business

● Walmart.com regularly monitors the prices of items on our site and compares them to the prices of those same items on competing websites. 2

● Online Retail Forecast, 2017 to 2022,” Forrester writes that ecommerce will account for 17.0% of retail sales by 2022, up from a projected 12.9% in 2017. 3

1. https://www2.deloitte.com/us/en/pages/consumer-business/articles/retail-distribution-industry-outlook.html 1 2. https://marketplace.walmart.com/knowledgebase/articles/Article/Automatic-Pricing-Rules-Overview 2 3. https://www.digitalcommerce360.com/2017/08/09/e-commerce-grow-17-us-retail-sales-2022/ 3

Step 2: Available Data (Please see Appendix 9 for all the available data used in designing EWS) Step 3: Categorize each variable as Either Leading or Lagging “Lagging indicators are those that follow an event, and leading indicators are those that signal future outcomes and those that feed directly into the performance of an outcome (lagging).

Leading Indicators Lagging Indicators Ad Rank Position Forecast Ad Rank Position Number of Customers Number of Customers Obtained Customers’ Spend per Trip Forecast Spend per Trip

Step 4: Add the “Connector” Assumptions Connectors are those values, often expressed as percentages, that translate leading indicators into lagging indicators as shown in the table below. We want to quantify the advertising campaigns on new products and sales strategies to forecast the number of new customers as well as revenues. Also, by catching up with customer preferences and introducing new product lines, Walmart would be able to increase revenue, so we want to forecast the new average of customer spending per trip as translated into a lagging indicator. The table below provides evidence and reasoning behind each number.

Leading Forecast Connector: % increase in indicator

Lagging Forecast

Ad Rank Position 1.5 Conversion rate: 10% New customers 26 million/year

Current Customers spend/trip

$55 Spend/trip: 20% After-plan Customers spend/trip $66

• The highest Ad Rank position is "1," and there is no "bottom" position. An average position of 1-8 is

generally on the first page of search results, 9-16 is generally on the second page, and so on. Average positions can be between two whole numbers. For example, an average position of "1.7" means that your ad usually appears in positions 1 or 2 of the search results. 1

• Walmart has over 260 million customers each week, and we use current data to forecast the number of the new customers 2

• Customers spend $55 per trip in Walmart on average, and we use this data to forecast the revenue when implementing the new strategic plan. 3

1 https://support.google.com/adwords/answer/14075 2 http://www.walmart.com/ 3http://247wallst.com/retail/2016/03/02/where-do-people-spend-more-wal-mart-costco-or-whole-foods

Step 5: Variance Calculation The “Actual” are the results (numbers) that come in after we complete our different campaign initiatives, so in this case, we would estimate the “Actual” to demonstrate the use of the EWS. Also, the “Variance” will be calculated as [Actual-Forecast]/Forecast.

Leading: Promotional driver Laggings:

Forecast Actual Variance in the Leading

Forecast Actuals Variance in the Lagging

Average ad rank position 1.5 2.2 -47% New customers 26 million/year

10 -61%

Customers Spend/Trip $66 $60 -9% After-plan Customers spend/trip

$66 $60 -9%

*Average ad rank position is about maintaining a certain position or ranking and thus a lower number is better. Therefore, we add a negative number when the higher position shown in the table above. Step 6: Calculating a Weighted Scored/ EWS Dashboard • Each leading indicator has been assigned a weight due to the level of importance and total add up to one. Weight Calculation:

Leading Variables Based on Forecast

Lagging Variables Weights

Average ad rank position New Customers obtained 0.6

Customers Spend/Trip After Plan Customers Spend/Trip 0.4

EWS Dashboard:

Promotional Leading Drivers Lagging

Forecast Actual Variance Weights EWS Weighted Variance

New Customers Obtained 26 10 -16 0.6 -9.6

After-plan Customers Spend/Trip 66 60 -6 0.4 -2.4

Step 7: Troubleshooting: When the EWS Shows Underperformance Negative Variance for Troubleshooting

