# Creating Sustainable Order Fulfillment Processes through Managing the Risk: Evidence from the Disposable Products Industry

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

- Finding out the factors that affect the order fulfillment process for the Disposable Products Industry in China,
- Contrasting the factors that affect the order fulfillment process for the Disposable Products Industry in China and other countries,
- Identifying the differences in focus for the order fulfillment process among various parties in the Disposable Products Industry,
- Investigating the relationship of the focus for the order fulfillment process and the performance of order fulfillment for various parties along the chain in the Disposable Products Industry,
- Identifying the normal practices for the order fulfillment process that are used by the companies with outstanding order fulfillment performances.

## 2. Related Research

#### 2.1. The Order Fulfillment Process and its Relationship with Other Business Processes

#### 2.2. Variation in Concerns and Strategies over Order Fulfillment

#### 2.3. Special Issues over Order Fulfillment

## 3. Conceptual Modeling: Definition, Purpose, and Benefits

#### 3.1. Risk Factors for Order Fulfillment

- Identify some risk factors that are involved in the Disposable Products Industry,
- State their frequencies and the impacts,
- Identify the role and significance of the order fulfillment process of these risk factors,
- Name the specific risk factors involved in the order fulfillment process,
- State the internal and external perspectives,
- Name the departments (internal) and parties (external) involved in the order fulfillment process with the company,
- Describe the relationship with these parties and departments and their roles,
- State the frequencies and the impacts of these internal factors and external factors.

#### 3.2. Order Fulfillment Risk Factors Variations for Different Characteristics

- Industry
- Product
- Firm
- Role
- Country

- State the measures and the task forces for performance measurement over the order fulfillment process,
- State the regulations and norms provided by the industry and regulatory bodies,
- State whether the process measurements are specific to:
- ⚬
- industry
- ⚬
- product
- ⚬
- firm
- ⚬
- role
- ⚬
- country,

- Rank the importance of the measures,
- Define the perfect order fulfillment process,
- State the relationship between the order fulfillment and the various risk factors.

## 4. Data Analysis

#### 4.1. Qualitative Research Analysis Approach

#### 4.2. Quantitative Research Analysis Approach

#### 4.3. Sampling and Survey Design

#### 4.4. Order fulfillment Paths in a Retail Supply Chain

**Note**Vendors (V), distribution centers (DC), direct-to-customer fulfillment centers (DTC), retail stores (R).) to fill online orders, known as integrated fulfillment [55]. In this case, retailers develop unified operational processes that combine warehousing activities for the store and online channels. The other option available to retailers is to use dedicated direct-to-customer order fulfillment centers. This dedicated fulfillment method requires a significant capital investment, process redesign, and coordinated product flows [56]. A critical threshold of online sales is needed to justify the high operating costs and inventory risks due to uncertain seasonal variations of demand in the online channel. Another approach used by retailers is to leverage inventory in local stores to fill online orders, known as store fulfillment [43]. If inventory investments and operational costs of supporting multiple channels are deemed prohibitive, retailers can also use their vendors to support online sales. In vendor fulfillment, retailers assign orders from online customers to vendors for fulfillment. The vendor fulfillment option allows retailers to sell additional products online without stocking inventory in the store and without the operational burden of filling online orders.

#### 4.5. Retail Distribution System

_{j}and T

_{j}represent the arcs that start and end at node j, respectively. The quantity of product k shipped over arc e during time t is given by the decision variables ${X}_{et}^{k}$. The unit inbound shipping cost for product k is represented by the cost parameter ${C}_{e}^{k}:e\in {E}_{I}$ and unit outbound shipping cost is represented by ${C}_{e}^{k}:\text{}e\in {E}_{O}$. Each unit of product k requires a storage space of αk, whereas the total available storage space at the location $\omega \in \mathsf{\Omega}$ is given by ${k}_{\omega}$. The end-of-season inventory ${I}_{\omega t}^{k}$ at location $\omega $ incurs inventory holding costs at the rate of ${h}_{\omega}^{k}$ per unit.

_{j}to signify the additional financial burden of operating separate fulfillment facilities over existing DCs. Another fulfillment option used by retailers is to fill online orders directly from vendors, represented by set $\mathbb{V}$ (i.e., vendor fulfillment). This option can be used by the retailer to sell an assortment of products in the online channel that it does not hold in stock. The fourth option is to fulfill online orders from retail stores (i.e., store fulfillment), where orders are shipped directly from retail stores to online customers.

