Inventory Analysis Techniques

CHAPTER 1 1 INTRODUCTION

2.3 Inventory Analysis Techniques

As discussed previously, inventory management is critical to streamline the business process and increase the company’s cost-efficiency. To further support inventory management, inventory analysis techniques could be one way to improve inventory management performance and cut unnecessary costs. Eveline, et al. (2019) claimed that the primary goal of the inventory analysis techniques is to cut the inventory costs to improve overall business efficiency. Eveline, et al. (2019) further stated that an excess inventory stock leads to a company’s financial burden and increases the chances of loss and damage. On the other hand, insufficient stock leads to low customer satisfaction, loss of potential sales and injure the company’s reputation.

Furthermore, Biswas, et al. (2017) claimed that a company’s inventory contributes to approximately 75 to 80 percent of the total assets for the retailers and wholesalers. Hence, many companies nowadays have implemented different types of inventory analysis to ensure that they have proper control over their inventory to maximise their profit. Eveline, et al. (2019) stated that combining different inventory analysis techniques could produce more obvious improvement of the business performance. Figure 2.41 shows the conceptual framework of inventory analysis techniques, where the output from multiple inventory analysis techniques results in the advancement of procurement performance.

Figure 2.41: Conceptual Framework of Inventory Analysis Techniques (Eveline, et al., 2019)

There is plenty of inventory analysis techniques introduced and used in inventory management systems. In this report, a total of five inventory analysis techniques will be discussed. The five inventory analysis techniques include ABC analysis, HML analysis, VED analysis, Safety Stock (SS) analysis and Economic Order Quantity (EOQ) analysis.

2.3.1 ABC Analysis

ABC analysis, also known as Activity-Based Costs analysis, is a well-known inventory categorisation technique used by many companies to control their inventory items.

ABC analysis applies the Pareto principle and works by categorising the inventory items into three sections: A, B and C (Afolabi, et al., 2017; Biswas, et al., 2017; Sporta, 2018). Each category denotes different levels of significance, and the items will be categorised based on their relevance. Category A includes items with a large investment, category B encompasses items with moderate investment, and category C consists of items with a low investment (Afolabi, et al., 2017). The formula to categorise the inventory items into ABC categories is shown as follows (Biswas, et al., 2017):

𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴 𝐶𝐶𝐶𝐶𝐴𝐴𝐶𝐶𝐴𝐴𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐴𝐴 =𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴 𝐷𝐷𝐷𝐷𝐶𝐶𝐴𝐴𝐴𝐴𝐷𝐷 ×𝑈𝑈𝐴𝐴𝐶𝐶𝐶𝐶 𝑃𝑃𝑃𝑃𝐶𝐶𝑃𝑃𝐷𝐷 where

𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴 𝐷𝐷𝐷𝐷𝐶𝐶𝐴𝐴𝐴𝐴𝐷𝐷 = 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴 𝐴𝐴𝐴𝐴𝐶𝐶𝑛𝑛𝐷𝐷𝑃𝑃 𝐶𝐶𝑜𝑜 𝐴𝐴𝐴𝐴𝐶𝐶𝐶𝐶𝐶𝐶 𝐶𝐶𝐶𝐶𝐴𝐴𝐷𝐷 𝐶𝐶𝐷𝐷𝑃𝑃 𝐶𝐶𝐶𝐶𝐷𝐷𝐶𝐶

(2.1)

According to Afolabi, et al. (2017), there are five steps to implement ABC analysis. The five steps of the implementation are as follows:

a) Determine the annual demand and unit price of each inventory item.

b) Calculate the annual consumption based on the above formula.

c) Sort the inventory items based on the annual consumption in descending order.

d) Calculate the percentages of the annual demand of each item and the annual consumption of each item.

e) Classify each inventory item into A, B and C categories, respectively, based on the percentage value of the annual consumption listed in Table 2.3.

Table 2.3: Rules of ABC Analysis (Biswas, et al., 2017; Nadkarni & Ghewari, 2016) Category Item Ratio Annual Consumption

Ratio

Control Strictness

A About 20% About 80% Strict control

B About 30% About 15% Moderate control

C About 50% About 5% Lenient control

Figure 2.42: Pareto Chart of ABC Classification Analysis (Vrat, 2014)

2.3.2 HML Analysis

HML analysis, also known as High-Medium-Low analysis, is an inventory analysis technique similar to ABC analysis (Dahiwale & Sangode, 2019). The significant difference between HML analysis and ABC analysis is the managing criterion used.

