Deo’s formula for optimal employee density to reduce theft-related shrink

Abstract

Theft-Related Shrink (TRS) is a common problem in the retail industry and researchers and retail managers have proposed various solutions to reduce it. Most of the solutions proposed and discussed in literature are in the domains of retail surveillance systems and structures, surveillance technology installation and its upgradation, economic incentives to employees, training and development of employees, and recruitment and hiring full time employees. A few quantitative studies have reported the negative relationship between the retail floor employee density (ED) and TRS. These studies have indicated that an increase in ED leads to reduction in TRS. However, there is a gap in the literature related to optimizing employee density to reduce TRS, as this idea has not been explored and presented in the extant literature. In this paper, a formula is developed and presented for optimal employee density (OED) to reduce the sum total of TRS and employees cost (EC) to the minimum. The formula could be useful to researchers, retail store managers, and human resource professionals in the retail industry to assess the number of employees required to minimize the sum total of EC and TRS and then accordingly plan the employee recruitment and hiring process.

Keywords

1. Introduction

Retail shrink (RS) is the amount of loss incurred in monetary units when there is a difference between merchandise currently available on hand for sale and recorded as received in books. May (2007, p. 12) described it as a total $ amount that includes theft by employees, shoplifting by outsiders, fraud committed by venders, and loss due to errors on the part of retail administration. In total, this loss is estimated to be in the range of 1.5% to 1.6% of annual retail sales.

Knego and Misevic (2018, pp. 58-59) explained RS in terms of book inventory (BI) and physical inventory (PI) and according to them, BI is defined as ‘what the store should have’ and PI is defined as ‘what the store does have’. Retail shrink is observed when BI is greater than PI or PI is less than BI or in other words BI minus PI is equal to RS. For a retail store, if BI is equal to PI, then there is no retail shrink. In literature, RS is also reported in terms of percentage of sales as well.

In 2014, retail sales in the USA were $3.19 trillion with retail shrink $44.02 billion, and it was calculated to be close to 1.40% of annual sales. In the USA retail shrink increased to 48.9 billion in 2016. In European countries, such as, UK, France, Portugal, Italy, Germany, Belgium, Spain, and the Netherlands the reported retail shrink is found to be in the range of 1.1% to 1.4% of sales, and in Far Eastern Countries (Japan and China) it is between 1.00% to 1.5% of sales (Salierno, 2018).

According to the Sensormatic Global Shrink Index from Tyco Retail Solutions and PlanetRetail RNG information appeared on Retail Dive website (https://www.retaildive.com/news/shrink-cost-retailers-100b-last-year/524460/ and accessed on June 24, 2019), worldwide loss of inventory in retail sector due to customer theft, employee theft and other errors collectively amounted to almost $100 billion in 2018, equivalent to about 1.82% of global sales.

As per the data from Innovation, Science and Economic Development at bdc.ca website (https://www.bdc.ca/en/ar ticles-tools/money-finance/manage-finances/pages/standard-costs-retail-industry.aspx, accessed on June 24, 2019), the cost of goods sold accounted for about 65% of sales for retailers with annual average revenues under $5 million in 2013. The largest operating cost in a retail business is found to be employees cost (EC) (13% of total cost for small retailers) followed by administrative costs (buying trips, payroll services and the like) at 4.9%, then rent about 4.4%, and marketing cost about 1.4%.

As per Bamfield (2004, p. 235), retail shrink is generally classified as criminal based shrink (result of unlawful acts to gain possession of merchandise) and non-criminal shrink (result of system failure, employee carelessness, and training issues). Criminal based shrink is linked to theft by the people external to the retail store, and theft by retail store employees. The people external to the retail stores could be customers, organized crime rings (OCR) stealing for profit, and vendors who are dishonest. The causes of shrinkage internal to the retail outlets could be store employees stealing merchandise for themselves, mistakes on the part of administration, and/or due to the failure of the system functioning itself.

1.1 Retail shrink categories

Beck (2018, p. 95) characterised retail shrink into four main categories or “buckets”; shop lifting by customers, theft by employees, administration errors, and the fraud committed by vendors. In the USA, in 2014 retail shrink due to customer theft was 38%, due to employee theft was 34.5 %, due to administration and paper work errors 16.5%, due to vendor fraud 6.8%, and unknown losses were 6.1% (Knego & Misevic, 2018, p. 62). Elsheikh and Deo (2010) also reported similar shrink categories and also observed that theft-related shrink (TRS) due to customers is about 32%, by employees about 48%. About 20% of the retail shrink was estimated to be due to administrative errors plus venders’ dishonesty and due to other unknown reasons.