#### 4.6. Concerns and Performance Focus Lead to the Order Fulfillment Failure

## 5. Discussion

#### 5.1. Qualitative Research

#### 5.2. Quantitative Research

#### 5.2.1. Loss in Order Fulfillment

#### 5.2.2. Likelihood of Order Fulfillment

#### 5.2.3. Performance References

#### 5.2.4. Order Fulfillment: Fractional Multinomial Logit Model

_{ik}represents the kth outcome for observation i, and x

_{i}, i = 1, ..., N, is a vector of exogenous covariates. The FMlogit model assumes that M conditional means have a multinomial logit functional form in linear indices as:

## 6. Conclusions and Further Studies

- It is interesting to break down Disposable Products into various groups as the respondents in the qualitative suggested that the product nature may affect the order fulfillment failure.
- The current research contrasts the differences in order fulfillment likelihood, loss and performance for various parts of China, and it may be a good idea to extend the analysis to different parts of the world, especially since internationalization will impose a difference on the current study.
- The current study suggests that it is more important to focus on the long-term order fulfillment factors, and it may be reasonable to provide a longitudinal study to contrast the long-term order fulfillment performances for companies with different levels of attention towards this long-term factor.

## Author Contributions

## Funding

## Conflicts of Interest

## Appendix A

**Figure A1.**Disposable Products Demand by Usage in Europe in 2013 [55].

**Figure A2.**Value stream defined order fulfillment process [9].

**Figure A3.**Transactional order fulfillment [13].

**Figure A4.**Relationship of the order fulfillment with the other process and entities [11].

**Figure A5.**Strategic order fulfillment process and supply chain management process [13].

**Figure A7.**Concerns comparison among various layers and parties of the disposable products industries.

## Appendix B

**Table A1.**Model significance for the regression on ranking and order picking order fulfillment failure likelihood.

Coefficients^{a} | ||||||
---|---|---|---|---|---|---|

Model | Unstandardized Coefficients | Standardized Coefficients | t | Sig. | ||

B | Std. Error | Beta | ||||

1 | (Constant) | 1.645 | 0.577 | 2.850 | 0.005 | |

Delay (Rank) | 0.013 | 0.067 | 0.019 | 0.201 | 0.841 | |

Doc (Rank) | 0.130 | 0.062 | 0.203 | 2.118 | 0.036 | |

Condition (Rank) | 0.003 | 0.070 | 0.004 | 0.039 | 0.969 | |

Response (Rank) | –0.004 | 0.080 | −0.005 | −0.050 | 0.960 | |

Cost (Rank) | 0.075 | 0.065 | 0.113 | 1.147 | 0.254 | |

Service (Rank) | 0.014 | 0.055 | 0.025 | 0.251 | 0.803 |

^{a}means Coefficient

^{0.05%}.

**Table A2.**Model significance for the regression on ranking and sustainability order fulfillment failure likelihood.

Coefficients^{a} | ||||||
---|---|---|---|---|---|---|

Model | Unstandardized Coefficients | Standardized Coefficients | t | Sig. | ||

B | Std. Error | Beta | ||||

1 | (Constant) | 4.939 | 0.593 | 8.324 | 0.000 | |

Delay (Rank) | −0.124 | 0.071 | −0.162 | −1.751 | 0.083 | |

Doc (Rank) | 0.073 | 0.064 | 0.105 | 1.135 | 0.259 | |

Condition (Rank) | −0.049 | 0.072 | −0.064 | −0.678 | 0.499 | |

Response (Rank) | −0.238 | 0.082 | −0.260 | −2.889 | 0.005 | |

Cost (Rank) | −0.140 | 0.068 | −0.196 | −2.076 | 0.040 | |

Service (Rank) | −0.036 | 0.057 | −0.059 | −0.632 | 0.529 |

^{a}means Coefficient

^{0.05%}.

**Table A3.**Model significance for the regression on ranking and supply chain order fulfillment failure likelihood.

Coefficients^{a} | ||||||
---|---|---|---|---|---|---|

Model | Unstandardized Coefficients | Standardized Coefficients | t | Sig. | ||

B | Std. Error | Beta | ||||

1 | (Constant) | 4.155 | 0.596 | 6.974 | 0.000 | |

Delay (Rank) | −0.131 | 0.068 | −0.189 | −1.942 | 0.055 | |

Doc (Rank) | 0.058 | 0.063 | 0.089 | 0.913 | 0.363 | |

Condition (Rank) | −0.045 | 0.072 | −0.062 | −0.619 | 0.537 | |

Response (Rank) | −0.016 | 0.081 | −0.018 | −0.192 | 0.848 | |

Cost (Rank) | −0.081 | 0.068 | −0.120 | −1.195 | 0.235 | |

Service (Rank) | −0.005 | 0.056 | −0.009 | −0.091 | 0.928 |

^{a}means Coefficient

^{0.05%}.