Instead of the use of annual consumption value as the managing criterion in ABC analysis, The HML analysis uses a cost per unit criterion (Biswas, et al., 2017; Jadhav

& Jaybhaye, 2020). Besides, unlike ABC analysis which uses categories A, B, and C, the HML analysis uses the terms High, Medium, and Low for the inventory classification. The high (H) cost category includes the high unit value items, the Medium (M) cost category contains the medium unit value items, and the Low (L) cost category consists of low unit value items. The HML analysis also uses the Pareto principle, but the percentage for each category is slightly different from the ABC analysis. The formula to categorise the inventory items into ABC categories is shown as follows:

𝑃𝑃𝐷𝐷𝑃𝑃𝑃𝑃𝐷𝐷𝐴𝐴𝐶𝐶𝐴𝐴𝑃𝑃𝐷𝐷 𝐶𝐶𝑜𝑜 𝑈𝑈𝐴𝐴𝐶𝐶𝐶𝐶 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶= 𝑈𝑈𝐴𝐴𝐶𝐶𝐶𝐶 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 𝐶𝐶𝐷𝐷𝑃𝑃 𝐼𝐼𝐶𝐶𝐷𝐷𝐶𝐶 𝑇𝑇𝐶𝐶𝐶𝐶𝐴𝐴𝐴𝐴 𝑈𝑈𝐴𝐴𝐶𝐶𝐶𝐶 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 𝐶𝐶𝑜𝑜 𝐴𝐴𝐴𝐴𝐴𝐴 𝐼𝐼𝐶𝐶𝐷𝐷𝐶𝐶𝐶𝐶

According to Jadhav and Jaybhaye (2020) as well as Kumar, et al. (2016), there are four steps to implement HML analysis. The four steps of the implementation are as follows:

a) Compute unit cost for each inventory item.

b) Sort the inventory items based on their unit cost in descending order.

c) Compute the percentage of unit cost for each inventory item based on the above formula.

d) Classify each inventory item into categories H, M, and L, respectively, based on the unit cost ratio listed in Table 2.4.

(2.2)

Table 2.4: Rules of HML Analysis (Kumar, et al., 2016) Category Item Ratio Unit Cost Ratio Control

Strictness

H About 15 to 20% About 75% Strict control

M About 20 to 25% About 15% Moderate control

L About 60 to 70% About 10% Lenient control

2.3.3 Safety Stock (SS) Analysis

Safety stock is the minimum additional quantity of the inventory item to act as a safety margin to avoid the stock-out problem (Biswas, et al., 2017; Tom, et al., 2013). Since the demand could be difficult to determine, the safety stock becomes necessary to satisfy the item demand when it exceeds the expected demand. Although having safety stock can guard against stock-out problems, it will increase the holding cost of the inventory items, especially when there is too much safety stock. Therefore, it is crucial to maintain a balance between the safety stock and customer satisfaction. Biswas, et al (2017) proposed a formula to calculate the safety stock. The formula is as follows:

