Order Fulfillment Process Research Articles

Role of SAP Order Management in Managing Backorders in High-Tech Industries

Effective order management is critical in the high-tech industry, where rapid innovation, fluctuating demand, and global supply chain complexities create unique challenges. SAP Order Management emerges as a robust solution for addressing these challenges, particularly in managing backorders. Backorders, a common issue in high-tech industries, can significantly impact customer satisfaction, operational efficiency, and profitability. This paper explores the role of SAP Order Management in mitigating backorder risks and streamlining the fulfillment process. SAP’s comprehensive tools offer real-time visibility into inventory, orders, and demand forecasts, enabling companies to prioritize high-value orders, reallocate resources effectively, and minimize delays. Its integration with advanced analytics allows businesses to identify root causes of backorders, such as supply chain disruptions or inaccurate demand planning. Furthermore, SAP’s automation capabilities facilitate efficient workflows by automating order prioritization and fulfillment processes, reducing manual errors and operational bottlenecks. In high-tech industries, where product lifecycles are short and customer expectations are high, the ability to manage backorders effectively is vital. SAP Order Management supports seamless collaboration across departments, ensuring that sales, supply chain, and production teams work cohesively to meet demand. This study highlights case examples where SAP Order Management has improved order accuracy, reduced backorder rates, and enhanced customer experiences in high-tech firms. Ultimately, SAP Order Management proves to be a transformative tool for high-tech companies, enabling them to address backorder challenges proactively and maintain a competitive edge in a fast-paced market.

USAGE OF MACHINE LEARNING IN INVENTORY AND WAREHOUSE MANAGEMENT SYSTEMS

The management of inventory and warehouse is a critical component of supply chain operations. This article explores the impact of machine learning on inventory and warehouse management systems. This research is important because machine learning is gaining increasing recognition for its potential to increase operational efficiency and accuracy across industries. The purpose of this research is to examine the hypothesized potential of implementing machine learning to solve challenging problems in inventory management systems in an attempt to improve their efficiency. In addition, the study aims to evaluate current applications of these capabilities to provide a basis for future research. The research is grounded in a comprehensive review of existing literature on the use of machine learning in inventory and warehouse management. To explore practical implementations, the study also analyses how leading companies have integrated machine learning into their products. The result of this study is a review of existing research and practical applications of machine learning in inventory and warehouse management. This paper provides a detailed analysis of how machine learning can be used to improve demand forecasting, inventory levels, warehouse planning, and order fulfillment processes. This material serves as a basis for further study and research in this area. The scientific novelty of this research is the study of machine learning methods applied to inventory and warehouse management. The study expands the understanding of how machine learning can be used to solve problems in these areas. The practical implications of the research provide a basis for the implementation of mentioned technologies in enterprises. Firstly, the study highlights the potential for significant cost savings, efficiency gains, and increased customer satisfaction. Secondly, it offers valuable material for further research in supply chain management.

Selection of Artificial Intelligence Technology For Digitalization of Order Fulfillment on Drug Factory Using Multi Criteria Decision Making

Objective: This paper aims to identify the most suitable Artificial Intelligence (AI) technology for the order fulfillment process in pharmaceutical factory, along with the key criteria and sub-criteria for successful AI implementation. Theoretical Framework: The study integrates theories of digital transformation, process optimization, and decision-making. Method: The research assesses digital readiness to evaluate a company's preparedness for digital transformation. The as-is process is mapped using BPMN to identify potential AI applications. AI technologies are then ranked using BWM and PROMETHEE methods, followed by scenario analysis to determine the optimal technology. Results and Discussion: Demand Forecasting is the top AI technology for adoption in the pharmaceutical factory's order fulfillment process, reducing cycle time by 29.50% to 21.80 hours. Research Implications: The research contributes to the understanding of selecting the most effective AI technology for optimizing the order fulfillment process in pharmaceutical companies, providing valuable insights for improving efficiency. Originality/Value: The study proposes a perspective on applying AI to optimize the order fulfillment process in pharmaceutical factory, offering valuable insights for improving operational efficiency through digital transformation.