Coefficients^{a} | ||||||
---|---|---|---|---|---|---|

Model | Unstandardized Coefficients | Standardized Coefficients | t | Sig. | ||

B | Std. Error | Beta | ||||

1 | (Constant) | 3.086 | 0.412 | 7.481 | 0.000 | |

Logistics | 0.167 | 0.098 | 0.204 | 1.697 | 0.093 | |

Order picking | −0.132 | 0.108 | −0.147 | −1.219 | 0.226 | |

Product nature | 0.130 | 0.103 | 0.147 | 1.265 | 0.209 | |

Sustainability | −0.262 | 0.117 | −0.324 | −2.245 | 0.027 | |

Information sharing | 0.218 | 0.133 | 0.254 | 1.636 | 0.105 | |

Supply chain strategies | 0.057 | 0.130 | 0.069 | 0.440 | 0.661 | |

Logistics (Likely) | −0.047 | 0.090 | −0.063 | −0.521 | 0.603 | |

Order picking (likely) | −0.125 | 0.109 | −0.134 | −1.150 | 0.253 | |

Product nature (likely) | 0.030 | 0.096 | 0.040 | 0.308 | 0.759 | |

Sustainability (likely) | 0.149 | 0.103 | 0.182 | 1.442 | 0.153 | |

Information sharing (likely) | −0.222 | 0.126 | −0.251 | −1.755 | 0.082 | |

Supply chain (likely) | 0.189 | 0.113 | 0.231 | 1.670 | 0.098 |

^{a}means Coefficient

^{0.05%}.

Coefficients^{a} | ||||||
---|---|---|---|---|---|---|

Model | Unstandardized Coefficients | Standardized Coefficients | t | Sig. | ||

B | Std. Error | Beta | ||||

1 | (Constant) | 3.525 | 0.413 | 8.546 | 0.000 | |

Logistics | 0.217 | 0.102 | 0.260 | 2.116 | 0.037 | |

Order picking | −0.132 | 0.109 | −0.145 | −1.215 | 0.227 | |

Product nature | 0.134 | 0.103 | 0.149 | 1.297 | 0.198 | |

Sustainability | −0.105 | 0.116 | −0.127 | −0.905 | 0.368 | |

Information sharing | −0.107 | 0.131 | −0.123 | −0.818 | 0.415 | |

Supply chain strategies | 0.103 | 0.141 | 0.121 | 0.732 | 0.466 | |

Logistics (Likely) | −0.171 | 0.094 | −0.225 | −1.812 | 0.073 | |

Order picking (likely) | −0.120 | 0.111 | −0.128 | −1.078 | 0.284 | |

Product nature (likely) | −0.105 | 0.105 | −0.135 | −1.003 | 0.318 | |

Sustainability (likely) | 0.180 | 0.104 | 0.213 | 1.738 | 0.085 | |

Information sharing (likely) | 0.136 | 0.125 | 0.150 | 1.093 | 0.277 | |

Supply chain (likely) | 0.105 | 0.117 | 0.127 | 0.894 | 0.374 |

^{a}means Coefficient

^{0.05%}.

Coefficients^{a} | ||||||
---|---|---|---|---|---|---|

Model | Unstandardized Coefficients | Standardized Coefficients | t | Sig. | ||

B | Std. Error | Beta | ||||

1 | (Constant) | 3.314 | 0.440 | 7.540 | 0.000 | |

Logistics | 0.121 | 0.106 | 0.141 | 1.139 | 0.257 | |

Order picking | −0.183 | 0.115 | −0.198 | −1.597 | 0.114 | |

Product nature | 0.161 | 0.109 | 0.174 | 1.473 | 0.144 | |

Sustainability | −0.089 | 0.125 | −0.106 | −0.710 | 0.480 | |

Information sharing | −0.036 | 0.143 | −0.040 | −0.255 | 0.800 | |

Supply chain strategies | 0.060 | 0.150 | 0.069 | 0.402 | 0.689 | |

Logistics (Likely) | 0.056 | 0.099 | 0.072 | 0.568 | 0.571 | |

Order picking (likely) | −0.219 | 0.119 | −0.227 | −1.833 | 0.070 | |

Product nature (likely) | −0.074 | 0.112 | −0.092 | −0.660 | 0.511 | |

Sustainability (likely) | 0.157 | 0.110 | 0.181 | 1.427 | 0.157 | |

Information sharing (likely) | 0.021 | 0.135 | 0.023 | 0.156 | 0.876 | |

Supply chain (likely) | 0.126 | 0.127 | 0.149 | 0.990 | 0.325 |

^{a}means Coefficient

^{0.05%}.