𝑆𝑆𝑆𝑆 =𝑍𝑍 ×𝐷𝐷 × 𝜎𝜎_𝐿𝐿 where

𝐷𝐷 = 𝐷𝐷𝐷𝐷𝐶𝐶𝐴𝐴𝐴𝐴𝐷𝐷 𝐶𝐶𝐷𝐷𝑃𝑃 𝑦𝑦𝐷𝐷𝐴𝐴𝑃𝑃 (𝐴𝐴𝐴𝐴𝐶𝐶𝐶𝐶𝐶𝐶) 𝑍𝑍= 𝑆𝑆𝐶𝐶𝐴𝐴𝐴𝐴𝐷𝐷𝐴𝐴𝑃𝑃𝐷𝐷 𝑁𝑁𝐶𝐶𝑃𝑃𝐶𝐶𝐴𝐴𝐴𝐴 𝑉𝑉𝐴𝐴𝐴𝐴𝐴𝐴𝐷𝐷

𝜎𝜎_𝐿𝐿 = 𝑆𝑆𝐶𝐶𝐴𝐴𝐴𝐴𝐷𝐷𝐴𝐴𝑃𝑃𝐷𝐷 𝐷𝐷𝐷𝐷𝐷𝐷𝐶𝐶𝐴𝐴𝐶𝐶𝐶𝐶𝐶𝐶𝐴𝐴 𝐶𝐶𝑜𝑜 𝐿𝐿𝐷𝐷𝐴𝐴𝐷𝐷 𝑇𝑇𝐶𝐶𝐶𝐶𝐷𝐷

The above formula assumes that the demand is constant. The Z value is dependent on the cycle service level. For example, if the cycle service level is assumed to be 95%, the Z value will be 1.645 (Biswas, et al., 2017). Since it is tough to determine the cycle service level, and the result might not be accurate when using assumptions, a simplified formula was found from Clarke (2021) and Stitch Labs (2019). The simplified formula is as follows:

(2.3)

𝑆𝑆𝑆𝑆 = (𝑆𝑆_{𝑚𝑚𝑚𝑚𝑚𝑚}×𝐿𝐿_{𝑚𝑚𝑚𝑚𝑚𝑚})−(𝑆𝑆_{𝑚𝑚𝑎𝑎𝑎𝑎}×𝐿𝐿_{𝑚𝑚𝑎𝑎𝑎𝑎}) where

𝑆𝑆𝑆𝑆= 𝑆𝑆𝐴𝐴𝑜𝑜𝐷𝐷𝐶𝐶𝑦𝑦 𝑆𝑆𝐶𝐶𝐶𝐶𝑃𝑃𝑆𝑆

𝑆𝑆_{𝑚𝑚𝑚𝑚𝑚𝑚} = 𝑀𝑀𝐴𝐴𝑀𝑀𝐶𝐶𝐶𝐶𝐴𝐴𝐶𝐶 𝐷𝐷𝐴𝐴𝐶𝐶𝐴𝐴𝑦𝑦 𝐷𝐷𝐷𝐷𝐶𝐶𝐴𝐴𝐴𝐴𝐷𝐷 (𝐻𝐻𝐶𝐶𝑃𝑃ℎ𝐷𝐷𝐶𝐶𝐶𝐶 𝐴𝐴𝐴𝐴𝐶𝐶𝑛𝑛𝐷𝐷𝑃𝑃 𝐶𝐶𝑜𝑜 𝐶𝐶𝐶𝐶𝐷𝐷𝐶𝐶𝐶𝐶 𝐶𝐶𝐶𝐶𝐴𝐴𝐷𝐷) 𝐿𝐿𝑚𝑚𝑚𝑚𝑚𝑚 =𝑀𝑀𝐴𝐴𝑀𝑀𝐶𝐶𝐶𝐶𝐴𝐴𝐶𝐶 𝐿𝐿𝐷𝐷𝐴𝐴𝐷𝐷 𝑇𝑇𝐶𝐶𝐶𝐶𝐷𝐷 𝐶𝐶𝐴𝐴 𝐷𝐷𝐴𝐴𝑦𝑦𝐶𝐶

𝑆𝑆_{𝑚𝑚𝑎𝑎𝑎𝑎} = 𝐴𝐴𝐷𝐷𝐷𝐷𝑃𝑃𝐴𝐴𝑃𝑃𝐷𝐷 𝐷𝐷𝐴𝐴𝐶𝐶𝐴𝐴𝑦𝑦 𝐷𝐷𝐷𝐷𝐶𝐶𝐴𝐴𝐴𝐴𝐷𝐷 (𝐴𝐴𝐷𝐷𝐷𝐷𝑃𝑃𝐴𝐴𝑃𝑃𝐷𝐷 𝐴𝐴𝐴𝐴𝐶𝐶𝑛𝑛𝐷𝐷𝑃𝑃 𝐶𝐶𝑜𝑜 𝐶𝐶𝐶𝐶𝐷𝐷𝐶𝐶 𝐶𝐶𝐶𝐶𝐴𝐴𝐷𝐷) 𝐿𝐿_{𝑚𝑚𝑎𝑎𝑎𝑎} =𝐴𝐴𝐷𝐷𝐷𝐷𝑃𝑃𝐴𝐴𝑃𝑃𝐷𝐷 𝐿𝐿𝐷𝐷𝐴𝐴𝐷𝐷 𝑇𝑇𝐶𝐶𝐶𝐶𝐷𝐷 𝐶𝐶𝐴𝐴 𝐷𝐷𝐴𝐴𝑦𝑦𝐶𝐶

Using the simplified formula above, we can determine the number of stocks the company can hold as a reserved stock to prevent the items from stock-out.