Variance Leading Indicators

Variance Conversion

Variance Lagging Indicators

EWS Weighted Variance

New Customers Obtained -47% -20% -16 -9.6

After-plan Customers Spend/Trip 9% -25% -6 -2.4

EWS provides us granular detail of where the problem resides. In the table above, for the “New Customers Obtained” negative variance could be due to many causes, some of which would be easy identify (e.g. a lower- than-expected conversion rate), whereas others may be less obvious (e.g. a lower-than-expected shipping speed). Also, for the “Customers Spend/Trip” negative variance could be due to that new products are less aligned with customer preference. 2. EWS for Management Plan

Step 1: The assumptions 1. The tendency that the public cares more about ethical labor practices and modern management style

than in the past will continue. Thus, the management plan will generate customer loyalty and will align with customers’ preference so that the revenues will increase.1

2. Walmart’s reputation has a lot to improve and such improvements will lead to increase in revenues.2 Evidence for Assumptions: • Millennials Customers - born from 1980 to 2000 - become the principal force of consumers, since they

have grown from financially dependent teens to young adults in their 20s and 30s. Nowadays there are roughly 80 million Millennials in the United States, and each year they spend approximately $600 billion. They care about the ethical practical as the inherent tradition from their predecessors and value more on faces behind the brand, such as the labors’ relationship. 1

• Walmart has been criticized for its child employees use, discrimination of genders, bias on racism, unsafe environment, and so on. Walmart has been pointed to be the “worst retailer” or even the “most-hated retailer”.2

1. https://www.entrepreneur.com/article/250609 1 2. https://thrivehive.com/why-is-having-a-good-reputation-important/ 2

Step 2: Available Data (Please see Appendix 9 for all the available data used in designing EWS) Step 3: Categorize each variable as Either Leading or Lagging

Leading: Promotional Indicators Lagging Indicators：

# of Seminars Revenue

# of Employees Involved in the Training Program Market Share

Increase in Wages Profitability

# of Ads Attached to Different Websites ROI

Investment in Ads

The Number of Employees Applying the skills Learned in the Seminars

Step 4: Add the “Connector” Assumptions Leading Forecast Connector: Lagging

The Number of Employees Applying the skills Learned in the Seminars

126k Conversion Constant: 238 dollars/people

Forecast Revenue increase: $30 million

Raise Wages of Labors/week $75 Conversion Constant: 400,000 $30 million

Investment on Ads (million) $840 Conversion Ratio: 4.7% $40

Connectors’ Assumptions: • In order to protect the effectiveness of the EWS, we break the causal forecast into short time frames. Hence,

the causal forecast is weekly based and updated. • As for the increase in revenues, our forecast is based on the current data. From 2016 to 2017, the revenues

increased by $197 million. We assume that the increase in revenues due to management plan is $100 million total.

https://www.statista.com/statistics/269403/net-sales-of-walmart-worldwide-by-division/ • The work day is assumed to be 5 days per week, while the work hour is assumed to be 8 hours a day. The

training program aims to all the employees (1.5 million people of Walmart US), and there are 70 percent of them finally attending the training program, while only 12% of which apply what they’ve learned into work.1 Also, we assume that every employee fully applying the skills will generate more around $5.95 dollars per hour due to higher efficiency and better practice, which turn into $238 dollars per week.

1https://corporate.walmart.com/our-story/locations/united-states#/united-states https://www.shiftelearning.com/blog/statistics-on-corporate-training-and-what-they-mean-for-your-companys-future • We forecast the raise of wages of labors based on the current data that Walmart's efforts so far have

translated into a pay raise of about 16% to $13.69 per hour for non-managerial full-time employees, thus, the raise per week will be $75. 2 We assume that the non-management employees are 80% of all of the employees.3 Then, there are 1.2 million non-management employees in Walmart. We assume that each dollar increment will improve the additional revenues for $0.33 dollars the non-management labor can make per week, which turn into around additional $400,000 dollars all the non-management labor in Walmart US can make per week.

3https://www.quora.com/How-many-people-work-in-a-single-walmart-target-or-box-store-location-And-how-many-floor- associates-and-cashiers 2http://www.businessinsider.com/walmart-is-investing-more-in-employees-2016-10 • The Ads mentioned here are the ones specifically presenting Walmart’s improving management. The

investment on ads was $2.1 billion in U.S, and our forecast is based on the current data and assume ads specifically presenting Walmart’s improving management is 40% of total investment.4 The more investment in ads will disseminate the improvements of management of Walmart, improve the reputation of Walmart, and attract more consumers’ attention. Thus, more loyalty the existing customers will be and more preference of the new customers will be addressed to Walmart. We assume that 4.7% of the investment will turn into additional revenues.