Coefficients^{a} | ||||||
---|---|---|---|---|---|---|

Model | Unstandardized Coefficients | Standardized Coefficients | t | Sig. | ||

B | Std. Error | Beta | ||||

1 | (Constant) | 3.209 | 0.441 | 7.270 | 0.000 | |

Logistics | 0.219 | 0.112 | 0.254 | 10.951 | 0.054 | |

Order picking | −0.023 | 0.118 | −0.024 | −0.194 | 0.846 | |

Product nature | 0.150 | 0.119 | 0.158 | 1.266 | 0.209 | |

Sustainability | −0.223 | 0.128 | −0.260 | −1.739 | 0.085 | |

Information sharing | 0.099 | 0.144 | 0.108 | 0.685 | 0.495 | |

Supply chain strategies | 0.048 | 0.155 | 0.054 | 0.311 | 0.757 | |

Logistics (Likely) | −0.009 | 0.108 | −0.012 | −0.085 | 0.932 | |

Order picking (likely) | −0.178 | 0.122 | −0.184 | −1.458 | 0.148 | |

Product nature (likely) | −0.030 | 0.115 | −0.037 | −0.259 | 0.796 | |

Sustainability (likely) | 0.092 | 0.113 | 0.107 | 0.821 | 0.414 | |

Information sharing (likely) | −0.184 | 0.137 | −0.194 | −1.340 | 0.184 | |

Supply chain (likely) | 0.115 | 0.127 | 0.136 | 0.907 | 0.367 |

^{a}means Coefficient

^{0.05%}.

Coefficients^{a} | ||||||
---|---|---|---|---|---|---|

Model | Unstandardized Coefficients | Standardized Coefficients | t | Sig. | ||

B | Std. Error | Beta | ||||

1 | (Constant) | 3.052 | 0.436 | 7.000 | 0.000 | |

Logistics | 0.117 | 0.104 | 0.146 | 1.126 | 0.263 | |

Order picking | 0.046 | 0.113 | 0.053 | 0.410 | 0.683 | |

Product nature | 0.150 | 0.113 | 0.166 | 1.332 | 0.186 | |

Sustainability | −0.108 | 0.125 | −0.133 | −0.864 | 0.390 | |

Information sharing | −0.088 | 0.141 | −0.103 | −0.625 | 0.534 | |

Supply chain strategies | 0.075 | 0.140 | 0.091 | 0.536 | 0.593 | |

Logistics (Likely) | −0.017 | 0.098 | −0.023 | −0.175 | 0.862 | |

Order picking (likely) | −0.113 | 0.115 | −0.122 | −0.979 | 0.330 | |

Product nature (likely) | 0.077 | 0.102 | 0.102 | 0.753 | 0.453 | |

Sustainability (likely) | 0.021 | 0.109 | 0.026 | 0.193 | 0.847 | |

Information sharing (likely) | −0.042 | 0.132 | −0.047 | −0.318 | 0.751 | |

Supply chain (likely) | 0.016 | 0.120 | 0.020 | 0.135 | 0.893 |

^{a}means Coefficient

^{0.05%}.

Coefficients^{a} | ||||||
---|---|---|---|---|---|---|

Model | Unstandardized Coefficients | Standardized Coefficients | t | Sig. | ||

B | Std. Error | Beta | ||||

1 | (Constant) | 3.230 | 0.439 | 7.354 | 0.000 | |

Logistics | 0.019 | 0.105 | 0.024 | 0.186 | 0.853 | |

Order picking | −0.157 | 0.115 | −0.175 | −1.367 | 0.175 | |

Product nature | 0.105 | 0.112 | 0.116 | 0.930 | 0.355 | |

Sustainability | −0.063 | 0.126 | −0.075 | −0.499 | 0.619 | |

Information sharing | 0.076 | 0.141 | 0.085 | 0.539 | 0.591 | |

Supply chain strategies | 0.147 | 0.141 | 0.173 | 1.043 | 0.300 | |

Logistics (Likely) | 0.024 | 0.095 | 0.031 | 0.246 | 0.806 | |

Order picking (likely) | −0.064 | 0.116 | −0.068 | −0.548 | 0.585 | |

Product nature (likely) | 0.005 | 0.103 | 0.007 | 0.051 | 0.960 | |

Sustainability (likely) | 0.033 | 0.112 | 0.039 | 0.294 | 0.769 | |

Information sharing (likely) | −0.134 | 0.133 | −0.147 | −1.007 | 0.317 | |

Supply chain (likely) | 0.130 | 0.120 | 0.158 | 1.079 | 0.283 |

^{a}means Coefficient

^{0.05%}.

