Radio Frequency Identification-Enabled Inventory Management Approach for Grocery Supply Chains

Pedro M. ReyesBaylor University

Gregory Frazier & Edmund PraterThe University of Texas at Arlington

Background

• Increased competition in the grocery retailing industry is creating pressure to simultaneously control inventory costs and improve customer service.

• Also faced with a growing number of complex technological options.

Background

• The growing trend of product proliferation adds to the complexity of inventory management.

• Effective inventory management is critical to grocery retail success and customer service is an increasingly focal point.

Background

• Grocery retail operations management depends largely on the ability to meet customers’ needs.

• However, retailers (and channel members) typically use historical sales, or observed demand, to decide what to stock and how much.

Background

• It is important to realize that observed demand is often distorted due to “temporary stock outs” and product substitution.

• Empirical evidence suggests that the occurrence of “temporary stock outs” is common.

Efficient Consumer Response

• Kurt Salmon Report (1993)– Estimated $10 billion could be saved by cutting

throughput time from supplier packing line to consumer in the United States from 104 to 61 days.

Efficient Consumer Response

• Main objective is to maintain minimal inventories at each echelon, without jeopardizing service levels.

• The secondary focus is quick and efficient response to market demands.

It should be noted that the gains in inventory reduction may result in more frequent replenishment orders, but these increased costs are most likely to decrease as new technologies (such as RFID) are further developed (and exploited) for a more agile supply chain.

Efficient Consumer Response

ECR update (Bowersox et al., 1999): a study of nine grocery supply chains that had implemented ECR (1992-1997) shows mixed results

– Decreased average inventory turns– Increased inventory levels– Net profit increased by 22% (not due to

improved operations, but rather due to larger “forward buying” purchases)

Overview of Technologies Adopted by the Grocery Retail Industry

• 1980s– Scanner system– Bar codes– Electronic cash register

• 1990s– Category management– Continuous

replenishment– Direct store delivery– Computer assisted

ordering– Cross-docking

Radio Frequency Identification

• Another technology application that can provide many of the benefits of ECR.

• The RFID tag can provide highly accurate and real-time information.

• Improved quality of information allows for better inventory management approaches.

Radio Frequency Identification• An RFID tag consists of two main

components:– an antenna– a chip that contains an electronic product code

(EPC), allowing tags to store more information than bar codes

• RFID technology has been available for many years, but only recently sparked with massive interest.

Radio Frequency Identification• RFID technology enables

– Minimizing time delays in a coordinated, comprehensive, and effective response to customer demand

– and the logistics thereof

Replenishment System

• Periodic review

• Replenishment orders are allowed only a designated times due to transportation economies

• Common practice is to “pack-out” the shelves

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ii CPSROP −=

Proposed Model

Although RFID technology could be used with any inventory management model, it (the RFID technology) is an enabling technology that is practical only with high levels of inventory record accuracy.

Theoretical (Key Assumptions)

• The use of RFID tags is economical at the item-level.

• Infrastructure in the grocery retail industry is available to signal when products (item-level tags) are low on inventory.

How It Would Work

• At inventory review time, all items in the store are quickly “zapped” by a central RF sending unit to obtain current inv. levels

• The computer then compares these levels with the order point levels for each product

• The order size is then determined that would bring the total number of units above the order point levels

• Orders are electronically sent to the DC

Proposed Approach

• Order if current inventory is below ROP

where

LT = order delivery lead timedi = daily demand rate SSit = safety stocknt = number of days until next order placement timeσdi = standard deviation of daily demand

( ) ititi SSdLTnROP ++=

( ) 2idtit LTnzSS σ+=

Consumer Behavior Issues

• The actual or final demand (resulting demand) is a function of two general factors– Consumers’ preferences prior to entering the store

• “the natural demand rate”– Influences on these preferences due to in-store

display of products• Proliferation of the other category products• Two types of product elasticities

– Cross elasticity– Cross-space elasticity

Conceptual Framework used for the Resulting Demand Function

Resulting Demand

No preference (random)

