# Analytical Business Model for Sustainable Distributed Retail Enterprises in a Competitive Market

^{*}

## Abstract

**:**

## 1. Introduction

**Figure 2.**Problems caused by poor data quality. Adapted from [8].

- ▪
- Development of a newly proposed analytical ARANN model that could intelligently assist distributed retail enterprise management within competitive markets to arrange products optimally on store shelves so that customers will purchase more products than planned in order to achieve an optimal profit level.
- ▪
- Detailed experimental evaluations conducted on the sustainable ARANN model as measures of its performance using publicly available data and a volume of real-life retail data sets captured in ever-changing markets.
- ▪
- Application of a robust business model in terms of (i) deployment scenarios, (ii) distributed and centralized analytics, (iii) time and memory scalability, and (iv) benchmark with classical methods for ease of implementation for managerial practices in IT.

## 2. Background Studies

#### 2.1. Related Work

#### 2.2. Association Rules

#### 2.2.1. Support Value

#### 2.2.2. Confidence Value

#### 2.3. Artificial Neural Networks

_{i}against corresponding weights w

_{i}and compares the ANN output to the threshold value, Ө. The threshold is determined by the inputs used. Let X be the net weighted input of the neuron, as shown in Equation (3). The decision of X is for discrete cases since it takes only certain values:

_{i}is the input signal, w

_{i}is the weight of input and n is the number of neurons.

## 3. Proposed Methodology for Sustainable Business Enterprises

#### 3.1. Proposed System Model for Distributed Retail Enterprises

Pseudo-code | |
---|---|

Steps | Input: Transactional data in database (D) = {t_{1}, t_{2}, t_{3}, .., t_{n}} |

Support () | |

Confidence () | |

Weights (W) = {w_{1}, w_{2}, w_{3}, .., w_{n}} | |

Output: Products pattern | |

Step 1: D = {t_{1}, t_{2}, t_{3}, .., t_{n}} //Transactions in the database | |

Step 2: C_{k} = Candidate item set of size k | |

Step 3: F_{k} = frequent item set of size k | |

{ | |

for (k =1; F_{k} != Ø; k++) // F_{k} is not equal to empty set. | |

{ | |

Scan the entire D to generate candidate sets C_{k} | |

{ | |

Compare candidate support count from C_{k} with the minimum support count to generate F_{k} | |

} | |

} | |

Step 4: Generate Support () & Confidence () | |

{ | |

Step 5: Input Support () & Confidence () into Neuron 1 (N_{1}) and Neuron 2 (N_{2}) as inputs | |

Step 6: Generate N_{1} by summing of the inputs with the corresponding weights and apply the output into sigmoid function | |

Step 7: Generate N_{2} by summing of the inputs with the corresponding weights and apply the output into sigmoid function | |

Step 8: Generate the summation of N_{1} & N_{2} after the sigmoid function and apply the output into sigmoid function to obtain Degree of Belief (DoB) | |

Step 9: Display products pattern where
DoB ≥ ARANN activation | |

} | |

} |

_{1}as the inputs and are multiplied with the corresponding weights.

_{1}after the sigmoid function

_{2}as the inputs and are multiplied with the corresponding weights:

_{2}after the sigmoid function

_{1}and N

_{2}are Neuron 1 and 2 respectively; W

_{1}, W

_{2}

_{,}W

_{3}, W

_{4}, W

_{5}and W

_{6}are the corresponding weights; O

_{1}is Neuron 1 output after sigmoid function; O

_{2}is Neuron 2 output after sigmoid function, F is input to final Neuron and ARANN activation is the threshold value set.

#### 3.2. Evaluation Mechanism

**Table 2.**Confusion matrix. Adapted from [36].

Predicted | |||
---|---|---|---|

Actual | True | False | |

True | a | b | |

False | c | d |

#### 3.3. Scenario—Arrangement of Products on Shelves for Distributed Retail Branches

Market-basket Transaction Data—Branch 3 | |
---|---|

TID | ITEMS |

T300 | Colgate, Vaseline, Geisha, Margarine, Bread |

T301 | Margarine, Bread, Coke, Colgate, Vaseline |

T302 | Coke, Colgate, Chocolate, Bread, Sweets, Margarine |

T303 | Geisha, Colgate, Chocolate, Towel, Vaseline, Sweets |

T304 | Colgate, Vaseline, Sweets, Chocolate, Bread, Margarine, Coke |

- >= 0.75 strongly connected products (strongly accepted)
- >= 0.65 moderately connected products (accepted)
- < 0.65 weakly connected products (rejected)