Summary Statistics | DTC[p] | DC[p] | R[p] | V[p] |
---|---|---|---|---|

Mean | 0.802 | 0.766 | 0.268 | 0.352 |

Std. dev. | 0.301 | 0.194 | 0.279 | 0.269 |

Min. | 0.000 | 0.000 | 0.000 | 0.000 |

Max. | 1.000 | 1.000 | 1.000 | 0.750 |

. | Response Variables | |||
---|---|---|---|---|

DTC[p] | DC[p] | R[p] | V[p] | |

Ind. variable:${d}_{\mathbb{F}}$ | ||||

Coefficient | −0.0915** | 0.0852** | 0.0001** | 0.0062** |

Std. error | 0.0134 | 0.0124 | 0.0000 | 0.0018 |

z-value | −68.16 | 68.34 | 17.13 | 33.50 |

Ind. variable:${d}_{\mathbb{V}}$ | ||||

Coefficient | 0.0697** | −0.0068 | 0.0001 | −0.0629** |

Std. error | 0.0066 | 0.0061 | 0 | 0.0015 |

z-value | 10.44 | −1.11 | −2.61 | −39.7 |

Ind. variable:${d}_{\mathbb{R}}$ | ||||

Coefficient | −0.009 | 0.0155** | −0.0024** | −0.0041 |

Std. error | 0.0062 | 0.0059 | 0.0001 | 0.0007 |

z-value | −1.43 | 2.60 | −18.39 | −5.38 |

Ind. variable:${f}_{\mathbb{F}}$ | ||||

Coefficient | −0.0731** | 0.0688** | 0.0001** | 0.0043** |

Std. error | 0.0009 | 0.0009 | 0.0000 | 0.0001 |

z-value | −73.59 | 74.72 | 17.29 | 30.96 |

Ind. variable:${\delta}_{\mathbb{F}}$ | ||||

Coefficient | −0.0569** | 0.0535** | 0.0000** | 0.0033** |

Std. error | 0.0856 | 0.0800 | 0.0004 | 0.0115 |

z-value | −66.53 | 66.92 | 16.31 | 29.10 |

Ind. variable:${\delta}_{\mathbb{V}}$ | ||||

Coefficient | 0.0603** | −0.0100** | −0.0001 | −0.0502** |

Std. error | 0.0365 | 0.0326 | 0.0001 | 0.0105 |

z-value | 16.52 | −3.08 | −0.76 | −47.52 |

Ind. variable:${\delta}_{\mathbb{R}}$ | ||||

Coefficient | −0.0459 | 0.0787** | −0.0123** | −0.0205** |

Std. error | 0.0314 | 0.0297 | 0.0006 | 0.0037 |

z-value | −1.46 | 2.64 | −18.17 | −5.45 |

Ind. variable:${k}_{\mathbb{R}}$ | ||||

Coefficient | 0.0162 | −0.0187 | 0.0015** | 0.0011 |

Std. error | 0.0302 | 0.0286 | 0.0001 | 0.0034 |

z-value | 0.53 | −0.65 | 14.86 | 0.31 |

Goodness-of-fitstatistics | Wald χ2 = 23,869 Log pseudolikelihood = −3,064.97 Prob > χ2 = 0.000 | |||

DTC Order Fulfillment Costs | DTC Warehousing Costs | ||||||
---|---|---|---|---|---|---|---|

${\mathit{d}}_{\mathbb{F}}$ | $\left(\frac{{\mathit{d}}_{\mathbb{F}}}{{\mathit{d}}_{\mathbb{D}}}\right)$ | $\mathbb{F}$ | $\mathbb{D}$ | $\mathbb{F}$ | $\mathbb{D}$ | $\mathbb{F}$ | $\mathbb{D}$ |