4https://www.statista.com/statistics/192068/us-ad-spending-of-walmart/

Step 5: Variance Calculation

Leading: Promotional Driver

Forecasts Actual Variance in the Leading

Forecasted Increase of Revenues

Actual Increase of Revenues

Variance in the Increase of Revenues

The Number of Employees Applying the skills Learned in the Seminars (unit: thousand)

126k 110k -16k (-12.6%) $30 $25 $-5 million (-16.7%)

Increase Amount of Wages of Labors (million)

$75/person $65/person $-10(-13.3%) $30 $25 $-5 million (-16.7%)

Investment on Ads(million) $840 $700 $-140(-16.7%) $40 $35 $-5 (-12.5%)

Step 6: Calculating a Weighted Scored/ EWS Dashboard

● The weight is calculated as a percentage of the total increase of the revenues. ● The weighted score is calculated by multiplying the weights with the variance.

Weights Calculation:

Leading Variables Based on Forecast Weights

The Number of Employees Applying the Skills Learned in the Seminars 0.3 (30/100)

Increase Amount of Wages of Labors 0.3 (30/100)

Investment on Ads 0.4 (40/100)

EWS Dashboard: Promotional Drivers

Lagging: Increase of Revenues

Forecast Actual Variance Weights EWS Weighted Variance

The Number of Employees Applying the skills Learned in the Seminars 30 25 -5 0.3 -1.5

Increase Amount of Wages of Labors 30 25 -5 0.3 -1.5 Investment on Ads 40 35 -5 0.4 -2

Step 7: EWS Dashboard/ Troubleshooting: When the EWS Shows Underperformance Negative Variance for Troubleshooting

Promotional Leading Drivers Variance Leading Indicators

Variance Conversion Rate

Variance Increase of Revenues

EWS Weighed Variance Score

The Number of Employees Applying the skills Learned in the Seminars

-12.6% -4.6% -16.7% -1.5

Increase of Wages of Labors -13.3% -3.8% -16.7% -1.5

Investment on Ads -16.7% 6.4% -12.5% -2

*The table of different variance conversion rates is in Appendix 11. EWS of management plan provides us granular detail of where the problem resides. In the table above, for the “Increase of Revenues” negative variance could result from causes which can be easily identified by following the table: • A lower-than expected number of employees apply the skills learned in the seminars, a lower-than-

expected efficient per application of skills as the conversion rate. • A lower-than-expected increase of wages of labors, a lower-than-expected incentive the increasing wages

bring to labors. • A lower-than-expected investment on ads, a lower-than-expected return the ads bring to Walmart. 3. Early Warning System for ERM plan Step 1: Assumptions: 1. The ERM program has been established and implemented in time at Walmart by the risk steering committee.

2. The ERM team followed the designed process and updates the substantial risks frequently.

3. The ERM program kept losses from risk scenarios within the risk appetite set by the board.

4. Walmart implemented ERM program to improve the internal and quality control.

Evidence:

• In this project, we assume that Walmart adopted the action plan and followed the suggestions purposed in the previous part of the project. Walmart established the plan of ERM implementation and finished the processes on time.

• Walmart’s inventory was updated quarterly by adding substantial risks/risk scenarios and subtracting existing risks with smaller impacts.

• Here we assume that ERM at Walmart works efficiently and keeps that losses due to risk scenarios within the risk appetite set by the board.

• Walmart received fewer complaints from customers and labors after the implementation since ERM improved the internal and quality control.

Step 2: All available data (Please see Appendix 9 for all the available data used in designing EWS) Step 3: Leading versus lagging indicators

Leading Indicators Lagging Indicators Risk appetite Losses after mitigation Stock Price, 500 common stocks Revenue Forecast GDP Number of customers Number of complaints received annually Forecasted complaints received in the coming year

Step 4: Add the “Connector” Assumptions

Leading Forecast Connector: % changes in indicator

Lagging

Risk Appetite ($Million) 850 The losses being mitigated as a percentage of risk appetite: 20%

Losses after mitigation: 680

Number of complaints received annually （＃）

3650 Percentage of complaints decreased: 20%

Forecasted complaints received in the coming year: 2,920

• Walmart has just started the ERM program and still adjusting and improving the processes. Therefore, we assume that ERM helps Walmart to prevent 20% more of the losses from risk events in the first year.