The impact of order fulfillment on consumer experience: text mining consumer reviews from Amazon US

PurposeThis research analyzes online consumer reviews and ratings to assess e-retail order fulfillment performance. The study aims to (1) identify consumer journey touchpoints in the order fulfillment process and (2) determine their relative importance for the consumer experience.Design/methodology/approachText mining and analytics were employed to examine over 100 m online purchase orders, along with associated consumer reviews and ratings from Amazon US. Using natural language processing techniques, the corpus of reviews was structured to pinpoint touchpoints related to order fulfillment. Reviews were then classified according to their stance (either positive or negative) toward these touchpoints. Finally, the classes were correlated with consumer rating, measured by the number of stars, to determine the relative importance of each touchpoint.FindingsThe study reveals 12 touchpoints within the order fulfillment process, which are split into three groups: delivery, packaging and returns. These touchpoints significantly influence star ratings: positive experiences elevate them, while negative ones reduce them. The findings provide a quantifiable measure of these effects, articulated in terms of star ratings, which directly reflect the influence of experiences on consumer evaluations.Research limitations/implicationsThe dataset utilized in this study is from the US market, which limits the generalizability of the findings to other markets. Moreover, the novel methodology used to map and quantify customer journey touchpoints requires further refinement.Practical implicationsIn e-retail and logistics, comprehending touchpoints in the order fulfillment process is pivotal. This understanding helps improve consumer interactions and enhance satisfaction. Such insights not only drive higher conversion rates but also guide informed managerial decisions, particularly in service development.Originality/valueDrawing upon consumer-generated data, this research identifies a cohesive set of touchpoints within the order fulfillment process and quantitatively evaluates their influence on consumer experience using star ratings as a metric.

Picker routing in scattered storage warehouses: an evaluation of solution methods based on TSP transformations

AbstractTo optimize their order fulfillment processes, many e-commerce warehouses employ a storage assignment strategy known as scattered or mixed-shelves storage. Under this approach, unit loads of homogeneous products are divided, and individual pieces are stored in various shelves throughout the warehouse. This arrangement ensures that products that appear together on unpredictable pick lists are stored in close proximity somewhere in the huge warehouses, reducing the travel distance for pickers. Despite these advancements, efficiently guiding pickers through the warehouse remains a significant planning challenge. Since the same products can be found in multiple storage positions, the traditional picker routing problem becomes more complex, as an additional selection task arises regarding which shelf to retrieve each requested product from. While previous research has developed several tailor-made solution algorithms, we demonstrate that known transformation schemes used for different variants of the well-known Traveling Salesman Problem (TSP) can be utilized to convert the single picker routing problem with scattered storage (SPRP-SS) into a classical TSP. This approach enables us to leverage the extensive array of state-of-the-art TSP solvers. The purpose of this paper is to explore the performance of these solvers when applied to solving the SPRP-SS. Through our computational study, we found that existing TSP solvers exhibit good performance, allowing near-optimal solutions to be obtained in less than a second for real-world scale SPRP-SS instances. Moreover, the efficiency of these TSP solvers remains unaffected by the number of cross aisles in the warehouse. Consequently, we exploit this flexibility to investigate the impact of cross aisles on picking performance in scattered storage warehouses.

Effect of Lean Six Sigma on order fulfilment process: evidence from manufacturing companies in Gauteng, South Africa

Effect of Lean Six Sigma on order fulfilment process: evidence from manufacturing companies in Gauteng, South Africa