$ 5.25 | 0.75 | 100% | 100% | 75% | 25% | ||

$ 5.29 | 0.85 | 100% | 65% | 35% | 100% | ||

$ 6.65 | 0.95 | 100% | 100% | 100% |

## References

- U.S. Census Bureau. Annual Retail E-Commerce Sales Report. 2015. Available online: http://www.ecommercefuel.com/drop-shipping-companies/ (accessed on 1 January 2020).
- Khan, S.A.; Dong, Q.L.; Yu, Z. Role of ABC Analysis in the process of efficient order fulfillment: Case study. Adv. Eng. Forum.
**2017**, 23, 114–121. [Google Scholar] [CrossRef] - Verhoef, P.C.; Kannan, P.K.; Inman, J.J. From multi-channel retailing to omni-channel retailing: Introduction to the special issue on multi-channel retailing. J. Retail.
**2015**, 91, 174–181. [Google Scholar] [CrossRef] - Metters, R.; Walton, S. Strategic supply chain choices for multi-channel Internet retailers. Serv. Bus.
**2007**, 1, 317–331. [Google Scholar] [CrossRef] - Nicholls, A.; Watson, A. Implementing e-value strategies in UK retailing. Int. J. Retail Distrib. Manag.
**2005**, 33, 6. [Google Scholar] [CrossRef] - Wickham, B.F.; Minella, M. Method and system for order fulfillment. United States patent US 9,733,633, 15 August 2017. [Google Scholar]
- National Bureau of Statistics of the People’s Republic of China. Industrial Products. Available online: http://data.stats.gov.cn/search.htm?s=%202015%20%E5%B7%A5%E4%B8%9A%E4%BC%81%E4%B8%9A%E4%BA%A7%E6%88%90%E5%93%81 (accessed on 29 December 2015).
- National Bureau of Statistics of the People’s Republic of China. Finished Products in Rubber and Plastic Products. Available online: http://data.stats.gov.cn/search.htm?s=2012%20%E6%A9%A1%E8%83%B6%E5%92%8C%E5%A1%91%E6%96%99%E5%88%B6%E5%93%81%E4%B8%9A%E4%BA%A7%E6%88%90%E5%93%81 (accessed on 29 December 2015).
- Baggaley, B.; Maskell, B.H. Value stream management for lean companies, Part II. J. Cost Manag.
**2003**, 17, 24–30. [Google Scholar] - Croxton, K.L. The order fulfillment process. Int. J. Logist. Manag.
**2003**, 14, 19–32. [Google Scholar] [CrossRef][Green Version] - Lin, F.R.; Shaw, M.J. Reengineering the order fulfillment process in supply chain networks. Int. J. Flex. Manuf. Syst.
**1998**, 10, 197–229. [Google Scholar] [CrossRef] - Malhotra, Y. Business process redesign: An overview. IEEE Eng. Manag. Rev.
**1998**, 26, 27–31. [Google Scholar] - Croxton, K.L.; Garcia-Dastugue, S.J.; Lambert, D.M.; Rogers, D.S. The supply chain management processes. Int. J. Logist. Manag.
**2001**, 12, 13–36. [Google Scholar] [CrossRef] - Stock, J.R.; Lambert, D.M. Strategic Logistics Management; M cG raw-Hill: New York, NY, USA, 2001. [Google Scholar]
- Mitchell, A. Why retailers’ power has reached the tipping point. Mark. Week
**2004**, 27, 32–33. [Google Scholar] - Davis-Sramek, B.; Mentzer, J.T.; Stank, T.P. Creating consumer durable retailer customer loyalty through order fulfillment service operations. J. Oper. Manag.
**2008**, 26, 781–797. [Google Scholar] [CrossRef] - Davis-Sramek, B.; Germain, R.; Stank, T.P. The impact of order fulfillment service on retailer merchandising decisions in the consumer durables industry. J. Bus. Logist.
**2010**, 31, 215–230. [Google Scholar] [CrossRef] - Fuller, J.B.; O’Conor, J.; Rawlinson, R. Tailored logistics: The next advantage. Harv. Bus. Rev.
**1993**, 71, 87–98. [Google Scholar] [PubMed] - Lee, I. Evaluating business process-integrated information technology investment. Bus. Process Manag. J.
**2004**, 10, 214–233. [Google Scholar] [CrossRef] - Zhang, L.L.; Jiao, R.J.; Ma, Q. Accountability-based order fulfillment process reengineering towards supply chain management: A case study at a semiconductor equipment manufacturer. J. Manuf. Technol. Manag.
**2010**, 21, 287–305. [Google Scholar] [CrossRef] - Stephens, S. Supply chain operations reference model version 5.0: A new tool to improve supply chain efficiency and achieve best practice. Inf. Syst. Front.
**2001**, 3, 471–476. [Google Scholar] [CrossRef] - Petersen, C.G.; Aase, G. A comparison of picking, storage, and routing policies in manual order picking. Int. J. Prod. Econ.
**2004**, 92, 11–19. [Google Scholar] [CrossRef] - De Koster, R.; Le-Duc, T.; Roodbergen, K.J. Design and control of warehouse order picking: A literature review. Eur. J. Oper. Res.
**2007**, 182, 481–501. [Google Scholar] [CrossRef] - Bullinger, H.J.; Kühner, M.; Van Hoof, A. Analysing supply chain performance using a balanced measurement method. Int. J. Prod. Res.
**2002**, 40, 3533–3543. [Google Scholar] [CrossRef] - Lapide, L. True measures of supply chain performance. Supply Chain Manag. Rev.