**{Colgate, Vaseline} => {Bread}**

_{1}= Supw

_{1}+ Conw

_{3}N

_{2}= Conw

_{4}+ Supw

_{2}

_{1}= $\frac{1}{1+{{\displaystyle e}}^{-N1}}=\frac{1}{1+{{\displaystyle e}}^{-0.81}}=0.69$ O

_{2}= $\frac{1}{1+{{\displaystyle e}}^{-N2}}=\frac{1}{1+{{\displaystyle e}}^{-0.81}}=0.69$

_{1}+ w6O

_{2}

**{Coke} => {Bread}**

_{1}+ w6O

_{2}

Market-basket Transaction Data—Branch 4 | |
---|---|

TID | ITEMS |

T400 | Maize meal, Beef, Fish, Cooking oil, Soups, Bread, Coke |

T401 | Cooking oil, Beans, Beef, Soups, Maize meal |

T402 | Rice, Fish, Soups, Cooking oil, Bread |

T403 | Fruits, Coke, Bread, Milk, Chocolate, Soups |

T404 | Bread, Beef, Fruit, Coke, Sweets, Maize meal |

**{Maize meal} => {Beef}**

_{1}+ w6O

_{2}

**moderately**connected and is

**accepted.**

**{Chocolate} => {Towel}**

_{1}= (0.20 × 0.20) + (0.33 × 0.20) N

_{2}= (0.33 × 0.20) + (0.20 × 0.20)

_{1}= $\frac{1}{1+{{\displaystyle e}}^{-0.11}}=0.53$ O

_{2}= $\frac{1}{1+{{\displaystyle e}}^{-0.11}}=0.53$

**weakly**connected and is

**rejected.**

## 4. Experimental Evaluations: Results and Discussions

#### 4.1. Experimental Setup

Body lotion | Colgate | Rice | Maize meal | ||
---|---|---|---|---|---|

Meat | Rice | Roll on | Cooking oil | Body lotion | |

- | - | - | - | - | - |

Drink | Roll on | Mince | Coke | Colgate | Perfume |

Bread | Sugar | Rice | Meat | Salt | Cooking oil | Flour | Soup |
---|---|---|---|---|---|---|---|

- | - | - | - | - | - | - | - |

Fruits | Sugar | Meat | Cooking oil | Salt | Soap | Bread |

**Table 7.**Sample of public data [37].

Fish | Orange juice | Tea | Wine | Peanuts | Canned soup | Bread | Beer |
---|---|---|---|---|---|---|---|

- | - | - | - | - | - | - | - |

Cookies | Fish | Orange juice | Tea | Wine | Peanuts | Canned soup | Chocolate milk |

#### 4.2. Experiment 1: Observations of ARANN with Varying Activation in Distributed Analytics

Dataset Branch 1 | Patterns Generated | DoB | ARANN Cooperative Decision with | |
---|---|---|---|---|

60 >= DoB < 70 | DoB >= 70 | |||

Roll on, perfume => Colgate | 0.71 | N/A | Strongly accepted | |

Colgate, Body lotion => roll-on | 0.69 | Accepted | N/A | |

Colgate => Body lotion | 0.71 | N/A | Strongly accepted | |

Bread, Milk => Eggs | 0.70 | N/A | Strongly accepted | |

Rice, Maize meal => soup | 0.62 | Accepted | N/A | |

Bread => Drink | 0.79 | N/A | Strongly accepted | |

Bread => Sugar | 0.76 | N/A | Strongly accepted |

Dataset Branch 1 | Patterns Generated | DoB | ARANN Cooperative Decision with | |
---|---|---|---|---|

60 >= DoB < 70 | DoB >= 70 | |||

Meat, Salt => Cooking_oil | 0.64 | Accepted | N/A | |

Meat => Salt | 0.71 | N/A | Strongly Accepted | |

Bread, rice => Eggs | 0.66 | Accepted | N/A | |

Bread => Lotion | 0.65 | Accepted | N/A | |

Bread => Eggs | 0.65 | Accepted | N/A |

Dataset Branch 1 | Patterns Generated | DoB | ARANN Cooperative Decision with | |
---|---|---|---|---|