In addition, the study by Knego and Misevic (2018, p. 62) showed that customer and employee theft-related shrink (TRS) make up almost 75% of the total shrink. However, earlier studies by Deo and Elsheikh (2010) and Quinn (2004, p. 30) indicated that customer and employee theft related shrink (TRS) together makes up about 80% of the total shrink. Therefore, it is important for retail management to secure goods against theft and not to give an opportunity to opportunistic and professional thieves to commit crime related to theft in retail stores.

1.2 Role of technology and employees in reducing shrink

Some of the studies suggest that the use of Electronic Article Surveillance (EAS), and Radio frequency identification (RFID) tags help reduce theft-related shrink (TRS) to a large extent. However, it has also been noticed that tagging technology causes false alarms and professional thieves take advantage of it. Other studies reported that Closed Circuit TV (CCTV), Dome cameras, and Video surveillance systems have an immediate impact on the reduction of TRS. However, these studies have been found to be based on short study period experiments and TRS reduction over the long run is found to be unsustainable (Beck, 2016). Moreover, it is also observed that the use of surveillance technologies is not very convenient in criminal investigations due to false positives in the identification and accusations (Adams & Ferryman, 2015, pp. 285-86). Opportunistic thieves may be deterred by surveillance systems whereas professional thieves and organized crime rings generally find a way to get around the systems (Beck, 2016). Rosenblum (2008, p. 52) proposed the addition of business intelligence systems, wherever possible, in daily store operations to support employee engagement and make them stakeholders in retail business, as the use of latest technology alone may not be very helpful to reduce TRS.

As per Beck (2016), good store design and its layout, and ‘people’ on the retail floor can play a very important role in discouraging shop lifters. For example, a store guard’s proximity to offenders and their mobility in the store along with good customer service practices can be an effective way to discourage shop lifters. In addition, retail staff on the floor can easily recognize the frequent visitors to the store and thus help reduce anonymity of would-be thieves. As per Beck (2016), anonymity has been found to be one of the key prerequisites for some of the offenders to commit a crime related to theft.

Qualitative and exploratory studies related to the hiring of part-time, full time, and honest employees showed that hiring part-time employees leads to high employee turnover and it in turn is found to be associated with an increase in TRS. Some studies reported employee training and their job security, effective communication, and their engagement along with adequate benefits and compensation lead to shrink reduction. However, a few other studies (Howells & Proudlove, 2007, p. 101; Deo & Elsheikh, 2010) have shown quantitative relationships between shrink and part time employees, full time employees, employee turnover, and employee density (number of employees per unit retail floor area).

1.3 Problem statement and its practical relevance

In literature there are studies related to retail surveillance systems and structures, surveillance technology installation and its upgradation, economic incentives to employees, training and development of employees, and recruiting and hiring full time employees. A few quantitative studies have also been reported that show the relationship between the retail floor employee density (ED) and TRS. These studies have indicated that an increase in ED leads to reduction in TRS. However, there is a research gap in the literature related to optimization of employee density to reduce TRS. This idea has not been explored so far in the literature on retail shrinkage and shop-lifting. In this paper, a formula is developed and presented for optimizing employee density to reduce the sum total of TRS and employees cost (EC) to the minimum.

The formula could be useful to researchers, retail store managers, and human resource professionals in the retail industry to assess;

1. 1.
The optimal number of employees required to minimize the sum total of EC and TRS and then accordingly plan the employee recruitment process.
2.
The formula can be useful to retail management to make an assessment of the budget required to achieve optimal employee density before starting the employee recruitment and hiring process.
3.
The formula can also be helpful in the assessment of theft-related shrink reduction in advance, as a result of optimal employee density on the retail floor.
4.
The formula can be helpful in the assessment of the employee cost that could be funded from the savings that could be made through reduction of TRS due to the optimal employee density on the retail floor.
5.
The formula can be helpful in the assessment of the savings that could be transferred to retail profits after meeting the budgeted cost requirement of optimal employees on the shop floor.