**2000**, 4, 25–28. [Google Scholar] - Lieb, R.; Bentz, B.A. CEO Perspectives on the Current Status and Future Prospects of the Third-Party Logistics Industry in the United States: The year 2003 Survey. Supply Chain. Forum An Int. J.
**2004**, 5, 2–11. [Google Scholar] [CrossRef] - Cotteleer, M.J.; Bendoly, E. Order lead-time improvement following enterprise information technology implementation: An empirical study. MIS Q.
**2006**, 30, 643–660. [Google Scholar] [CrossRef][Green Version] - McAfee, A. The impact of enterprise information technology adoption on operational performance: An empirical investigation. Prod. Oper. Manag.
**2002**, 11, 33–53. [Google Scholar] [CrossRef] - Scheer, A.W.; Habermann, F. Enterprise resource planning: Making ERP a success. Commun. ACM
**2000**, 43, 57–61. [Google Scholar] [CrossRef] - Croom, S.; Fawcett, S.E.; Osterhaus, P.; Magnan, G.M.; Brau, J.C.; McCarter, M.W. Information sharing and supply chain performance: The role of connectivity and willingness. Supply Chain Manag. Int. J.
**2007**, 12, 5. [Google Scholar] - Sambamurthy, V.; Bharadwaj, A.; Grover, V. Shaping agility through digital options: Reconceptualizing the role of information technology in contemporary firms. MIS Q.
**2003**, 27, 237–263. [Google Scholar] [CrossRef][Green Version] - Shamsuzzaman, M.; Alzeraif, M.; Alsyouf, I.; Khoo, M.B. Using Lean Six Sigma to improve mobile order fulfilment process in a telecom service sector. Prod. Plan. Control.
**2018**, 29, 301–314. [Google Scholar] [CrossRef] - Strader, T.J.; Lin, F.R.; Shaw, M.J. Information infrastructure for electronic virtual organization management. Decis. Support Syst.
**1998**, 23, 75–94. [Google Scholar] [CrossRef] - Boar, B.H. Practical Steps for Aligning Information Technology with Business Strategies: How to Achieve a Competitive Advantage; John Wiley & Sons, Inc.: Hoboken, NJ, USA, 1994. [Google Scholar]
- Davenport, T.H. Process Innovation: Reengineering Work through Information Technology; Harvard Business Press: Cambridge, MA, USA, 1993. [Google Scholar]
- Karimi, J.; Somers, T.M.; Gupta, Y.P. Impact of information technology management practices on customer service. J. Manag. Inf. Syst.
**2001**, 17, 125–158. [Google Scholar] [CrossRef] - Putri, D.N.; Hisjam, M.; Sutopo, W.; Widodo, K.H. Simulation of supplier-manufacturer relationship model for securing availability of teak log in furniture industry with sustainability consideration. In Proceedings of the 2013 IEEE International Conference on Industrial Engineering and Engineering Management, Bangkok, Thailand, 10 December 2013; pp. 367–371. [Google Scholar]
- Troyer, L.; Smith, J.; Marshall, S.; Yaniv, E.; Tayur, S.; Barkman, M.; Kaya, A.; Liu, Y. Improving asset management and order fulfillment at Deere & Company’s C&CE division. Interfaces
**2005**, 35, 76–87. [Google Scholar] - Day, G.S. The capabilities of market-driven organizations. J. Mark.
**1994**, 58, 37–52. [Google Scholar] [CrossRef] - Johnsen, T.; Miemczyk, J.; Macquet, M. Sustainable purchasing and supply management: A structured literature review of definitions and measures at dyad, chain and network levels. Supply Chain Manag. Int. J.
**2012**, 17, 478–496. [Google Scholar] - Hajdul, M. Model of coordination of transport processes according to the concept of sustainable development. LogForum
**2010**, 3, 45–55. [Google Scholar] - Muralidhar, P.; Ravindranath, K.; Srihari, V. Application of fuzzy AHP for evaluation of green supply chain management strategies. IOSR J. Eng.
**2012**, 2, 461–467. [Google Scholar] [CrossRef] - Agatz, N.A.; Fleischmann, M.; Van Nunen, J.A. E-fulfillment and multi-channel distribution–A review. Eur. J. Oper. Res.
**2008**, 187, 339–356. [Google Scholar] [CrossRef][Green Version] - Pil, F.K.; Holweg, M. Linking product variety to order-fulfillment strategies. Interfaces
**2004**, 34, 394–403. [Google Scholar] [CrossRef] - Min, H. Blockchain technology for enhancing supply chain resilience. Bus. Horiz.
**2019**, 62, 35–45. [Google Scholar] [CrossRef] - Forslund, H.; Jonsson, P. Integrating the performance management process of on-time delivery with suppliers. Int. J. Logist. Res. Appl.
**2010**, 13, 225–241. [Google Scholar] [CrossRef] - Guisasola, L.; Tresserras, R.; Rius, A.; López-Dóriga, A.; Purtí, E. Vision problems causing and not causing visual impairment in a working population of Catalonia. Arch. Prev. Riesgos Laborales
**2013**, 16, 71–76. [Google Scholar] [CrossRef] - Lin, J.T.; Hong, I.H.; Wu, C.H.; Wang, K.S. A model for batch available-to-promise in order fulfillment processes for TFT-LCD production chains. Comput. Ind. Eng.
