesn’t look good,” Mets’ shortstop Francisco Lindor told reporters afterwards. “If our manager says it’s unacceptable, it’s unacceptable. I hope tomorrow [López] feels completely different. … If he doesn’t, at the end of the day, he’s our teammate and we’ve got to go out there and compete, day in and day out, and I’ll back him up.”

According to multiple reports , Lopez is expected to be designated for assignment (DFA) by the team, but no official announcement has been made.

DFA is when a player is immediately removed from a club’s 40-man roster and can be either traded or placed on waivers within seven days of having their contract designated as such.

CNN has reached out to the Mets for comment.

Following Wednesday’s defeat, the Mets slip to a record of 22-33 on the season having lost their last three.

His frustration, and the frustrating season of the New York Mets all seem to have bubbled out in front of fans at Citi Field as the team finds itself sixteen games out of first place.

“We’re just not getting it done,” Mets reliever Adam Ottavino said. “We’re not throwing up zeroes when we need them, and we’re not getting the hits when we need them. And we’re not putting the at-bats together, we’re not playing the defense. It’s really all over the board. We stink right now.”

Nearly 3 out of 10 children in Afghanistan face crisis or emergency level of hunger in 2024

I SLAMABAD (AP) — About 6.5 million children in Afghanistan were forecast to experience crisis levels of hunger in 2024, a nongovernmental organization said.

Nearly three out of 10 Afghan children will face crisis or emergency levels of hunger this year as the country feels the immediate impacts of floods, the long-term effects of drought, and the return of Afghans from neighboring Pakistan and Iran, according to a report released late Tuesday by Save The Children.

New figures from global hunger monitoring body Integrated Food Security Phase Classification forecast that 28% of Afghanistan's population, about 12.4 million people, will face acute food insecurity before October. Of those, nearly 2.4 million are predicted to experience emergency levels of hunger, which is one level above famine, according to Save the Children.

The figures show a slight improvement from the last report, released in October 2023, but underline the continuing need for assistance, with poverty affecting half of the population.

Torrential rain and flash floods hit northern Afghanistan in May , killing more than 400 people. Thousands of homes were destroyed or damaged and farmland was turned into mud.

Save the Children is operating a “clinic on wheels” in Baghlan province, which was hit the worst by floods, as part of its emergency response program. The organization added that an estimated 2.9 million children under the age of 5 are projected to suffer from acute malnutrition in 2024.

Arshad Malik, country director for Save the Children in Afghanistan, said that the NGO has treated more than 7,000 children for severe or acute malnutrition so far this year.

“Those numbers are a sign of the massive need for continuing support for families as they experience shock after shock,” Malik said. Children are feeling the devastating impacts of three years of drought, high levels of unemployment , and the return of more than 1.4 million Afghans from Pakistan and Iran, he added.

“We need long-term, community-based solutions to help families rebuild their lives,” Malik said.

More than 557,000 Afghans have returned from Pakistan since September 2023, after Pakistan began cracking down on foreigners it alleges are in the country illegally, including 1.7 million Afghans. It insists the campaign isn't directed against Afghans specifically, but they make up most of the foreigners in the South Asian country.

In April, Save the Children said that a quarter-million Afghan children need education, food and homes after being forcibly returned from Pakistan.

Malik added that only 16% of funding for the 2024 humanitarian response plan has been met so far, but nearly half the population needs assistance.

“This is not the time for the world to look away,” he said.

Meanwhile, the European Union is allocating an additional 10 million euros (nearly $10.9 million) to the U.N. food agency for school feeding activities in Afghanistan. These latest funds from the EU follow an earlier contribution of 20.9 million euros ($22.7 million) towards the World Food Program's school meal program in Afghanistan for 2022 and 2023.

The funding comes at a timely moment and averts WFP having to downsize its school meal program this year because of a lack of funding, the WFP said in a statement.

“Hunger can be a barrier to education. The additional EU funding to our long-standing partner WFP ensures that more children in Afghanistan receive nutritious food,” said Raffaella Iodice, chargé d’affaires of the EU's delegation to Afghanistan.

The WFP's statement said that the agency will be able to use the funding to distribute fortified biscuits or locally produced nutritious school snacks to pupils in more than 10,000 schools in the eight provinces of Farah, Ghor, Jawzjan, Nangarhar, Nuristan, Paktika, Uruzgan and Zabul.

Last year, WFP supported 1.5 million school-age children through this program.

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Former Penn State Football Doctor Awarded $5.25 Million in Wrongful Termination Suit

Mark wogenrich | 9 hours ago.

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A Pennsylvania jury awarded Dr. Scott Lynch, Penn State's former football team doctor, $5.25 million in damages in his wrongful termination lawsuit against several parties, including his employer Penn State Health. The Dauphin County jury deliberated for several hours Wednesday after a seven-day trial that invoked Penn State coach James Franklin and included testimony from former players Saquon Barkley and Trace McSorley.