Part 2 and 3 of the paper discusses the literature relevant to employee turnover (TO), and employee density (ED) and its relationship to retail shrink. In part 4, the formula is presented for optimizing the employee density to reduce the sum total of employee cost (EC) and theft related shrink (TRS). Part 5 describes the application of the formula using retail shrink data from a retail store. Part 6 provides conclusions, future research directions, and possible limitations of the formula.
2. Employee turnover and shrink size-literature review

Beck and Peacock (2007, p. 34) studied retail shrink in five retailers in the United States to look for best practices for preventing retail shrink. They observed greater organizational commitment from the senior level down to the shop floor level with the employee as a main pillar of retail shrink prevention. Adherence to the standard systems and procedures starting from receiving merchandise to checkout points helps reduce retail shrink. Awareness and disclosure of actual shrink figures to staff in a timely manner is also found to be helpful. Empowering frontline staff, seeking their engagement and contribution to loss prevention, providing necessary training, and making them accountable is helpful to reduce shrink. As per May (2007), retail shrink prevention should be every employee’s business.

Howells and Proudlove (2007, p. 101) in their study showed that the higher the turnover of an item in a retail store, the lower the shrink for that particular item. It could be the result of regular follow up and monitoring of fast moving items in retail store. According to them, an adequate number of employees on duty may also be a reason for lower retail shrinks. The study reported that customers and staff “crowding” at various sales and checkout points is helpful in reducing TRS.

The drive to reduce employee cost (wages and other related expenses) generally motivates managers to hire more part-time employees, usually with high turnover, and high retail shrink. Employee turnover has been found to be a good indicator of retail shrink. Elsheikh and Deo (2010) found that increase in the percentage of part time employees led to an increase in the employee turnover, and high employee turnover (TO) is associated with high retail shrink (Fig. 1). The correlation coefficient (r) between employee turnover and shrink was found to be 0.720791, high association between employee turnover (TO) and retail shrink.

Figure 1.

Shrink Size Association with Employee turnover (TO)*.*Source: Adapted from Elsheikh and Deo (2010).

3. Employee density (ED) and retail shrink

Howells and Proudlove (2007, p. 101), as well as Elsheikh and Deo (2010), discovered that for a retail store having a certain level of surveillance system and structure, there is a negative relationship between shrink and retail floor staff density. The relationship is shown in Fig. 2.

Figure 2.

$Shrink vs. Staff Density per unit of sales area*. *Source: Adapted from Elsheikh and Deo (2010).

The correlation coefficient (r) between staff density in terms of labour (employee) hours per unit of sales area (LHPUSA) and retail shrink is observed to be $-$ 0.97, showing a very high negative association.

3.1 Employee density, employee cost and shrink size

The staff density related data presented in Elsheikh and Deo (2010) transformed into $ amount is presented in Fig. 3, showing the relationship of employee density to employee cost (EC), and with retail shrink.

Figure 3.

Employee Density ( $X$ -axis) Relationship to Employee Cost and $ Shrink*. *Source: Adapted from Elsheikh and Deo (2010).

In Fig. 3, the increase in employee density ( $X$ -axis), leads to increase in employees cost (EC) to retail store. In Fig. 3, increase in employee density also lead to reduction in retail shrink. At a certain employee density ( $X$ -axis), retail floor employee cost, and retail shrink, are found to be equal and that is the point of optimal employee density at which the sum total of the employee cost (EC) and retail shrink (RS) is minimum.

4. Formula for optimizing employee density (ED) to reduce TRS

Based on the relationship of the employee density (ED) with retail shrink, as well as with the cost of employees, a generic formula for optimizing ED is developed in this paper. At optimal employee density (OED), the employee cost (EC) will be equal to theft-related shrink (TRS) and the sum total of employees cost (EC) and theft-related shrink (TRS) would be minimum. To develop the generic formula, following assumptions, based on the literature reviewed, are made;

Assumption A

Retail shrink (RS) can be grouped into four categories or “buckets”; theft by customers, theft by employees, administration errors and vendor fraud (Beck, 2018, p. 95).

Assumption B

Theft-related shrink (Customer and employee theft together) contribute 75% to 80% of the retail shrink (Knego & Misevic 2018, p. 62; Deo & Elsheikh 2010; Quinn, 2004).

Assumption C

Retail surveillance structures and related surveillance technological systems help reduce shrink in the short run (Beck, 2016) only. Professional and opportunistic thieves find ways to get around the surveillance structure and systems in the long run (Finefrock, 2008, p. L22).

Assumption D

For a given level of retail surveillance structure and surveillance technology, employee presence on retail shop floor and their mobility in the retail store effectively discourage opportunistic and professional thieves (Beck, 2016) and thus leads to reduction in theft-related shrink.