Developing an Efficient Model for Online Grocery Order Fulfillment

Due to the convenience of online grocery apps and home delivery, online grocery shopping has become popular in recent years. Globally, consumer behavior has significantly changed the consumption and purchase patterns of online grocery shopping. This study aimed to develop an efficient model for online grocery order fulfillment that both reduces costs and increases supply chain efficiency and sustainability. This study first aimed to develop the current picking model by adopting real-world data from a store in Riyadh, Saudi Arabia. Subsequently, four proposed models were developed to improve the efficiency and sustainability of the online grocery order fulfillment process. The results show a significant improvement in all models over the current picking model. The percentage improvements in fulfillment time per product are as follows: single order picking—8.33%; batch order picking—6.78%; zone order picking—3.08%; and hybrid order picking—13.20%, which combines zone and batch order picking. Retailers and online grocery apps could adopt these models to increase efficiency and sustainability. Also, these models have great potential for future research and improvement by optimizing product placement, in addition to picking methods and picking routes, which are the focus of this study.

Stochastic modeling of integrated order fulfillment processes with delivery time promise: Order picking, batching, and last-mile delivery

To guarantee high customer service and short and accurate lead times, many e-commerce retailers have started to home deliver their customer orders within a few hours or even minutes, also known as quick-commerce order fulfillment. Quick-commerce order fulfillment consists of three main processes: order picking in the warehouse, order batching for delivery, and last-mile delivery. The ultimate delivery performance depends on managing all three processes, which are highly stochastic, and interdependent. We capture this stochasticity and interdependency in an integrated analytical framework and derive approximate analytical expressions for the mean and variance of the total order fulfillment time. We validate the analytical expressions with both in-house detailed process simulations and external-party output measures. We then analyze the delivery cost-service quality trade-offs using an optimization model that minimizes the expected order fulfillment cost with a delivery probability (DP) constraint, focusing on meeting delivery time deadlines. The optimization model determines the number of pickers, the optimal delivery batch size, and the number of vehicles required to deliver the customer orders. Achieving a high delivery reliability comes at a cost. In comparison to the model with DP constraints, we observe that the expected order fulfillment cost averaged over all data parameter settings obtained from the model without DP constraints is 8.9% lower; however, the mean and standard deviation of order fulfillment time increase by 44.1% and 18.6%, respectively, which results in low delivery reliability. We further demonstrate that an integrated analysis of the order fulfillment process is essential to set reliable fulfillment due times.

Integrated inventory replenishment and online demand allocation decisions for an omnichannel retailer with ship-from-store strategy

Retailing has changed dramatically from single-channel brick-and-mortar stores to multi-channel and omnichannel retailers over the last few decades. Omnichannel retailers employ different strategies to integrate online and offline sales channels as well as order fulfillment processes. Among these strategies, the ship-from-store is the most popular and widely accepted among retailers. It enables retailers to use inventory from store locations to fulfill online demand. An omnichannel retailer with a distribution center and a retail store has to make important, interlinked decisions — (1) how much inventory to keep at the retail store, and (2) where to fulfill the online demand from and how much. In this work, we model the integrated inventory replenishment and online demand allocation decisions for an omnichannel retailer employing the ship-from-store strategy. We analyze this problem for both single-period and multi-period settings. We extend the analytical framework of the single-period problem by providing a finite-horizon Markov decision process (MDP) formulation for the multi-period problem. Our findings suggest that for a single-period setting, decentralized inventory replenishment and demand allocation system maximizes the profit of the omnichannel retailer for low values of the incentive for fulfilling the online demand through store inventory, while for sufficiently high values of the incentive, a pooled system provides the optimal profit. An increment in the discount factor has the same effect on the optimal decisions in a multi-period setting as that of salvage value in a single-period setting for a given value of the incentive for the ship-from-store strategy. We also provide several extensions (such as cross selling, endogenous and correlated demand streams) of our analytical framework for the multi-period problem.

Analysis of the Application of Inventory Accounting Information Systems

The development of the industry that is currently increasing rapidly makes the level of competition even tighter. Companies need to pay attention to several things in the face of competition, one thing that really needs to be considered is inventory for the smooth running of the entire order fulfillment process on CV. Prosperous Business Development. This research was conducted to find out whether the implementation of the inventory accounting information system at the company has been running in accordance with production operational standards or still needs improvement. The results showed that the Accounting Information System at CV. Bina Usaha Sejahtera is still not running optimally because existing production operational standards are not properly implemented by employees.