• We assume Walmart receives 3650 complaints per year and we use this number to forecast how ERM at Walmart helps to reduce the number in the following year.

Step 5: Variance Calculation / EWS Dashboard

Leading: Promotional Driver

Forecasts Actual Variance in the Leading

Forecasted change in indicators

Actual change in indicators

Variance in the change in indicators

Losses after mitigation $680M $750M -10.29% $170M $100M -$70M (-41.18%)

Number of complaints received annually

2920 3100 -6.16% 730 550 -180(-24.66%)

• The number of losses covered by ERM shows a preferable outcome when the number is lower. Therefore, we make the variance negative when the actual outcome is higher than the forecasted one.

• The number of complaints shows a preferable outcome when the number is lower. Therefore, we make the variance negative when the actual outcome is higher than the forecasted one.

Step 6: Calculating a Weighted Scored

• Each leading indicator has been assigned a weight due to the level of importance and total add up to one.

Promotional Drivers Lagging

Forecast Actual Variance Weights EWS Weighted Variance

Losses after mitigation 680 750 -70 0.7 -49

Number of complaints received annually 2920 3100 -180 0.3 -54

Leading Variables Based on Forecast Weights

Losses covered by the ERM program set by risk appetite 0.7 (70/100)

Number of complaints received annually 0.3 (30/100)

Step 7: Troubleshooting: When the EWS Shows Underperformance Negative Variance for Troubleshooting

Promotional Drivers Variance Leading Indicators

Variance Conversion

Variance Lagging Indicators

EWS Weighted Variance

Losses after mitigation -10.29% -41% -90 -63

Number of complaints received annually -6.16% -25% -180 -54

EWS provides us a granular detail of where the problem resides. In the table above, for the “Losses covered by the ERM set by risk appetite” negative variance could be due to many causes, some of which would be easy identified (e.g., unexpected weather condition and natural disaster), whereas others may be less obvious (e.g., an uncommon risk scenario which has not been identified before). Also, for the “Number of complaints received annually” negative variance could be due to that the internal control has not sufficiently covered all the layers of Walmart and part of the ERM processes need adjustments.

Final Recommendations

According to our semester-long research and analysis by using various models and methodologies, we conclude the following aspects as our final recommendations:

1. Appropriate strategic movements toward trends and shifts in consumer preferences/demands and advertisements: Our research revealed that Walmart failed to address the trends and shifts in consumer preferences. We believe that Walmart needs to make an aggressive integration of new products to current offering by assigning an R&D team to identify the consumer preferences and trends in retail industry regularly. Once Walmart decides the new products to offer, they should contract with suppliers and other relevant business partners along with advertisements to the new offerings. We recommend focusing and investing in a social media platform, company’s website, and purchasing new google ad-words related to these new foods.

2. Appropriate strategic movements toward e-commerce challenges and growing competition: Walmart set foot in e-commerce platform to develop its online shopping market; however, Walmart’s primary competitors are showing stronger performances. We recommend Walmart to enhance shipping offers to include groceries and provide same-day/two-hour options and improve partnerships with carriers such as UPS, FedEx and so on.

3. Improvements in management practices, labor relations, and Walmart’s company image: We recommend Walmart to implement a new training program for managers and employees of all levels. The training program includes seminars, conferences, and activities to improve labor practices and labor relations. Meanwhile, senior management and the CFO at Walmart should also consider raising wages as another option. Together, the improvements should be combined with an advertising campaign to improve Walmart’s image.

4. Development of an entire ERM program in the company: We recommend Walmart to develop a companywide ERM program by hiring a CRO and an ERM team first. The program will manage risks proactively and allow Walmart to make risk-based decisions, by having a risk- aware culture as well as risk policies and procedures. An efficient ERM team will help Walmart achieve its strategic objectives and translate risks into opportunities.