Assumption E

Total cost of employees on the retail floor area is directly related to the number of employees on retail floor (Employee density). It means an increase in employee density per unit of retail floor area leads to increase in the cost of employees to the retail store.

Assumption F

Employee density (ED) for a retail floor is negatively related to theft-related shrink (TRS). It means the higher the employee density on the retail floor, the lesser the TRS (Elsheikh & Deo, 2010; Howells & Proudlove, 2007).

Assumption G

Employee cost (EC), and theft-related shrink (TRS) in monetary units are the two cost items that are relevant for assessing optimal employee density (OED).

4.1 Variables and parameters used in the formula

N – Optimal employee density (OED) in terms of number of employees.

C – $ Employee cost/employee/year.

S – $ Total retail shrink for a given retail store per year.

$p$ – Proportion of TRS (Theft-related shrink) in the retail shrink.

4.2 Formula derivation

If, S, is the total retail shrink (for a given retail store with a given surveillance structure and surveillance technological system in place) in monetary units, and $p$ , the proportion of theft-related shrink (TRS) in the total retail shrink, then;

$\displaystyle\textit{TRS}=S\times p$ (1)

If, $N$ , is the optimal employee density

Then at optimal Employee Density, $N$ ;

$\displaystyle\textit{TRS}=\textit{EC}$

If, employee density at a retail floor is increased to $N$ (optimal employee density), Then, TRS, as per the negative relationship to $N$ , reduces to;

$\displaystyle\textit{TRS}=(S\times p)/N$ (2)

If, $C$ , the cost of an employee per year, Then,

$\displaystyle\text{Yearly cost of $N$ employees at OED}=N\times C$ (3)

Thus, at $N$ , optimal employee density, the sum total of TRS plus employees cost (EC) would be;

$\displaystyle\textit{TRS}+\textit{EC}=\{(S\times p)/N\}+N\times C$ (4)

The sum total of TRS plus EC would be minimum at an OED, $N$ , where EC line intersects the TRS line (Fig. 3) and at that point;

$\displaystyle\textit{EC}=\textit{TRS}\quad\text{(\Fig{3}{})}.$

$\displaystyle N\times C=(S\times p)/N$ (5)

Thus solving the Eq. (5), for $N$ , we find;

$\displaystyle\text{Optimal Employee Density},N=\{(S\times p)/C)\}^{1/2}$ (6)

5. Formula application to retail store shrink

For application of the formula (Eq. (6)), the data from a retail store is presented in Table 1.

Table 1
$Shrink of a retail outlet (May, 2004 to May, 2008)

Shrink category	Year 2004	Year 2005	Year 2006	Year 2007	Year 2008
HBA	22697	26364	24638	15102	31394
Cosmetics	5592	12189	16304	2377	63288
OTC	12076	7870	9408	5268	36374
Total shrink	40365	46423	50350	22747	131056

Source: Adopted from Elsheikh & Deo (2010).

In Table 1, the data related to shrink for Health and Beauty Aids (HBA) section, Cosmetics section, and ‘Over the counter’ (OTC) medication section is taken, over a period of five years (May 2004 to May 2008). The actual data collected was found to be having no differentiation in terms of ‘customer theft’ and ‘employee theft’ in retail shrink. The $ amount in the Table 1 are in terms of retail shrink. For example, in the year 2008, the retail management could not hire adequate number of employees and it is reflected in the significant increase in retail shrink from $22,747 in 2007 to $131,056 in 2008.

Therefore, to calculate the optimal employee density for year 2008, the $ value of retail shrink, S $=$ $131,056 (for HBA plus Cosmetics plus OTC section of the store) is used for calculations.

As per literature reviewed, TRS (theft related shrink due to internal and external theft) is reported to be close to 80% of the retail shrink (RS) and the other 20% is due to administrative errors, system failures, and vendor fraud.

Therefore, the proportion, $p$ , of TRS in retail shrink is taken as, 80% of RS or 0.8 of the RS.