Managing cutoff-based shipment promises for order fulfilment processes in warehousing

Warehouses recently face increasing stress imposed by a volatile customer demand and increasing customer expectations in terms of ever shorter order response times. In that respect, warehouses more and more offer same-day and next-day shipment conditions. However, same-day shipment promises are challenging to fulfil, especially as the order fulfilment process operates against fixed deadlines imposed by the predefined truck departure times. As a natural mitigation strategy, warehouses set a cutoff point and offer same-day shipment only to customers that order until the cutoff point, but next-day shipment to all customers ordering thereafter. Setting an appropriate cutoff point is challenging as it affects multiple facets of the service quality, such as the order response time and the service level. In this paper, we study the design of cutoff-based shipment promises for stochastic deadline-oriented order fulfilment processes in warehouses. We present a discrete-time Markov chain model for exact steady-state performance analysis and propose two novel performance measures – α-\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\alpha-$$\\end{document} and β-\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\beta-$$\\end{document}cutoff service level – for service level measurement in these systems. We numerically show the benefit of cutoff-based shipment promises. Even with a late cutoff point, there is a significant gain in the system performance. Furthermore, we find that warehouses should set the cutoff point such that it balances customer expectations in terms of service level and order response time. Finally, warehouses can improve their shipment promises when referring to β-\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\beta-$$\\end{document} instead of α-\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\alpha-$$\\end{document}cutoff service level and by implementing measures reducing the utilisation and the variabilities of the order fulfilment process.

Throughput Time Predictions Along the Order Fulfilment Process

Throughput Time Predictions Along the Order Fulfilment Process

Data-Driven Order Fulfillment Consolidation for Online Grocery Retailing

Improving fulfillment efficiency is critical for long-term sustainability of online grocery retailing. In this paper, we study reducing order fulfillment cost by order consolidation. Motivated by the observation that a significant percentage of buyers place multiple orders within a short time interval, we propose a scheme that attempts to consolidate such “multiorders” to reduce the number of parcels and hence, the shipping cost. At the same time, it cannot significantly disturb the existing order fulfillment process or undermine the customer service level. Successful execution of the scheme requires a prediction of multiorder probabilities and a control policy that selectively prioritizes order processing. For the prediction task, we formulate a binary classification problem and use machine-learning algorithms to predict in real time the probability of a multiorder. For the control task, our proposal is to hold arriving orders in a temporary order pool for potential consolidation and to determine the release timing by a dynamic program. The proposed solution is estimated to capture 92.8% of all the multiorders at the cost of holding the orders for about 20.3 minutes on average. This translates to more than 10 million U.S. dollars of order fulfillment cost saving annually. History: This paper was refereed.

Analisis Penerapan Sistem Informasi Akuntansi Persediaan Studi Kasus Pada CV. Bina Usaha Sejahtera Malang

The rapid development of the industry has made the level of competition even tighter. Companies need to pay attention to several things in the face of competition, one thing that really needs to be considered in the order fulfillment process is the need for an effective and efficient inventory system. The purpose of this research is to find out the implementation of inventory accounting information systems in CV. Prosperous Business Development. The method used by researchers for this research is descriptive qualitative method with primary data and secondary data types. The research results obtained are the Accounting Information System contained in the CV. Bina Usaha Sejahtera is still not optimal, one of the contributing factors is the double duty of the warehouse department.