2.3.4 Economic Order Quantity (EOQ) Analysis

Economic Order Quantity is an inventory analysis technique used to identify the optimum amount of inventory items to order each time (Afolabi, et al., 2017; Sporta, 2018; Tom, et al., 2013). In inventory management, the amount of the inventory items ordered will affect the inventory ordering and holding costs. Therefore, EOQ aims to minimise the inventory ordering and holding costs by calculating the optimum amount of items to be ordered to increase the company’s profit. The formula to compute the EOQ of an item is shown as follows (Biswas, et al., 2017; Tom, et al., 2013):

𝐸𝐸𝐸𝐸𝐸𝐸= �2𝐷𝐷𝑆𝑆 𝐻𝐻 where

𝐷𝐷 = 𝐷𝐷𝐷𝐷𝐶𝐶𝐴𝐴𝐴𝐴𝐷𝐷 𝐶𝐶𝐷𝐷𝑃𝑃 𝐶𝐶𝐶𝐶𝐶𝐶𝐷𝐷= 𝑇𝑇𝐶𝐶𝐶𝐶𝐴𝐴𝐴𝐴 𝐴𝐴𝐴𝐴𝐶𝐶𝑛𝑛𝐷𝐷𝑃𝑃 𝐶𝐶𝑜𝑜 𝐴𝐴𝐴𝐴𝐶𝐶𝐶𝐶𝐶𝐶 𝐶𝐶𝐶𝐶𝐴𝐴𝐷𝐷 𝐶𝐶𝐷𝐷𝑃𝑃 𝐶𝐶𝐶𝐶𝐶𝐶𝐷𝐷 𝑆𝑆=𝑅𝑅𝐷𝐷𝐶𝐶𝑃𝑃𝐷𝐷𝐷𝐷𝑃𝑃 𝑃𝑃𝐶𝐶𝐶𝐶𝐶𝐶 = 𝐹𝐹𝐶𝐶𝑀𝑀𝐷𝐷𝐷𝐷 𝑃𝑃𝐶𝐶𝐶𝐶𝐶𝐶 𝐶𝐶𝐷𝐷𝑃𝑃 𝐶𝐶𝐴𝐴𝑃𝑃𝑃𝑃ℎ𝐴𝐴𝐶𝐶𝐷𝐷 𝐶𝐶𝑃𝑃𝐷𝐷𝐷𝐷𝑃𝑃

𝐻𝐻 =𝐻𝐻𝐶𝐶𝐴𝐴𝐷𝐷𝐶𝐶𝐴𝐴𝑃𝑃 𝑃𝑃𝐶𝐶𝐶𝐶𝐶𝐶 𝐶𝐶𝐷𝐷𝑃𝑃 𝐶𝐶𝐶𝐶𝐶𝐶𝐷𝐷 (𝐴𝐴𝐴𝐴𝐶𝐶𝐶𝐶)

According to Afolabi, et al. (2017), there are four assumptions made by the EOQ model. The assumptions are as follows:

a) Demand is constant.

b) Stock is depleted linearly and constantly.

c) No discount is implied on the quantity of the order purchases.

d) The time interval between placing and receiving the order is fixed.

(2.4)

(2.5)

2.3.5 Conclusion

All in all, four types of inventory analysis techniques, ABC analysis, HML analysis, SS analysis and EOQ analysis, have been discussed in this section. Each of the inventory analysis techniques produces different analysis results. In my opinion, the inventory management system should be able to compute the safety stock by using SS analysis and the optimum amount of orders purchased each time. The reason is that the system can alert the admin when an item is lower than the safety stock. Besides, the admin can also know the optimum amount of inventory items to be ordered each time to reduce unnecessary costs. The ABC analysis and HML analysis both helps to classify the inventory items into different categories. These analyses allow the admin to make changes on the inventory items based on the categories of the inventory items.

I think the system can compute and show the categories of the inventory items by either ABC analysis or HML analysis. However, it depends on the admin to change the control of the inventory items or set specific procedures for each category.