Assuming 2000 hours of work per year per employee @ $10 per hour, yearly employee cost, $C=$ $20,000. Then, optimal number of employees required to minimizes sum total of employee cost and theft-related shrink are calculated using the formula;

Optimal Employee Density, $N=\{(S\times p)/C)\}^{1/2}$

$\displaystyle N=\{(131056\times 0.8)/20{,}000)\}^{1/2}$ $\displaystyle N=\{(104{,}844)/20{,}000)\}^{1/2}$ $\displaystyle N=2.289$

To minimize the sum total of employee cost and the theft-related shrink, the management would have needed 2.289 employees per unit of retail area as OED. It means that retail management would have needed to budget for 2.289 employees, and that would have amounted to $45,792 (2.289 employees *20,000). Putting 2.289 employees on retail floor would have reduced the initial TRS from $104,844 to $45,792 and thus would have made the savings of $59,053 from Initial TRS of $104, 844. Out of $59053, $45,792 would have been used to fund employee cost, and remaining $13,260 would have added to the total profit of the retail store. Optimal employee density on retail floor would have created these savings through proper monitoring and discouraging theft related activities of opportunistic and professional thieves.

6. Conclusions, future research directions, and limitations

The literature reviewed for this paper indicates that retail shrink is a global phenomenon and it causes huge loss ($billions) to the retail industry every year. Researchers and practitioners suggested different ways to reduce shrink. The retail sector has been using surveillance technology including video surveillance cameras, EAS and RFID tags and it has been observed that technology alone does not help reduce shrink. Research has shown that full-time employees employed are more effective in reducing theft-related shrink.

The formula developed and presented in this paper indicates that the optimal employee density (OED) can help reduce theft-related shrink to a large extent. The formula shows that a large part of retail shrink can be reduced and the savings made thereof could have been used to pay for optimal number of employees hired to effectively discourage thieves in retail stores. The hired employees would also have paid taxes to government by becoming a productive part of the society. The optimal number of employees on retail floor can also generate net savings, in addition to the funds required to meet the optimal employment budget, that can be transferred towards profits of retail stores.

6.1 Future research directions

The formula presented for optimal employee density to reduce theft-related shrink can also be used to plan the budget for employees. Reduction made in theft-related shrink can cover the payments for the employees cost and can also generate net savings towards profitability of the store.

In addition, the formula can be used to estimate the optimal number of employees for;

1. 1.
A single retail outlet, and for all the outlets of a company at provincial and at national level.
2.
Assessment of the potential reduction in theft-related shrink at retail outlets at the city, provincial and national level.
3.
Estimation of the savings to be made towards the profitability of the retail stores.
4.
Assessment of the future employment potential in a particular chain of retail stores in a city, province, and at national level, assuming that retail shrink in the future would be the function of the past.

6.2 Limitations

The formula presented in this paper has its limitations as well.

1. 1.

The proportion, $p$ , for theft-related shrink may vary from store to store, and from city to city within provincial and national boundaries.

The negative correlation coefficient between employee density (ED) and theft-related shrink (TRS) may also vary from store to store, and from city to city within provincial and national boundaries.

Yearly employee cost may also vary from store to store, city to city and within provincial and national boundaries.

In this formula, only employee cost and theft related shrink are considered. There is a possibility of other cost items, such as technology, that is not considered in this formula.

Despite the above mentioned limitations, the formula is found to be robust in optimizing the employee density to reduce theft-related shrink. The limitations related to changes in the TRS proportion, $p$ , in retail shrink, variations in negative correlation between ED and TRS, and changes in yearly employee cost can be taken care of by re-assessing the proportion, $p$ , re-assessing negative relation between Ed and TRS, and by re-assessing yearly employee cost, $C$ . Despite its limitations, the formula has usefulness to researchers, retail managers, and human resource professionals in the retail sector for optimizing employee density that would be helpful in reducing theft- related shrink in retail stores.

Footnotes

Acknowledgments

The author is thankful to the University of Northern BC for providing research facilities for this paper. The author is also thankful to the reviewers for providing constructive feedback to make improvements in this paper. However, the views and opinions expressed in this paper are not necessarily of any institution or organization.

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Deo’s formula for optimal employee density to reduce theft-related shrink

Abstract

Keywords

1. Introduction

1.1 Retail shrink categories

1.2 Role of technology and employees in reducing shrink

1.3 Problem statement and its practical relevance

Assumption A

Assumption B

Assumption C

Assumption D

Assumption E

Assumption F

Assumption G

4.1 Variables and parameters used in the formula

4.2 Formula derivation

Table 1 $Shrink of a retail outlet (May, 2004 to May, 2008)

6.1 Future research directions

Footnotes

Acknowledgments

References

Table 1
$Shrink of a retail outlet (May, 2004 to May, 2008)