60 >= DoB < 70 | DoB >= 70 | |||

Fish, Canned soup => Wine | 0.64 | Accepted | N/A | |

Fish => Canned soup | 0.74 | N/A | Strongly Accepted | |

Tea, Cookies => Peanuts | 0.61 | Accepted | N/A | |

Bread => Chocolate milk | 0.73 | N/A | Strongly accepted | |

Bread, Chocolate milk => Tea | 0.64 | Accepted | N/A | |

Beer => Tea | 0.67 | Accepted | N/A | |

Beer => Chocolate milk | 0.69 | Accepted | N/A | |

Wine => Beer | 0.69 | Accepted | N/A | |

Canned soup => Bread | 0.79 | N/A | Strongly Accepted | |

Orange juice => Bread | 0.73 | N/A | Strongly Accepted | |

Peanuts, Bread => Canned soup | 0.67 | Accepted | N/A | |

Tea, Bread => Orange juice | 0.65 | Accepted | N/A |

Dataset Branch 1 | Patterns Generated | DoB | ARANN Cooperative Decision with | |
---|---|---|---|---|

60 >= DoB < 70 | DoB >= 70 | |||

Fish, Canned soup => Wine | 0.64 | Accepted | N/A | |

Fish => Canned soup | 0.74 | N/A | Strongly Accepted | |

Tea, Cookies => Peanuts | 0.61 | Accepted | N/A | |

Bread => Chocolate milk | 0.72 | N/A | Strongly Accepted | |

Bread, Chocolate milk => Tea | 0.66 | Accepted | N/A | |

Beer => Tea | 0.67 | Accepted | N/A | |

Beer => Chocolate milk | 0.67 | Accepted | N/A | |

Wine => Beer | 0.70 | N/A | Strongly accepted | |

Canned soup => Bread | 0.80 | N/A | Strongly accepted | |

Orange juice => Bread | 0.73 | N/A | Strongly accepted | |

Peanuts, Bread => Canned soup | 0.68 | Accepted | N/A | |

Tea, Bread => Orange juice | 0.67 | Accepted | N/A |

#### 4.3 Experiment 2: Performance Evaluations of ARANN in Comparison with Classical Methods

Dataset | Algorithms | No. of Patterns | Correctly Classifies sets (a, d) | Incorrectly Classified sets (b, c) | Error Rate |
---|---|---|---|---|---|

Real life Branch 1 (66 Records) | AR | 10 | 7 | 3 | 30% |

ANN | 10 | 6 | 4 | 40% | |

ARANN | 6 | 5 | 1 | 17% | |

Branch 2 (66 Records) | AR | 10 | 8 | 2 | 20% |

ANN | 10 | 8 | 2 | 20% | |

ARANN | 7 | 6 | 1 | 14% | |

Public Branch 3 (200 Records) | AR | 10 | 8 | 2 | 20% |

ANN | 10 | 6 | 4 | 40% | |

ARANN | 6 | 5 | 1 | 17% | |

Branch 4 (200 Records) | AR | 10 | 8 | 2 | 20% |

ANN | 10 | 7 | 3 | 30% | |

ARANN | 8 | 6 | 2 | 25% |

#### 4.4 Experiment 3: Comparing Performance of Distributed and Centralized Retail Analytics

## 5. Conclusions

## Acknowledgments

## Author Contributions

## Conflicts of Interest

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**MDPI and ACS Style**

Matobobo, C.; Osunmakinde, I.O.
Analytical Business Model for Sustainable Distributed Retail Enterprises in a Competitive Market. *Sustainability* **2016**, *8*, 140.
https://doi.org/10.3390/su8020140

**AMA Style**

Matobobo C, Osunmakinde IO.
Analytical Business Model for Sustainable Distributed Retail Enterprises in a Competitive Market. *Sustainability*. 2016; 8(2):140.
https://doi.org/10.3390/su8020140

**Chicago/Turabian Style**

Matobobo, Courage, and Isaac O. Osunmakinde.
2016. "Analytical Business Model for Sustainable Distributed Retail Enterprises in a Competitive Market" *Sustainability* 8, no. 2: 140.
https://doi.org/10.3390/su8020140