Pennlive.com reported Wednesday that the jury awarded Lynch $250,000 in compensatory damages and $5 million in punitive damages stemming from the civil suit Lynch filed in 2019. The plaintiffs were Penn State Health, Milton S. Hershey Medical Center and Dr. Kevin Black, Lynch's former supervisor who terminated Lynch as the football team's orthopedic physician and Penn State's director of athletic medicine in 2019.

In a statement Thursday, Penn State Health said it is considering an appeal.

"We are extremely disappointed to learn of the jury’s decision, as we continue to believe that the claims in the complaint have no merit," the statement said. "Penn State Health will soon determine whether it will appeal the decision. Penn State Health and the University remain dedicated to the health and well-being of our student-athletes."

Lynch initially filed the suit against the current defendants as well as Franklin, Penn State University and two former athletic administrators, including former athletic director Sandy Barbour. In 2020, a judge dropped Franklin, Penn State and the administrators from the suit because Lynch filed it after a Pennsylvania deadline for whistleblower suits.

The suit was scheduled to go to trial in March, but a judge declared a mistral and set a new trial date for May. Despite no longer being a plaintiff in the suit, Franklin and Penn State football were central subjects of the trial. In his 2019 filing, Lynch alleged that he was relieved from his roles after reporting "Franklin's attempts to influence and interfere with the plaintiff's medical management and return-to-play decisions related to student-athletes." Trial testimony repeated the allegations.

In a 2019 statement, Lynch said he filed the suit "with significant concern for the safety of the college athlete..." Lynch remains employed at Penn State Health as an orthopedic surgeon and director of sports medicine.

"Please take note that, prior to filing this lawsuit, I lodged informal complaints with The Hershey Medical Center and the Integrity Officer at PSU Athletics that the autonomy of medical providers was being challenged, and presented recommendations to manage the concern," Lynch said in the statement. "The recommendations presented, if implemented, I believe would make great strides in ensuring medical autonomy and the protection of the student athlete.

"To my disappointment, my recommendations have not been embraced. It is my understanding, that since being removed from my position with Penn State athletics in retaliation of and as a result of my complaints, my concerns have been investigated by the Integrity Office at PSU Athletics. Unfortunately, the results of the investigation remain unpublished and have been withheld from me. It is my hope that this civil action will serve to perfect the change that my informal efforts were unable to accomplish.”

According to Pennlive , Lynch's attorney Steven Marino told the jury in closing arguments that “Dr. Lynch wouldn’t relent. He would not let Coach Franklin interfere with his medical autonomy." In the defense's closing argument, attorney Sarah Bouchard said that Lynch, who worked in Hershey while serving as the team doctor, was not available full-time in State College and therefore wasn't "all-in" for the job. In 2019, Penn State University asked for the suit to be dismissed and said Lynch was a "disgruntled" former team doctor.

“Notwithstanding the unwavering commitment to student-athlete welfare and safety demonstrated for decades by the University, Ms. Barbour, Ms. [Charmelle] Green [former senior associate athletic director] and Coach Franklin, the Plaintiff in this lawsuit — disgruntled because he was removed from his assignment as the Football Team Orthopedic Physician and Director of Sports Medicine — has directly called into question the reputations built by those defendants," Penn State said in its 2019 response to Lynch's suit. "The University Defendants reject the Plaintiff’s attempt to denigrate decades of exemplary commitment to the University’s student-athletes and are prepared to defend against his claims.”

AllPennState is the place for Penn State news, opinion and perspective on the SI.com network. Publisher Mark Wogenrich has covered Penn State for more than 20 years, tracking three coaching staffs, three Big Ten titles and a catalog of great stories. Follow him on Twitter @MarkWogenrich .

MARK WOGENRICH

Mark Wogenrich is Editor and Publisher of AllPennState, the site for Penn State news on SI's FanNation Network. He has covered Penn State sports for more than two decades across three coaching staffs and three Rose Bowls.

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Freight Analysis Framework Modernization: Overview & Feedback*

June 25, 2024 at 3:00 PM Eastern

The Bureau of Transportation Statistics (BTS) is developing two new products as part of the Freight Analysis Framework (FAF) commodity flow data program: a multimodal assignment and flow visualization tool and a county-level origin-destination flow database.

BTS will share updates on these products and on its plans to modernize the FAF forecasting process. Presenters will seek input from attendees to shape future product features for the benefit of freight data users across the United States. 

*The webinar will cover similar concepts as what BTS presented at Transportation Review Board in January 2024. **Click here to sign up for future BTS freight data announcements. 

This presentation will be moderated by Stephanie Lawrence, Director of the Office of Statistical and Economic Analysis.

To register for the webinar, please click HERE .

Presentations:

• Ongoing Improvements to the FAF by Monique Stinson
• FAF Multimodal Network Development and Flow Visualization Tools by Laura Dods