Substituting when another product

not in assortmentNatural demand for product in the category assortment

Switching when product is in stock

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(+/–)

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Mathematical Expression for the Resulting Demand Function

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1. the natural demand from the consumer’s first preference

2. the potential switching to another product that is carried in the category assortment

3. the potential switching from another product that is carried in the category assortment

4. the randomness effect due to no initial first preference

5. the substitution effect due to demand for a product that is not carried in the category assortment

Experimental Methodology

• A simulation experiment was designed to address several research questions of interest

• Several main treatment factors and environmental factors were included

Main Treatment Factors

1. Inventory model (Model 1, Model 2)2. Casepack size (6, 12, 24 units per case)3. Frequency of deliver (3, 6 times/week)4. DC stockout substitution policy (no

substitute, substitute similar item)5. Availability to DC of current store inventory

levels (yes or no)

Environmental Factors• Product daily demand rate (high, low)

– A product category with 5 products was used– High demand rate: 15, 10, 8, 6, 4 units/day– Low demand rate: 6, 4, 4, 3, 2 units/day

• Existence of artificial outlier demands (occasional outlier demands, none)– Outlier demand: 5% probability of an additional

2-4 times average daily demand for a product

Product Substitution

• If a product is out of stock in the store, research has shown that most consumers will substitute a similar product, but some are brand loyal and will go elsewhere

• We used a conservative estimate of 60% of consumers that will substitute. The substitute product was randomly selected from the remaining products in stock.

Research Questions

1. How does the choice of inventory modelaffect supply chain performance?

2. How does casepack size affect supply chain performance?

3. How does frequency of delivery affect supply chain performance?

4. How does the DC substitution policy affect supply chain performance?

Research Questions

5. How does the DC’s access to current retail store inventory data affect performance?

6. Are these results affected by the levels of product demands at the stores?

7. Are these results affected by the existence of artificial outlier demands at the stores?

Simulated Supply Chain

• 1 grocery distribution center (DC)• 10 grocery retail stores• All stores are same size and have same

average demand rates; only the timing of random demand is different

Simulated Grocery Supply Chain

DistributionCenter

Store 1

Store 2

Store 10

:::

:::

Mfgr. 1

Mfgr. 2

Mfgr. NOrders

Products

Orders

Products

Information Information

Performance Measures

• Average inventory level at retail stores• Average inventory level at DC• Average # of orders placed by stores• Total # of DC product-days stocked out• Actual service level % at stores

1 – [(total # of lost sales)/(total demand)]

Simulation Results

• The choice of inventory model had the greatest impact on supply chain performance

• Proposed Model resulted in a substantial decrease in average store inventory levels, with only a slight decrease in customer service levels (<1.4%)

• This allows stores to reduce the shelf space for current items and add new items

Simulation Results

• Smaller casepack sizes with Proposed Model resulted in substantially lower average inventory levels (42% for 24 6), with only a slight reduction in services levels (<1%)

• Large grocery chains have the leverage to force manufacturers to reduce casepack sizes

Simulation Results

• More frequent deliveries with Proposed Model resulted in a substantial reduction in average store inventory levels (26%) with only a slight decrease in service levels (<0.3%)

• However, this benefit must be weighed against the additional transportation cost of more frequent deliveries

• The other factors had only small impacts

Simulation Results

• Proposed Model requires greater accuracy of inventory data, but this accuracy is possible when using RFID tags

• The use of RFID tags and Proposed Model allows for lower inventory investment and more shelf space available for additional products, which provides a competitive advantage

Expected Results Using RFID

• Retail store– Reduced stock outs– Increased sales– Optimized inventories

• Distribution Center– Improved fill-rate

• Overall– Reduced the “touches” (and workload)

Discussion

• Research related to Homeland Security– Terrorist attacks– Natural disasters– Large-scale emergencies

• RFID technology enables– Minimizing time delays in a coordinated,

comprehensive, and effective response– And the logistics thereof

Questions