**2010**, 59, 720–729. [Google Scholar] [CrossRef] - Lečić-Cvetković, D.; Atanasov, N.; Babarogić, S. An algorithm for customer order fulfillment in a make-to-stock manufacturing system. Int. J. Comput. Commun. Control
**2010**, 5, 783–791. [Google Scholar] [CrossRef] - Mishra, P.; Sharma, R.K. Investigating the impact of perfect order fulfilment on quality level and SCM performance. Int. J. Model. Oper. Manag.
**2014**, 4, 95–115. [Google Scholar] [CrossRef] - Song, J.S.; Xu, S.H.; Liu, B. Order-fulfillment performance measures in an assemble-to-order system with stochastic leadtimes. Oper. Res.
**1999**, 47, 131–149. [Google Scholar] [CrossRef][Green Version] - Hult, G.T.; Ketchen, D.J., Jr.; Nichols, E.L., Jr. An examination of cultural competitiveness and order fulfillment cycle time within supply chains. Acad. Manag. J.
**2002**, 45, 577–586. [Google Scholar] - Philipp, M. Qualitative content analysis. InForum Qual. Soc. Res.
**2000**, 1, 10. [Google Scholar] - Neuman, S.B.; Roskos, K. Literacy knowledge in practice: Contexts of participation for young writers and readers. Read. Res. Q.
**1997**, 32, 10–32. [Google Scholar] [CrossRef] - Mena, C.; Bourlakis, M.; Ishfaq, R.; Defee, C.C.; Gibson, B.J.; Raja, U. Realignment of the physical distribution process in omni-channel fulfillment. Int. J. Phys. Distrib. Logist. Manag.
**2016**, 46, 543–561. [Google Scholar] - Lummus, R.R.; Vokurka, R.J. Making the right e-fulfillment decision. Prod. Inventory Manag. J.
**2002**, 43, 50. [Google Scholar] - Ayanso, A.; Diaby, M.; Nair, S.K. Inventory rationing via drop-shipping in Internet retailing: A sensitivity analysis. Eur. J. Oper. Res.
**2006**, 171, 135–152. [Google Scholar] [CrossRef] - Croxton, K.L.; Zinn, W. Inventory considerations in network design. J. Bus. Logist.
**2005**, 26, 149–168. [Google Scholar] [CrossRef] - Gibson, B.; Defee, C.; Ishfaq, R. Fifth Annual State of Retail Supply Chain Report; Retail Industry Leaders Association: Arlington County, VA, USA, 2015. [Google Scholar]
- Europe, P.; An analysis of european plastics production demand and waste data. Plastics Facts 2015. Available online: https://www.plasticseurope.org/application/files/3715/1689/8308/2015plastics_the_facts_14122015.pdf (accessed on 3 March 2020).
- Ramalho, E.A.; Joaquim, J.S.; Ramalho, J.; Murteira, M.R. Alternative estimating and testing empirical strategies for fractional regression models. J. Econ. Surv.
**2011**, 25, 19–68. [Google Scholar] [CrossRef] - Papke, L.E.; Wooldridge, J.M. Econometric methods for fractional response variables with an application to 401 (k) plan participation rates. J. Appl. Econ.
**1996**, 11, 619–632. [Google Scholar] [CrossRef][Green Version] - Sudusinghe, J.I.; Seuring, S. Social Sustainability Empowering the Economic Sustainability in the Global Apparel Supply Chain. Sustainability
**2020**, 12, 2595. [Google Scholar] [CrossRef][Green Version] - Li, X.; Zhu, Q. Contract Design for Enhancing Green Food Material Production Effort with Asymmetric Supply Cost Information. Sustainability
**2020**, 12, 2119. [Google Scholar] [CrossRef][Green Version] - Mullahy, J. Multivariate fractional regression estimation of econometric share models. J. Econ. Methods
**2015**, 4, 71–100. [Google Scholar] [CrossRef] - Ishfaq, R.; Bajwa, N. Profitability of online order fulfillment in multi-channel retailing. Eur. J. Oper. Res.
**2019**, 272, 1028–1040. [Google Scholar] [CrossRef] - Kautish, P.; Sharma, R. Managing online product assortment and order fulfillment for superior e-tailing service experience. Asia Pac. J. Mark. Logist.
**2019**, 31, 4. [Google Scholar] [CrossRef] - Johnson, R.; Johnson, M.C.; Johnson, S.; Welty, B. Order Grouping in Warehouse Order Fulfillment Operations. U.S. patent 10,572,854, 25 February 2020. [Google Scholar]

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Heydari, M.; Lai, K.K.; Zhou, X. Creating Sustainable Order Fulfillment Processes through Managing the Risk: Evidence from the Disposable Products Industry. *Sustainability* **2020**, *12*, 2871.
https://doi.org/10.3390/su12072871

**AMA Style**

Heydari M, Lai KK, Zhou X. Creating Sustainable Order Fulfillment Processes through Managing the Risk: Evidence from the Disposable Products Industry. *Sustainability*. 2020; 12(7):2871.
https://doi.org/10.3390/su12072871

**Chicago/Turabian Style**

Heydari, Mohammad, Kin Keung Lai, and Xiaohu Zhou. 2020. "Creating Sustainable Order Fulfillment Processes through Managing the Risk: Evidence from the Disposable Products Industry" *Sustainability* 12, no. 7: 2871.
https://doi.org/10.3390/su12072871