The double-edged sword of delivery guarantee in E-commerce

E-commerce retailers (e-tailers) are increasingly competing in order fulfillment to win over customers. Delivery guarantee emerges as a key competitive factor. Prior studies have examined the relationship between delivery performance and consumer satisfaction, but little attention was paid to the practice of delivery guarantee itself. This study examines delivery guarantee as a decision support tool provided by e-tailers. Particularly, we draw on the signaling theory, expectation disconfirmation theory, and attribution theory to investigate whether it is worth to offer delivery guarantee, who (what kind of e-tailers) can benefit more from it, and how e-tailers should manage the order fulfillment process when they offer delivery guarantee. Through analyzing data from Alibaba using the Heckman Ordered Probit Model, we show that delivery guarantee can be a double-edged sword. That is, offering and meeting a delivery guarantee leads to higher consumer satisfaction towards order fulfillment than not offering one, but failing to meet the guarantee lowers consumer satisfaction more than not providing it. We further show that large e-tailers are more negatively affected by failed guarantees than small ones, and that the “middle mile” (delivery en route) of order fulfillment significantly reduces consumer satisfaction if failed. Taken together, this study generates implications for e-tailers that consider providing delivery guarantee. Large e-tailers should be especially cautious and assess their capability and commitment. The middle mile should receive enough managerial attention in addition to the widely concerned last mile.

Human-Robot Cooperation: Coordinating Autonomous Mobile Robots and Human Order Pickers

In the e-commerce era, efficient order fulfillment processes in distribution centers have become a key success factor. One novel technology to streamline these processes is robot-assisted order picking. In these systems, human order pickers are supported by autonomous mobile robots (AMRs), which carry bins for collecting picking orders, autonomously move through the warehouse, and wait in front of a shelf containing a requested stock keeping unit (SKU). Once a picker has approached a waiting AMR and placed the requested SKU into the respective bin, AMR and picker may separate and move toward other picking positions. In this way, pickers continuously move between different waiting AMRs without having to return to the depot. This paper treats the coordination of multiple AMRs and multiple pickers to minimize the makespan. We present a heuristic method for the deterministic case that can handle the requirements of large e-commerce fulfillment centers and successfully solves instances with more than one thousand picking positions. Based on the obtained solutions, the performance of our picking system is compared with the traditional warehouse setup without AMR support and to another work policy using fixed pairings of picker and AMR per order. We find that largely improved makespans can be expected. In addition, we analyze the effects of stochastic picking times, speed differences between AMRs and pickers, and a zoning strategy. The ripple effect caused by stochastic picking times, in which a single delay may cascade through a tightly synchronized schedule and deteriorate picking performance, can be effectively mitigated by separating the workforce into smaller subgroups. Another important finding is that pickers and AMR should have approximately the same travel speed because slower AMRs deteriorate system performance. Finally, zoning slightly decreases the flexibility of the system and should be used if dictated by organizational reasons. History: This paper has been accepted for the Transportation Science Special Issue on Emerging Topics in Transportation Science and Logistics. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.1207 .

Predicting Weighing Deviations in the Dispatch Workflow Process: A Case Study in a Cement Industry

The emergence of the Industry 4.0 concept and the profound digital transformation of the industry plays a crucial role in improving organisations’ supply chain (SC) performance, consequently achieving a competitive advantage. The order fulfilment process (OFP) consists of one of the key business processes for the organization SC and represents a core process for the operational logistics flow. The dispatch workflow process consists of an integral part of the OFP and is also a crucial process in the SC of cement industry organizations. In this work, we focus on enhancing the order fulfilment process by improving the dispatch workflow process, specifically with respect to the cement loading process. Thus, we proposed a machine learning (ML) approach to predict weighing deviations in the cement loading process. We adopted a realistic and robust rolling window scheme to evaluate six classification models in a real-world case study, from which the random forest (RF) model provides the best predictive performance. We also extracted explainable knowledge from the RF classifier by using the Shapley additive explanations (SHAP) method, demonstrating the influence of each input data attribute used in the prediction process.

Value stream mapping in shared service centres to enhance efficiency of order fulfilment process

Value stream mapping in shared service centres to enhance efficiency of order fulfilment process

Go Wide or Go Deep? Assortment Strategy and Order Fulfillment in Online Retail

Problem definition: Expansions in product assortment by online retailers often engender operational challenges. In undertaking such expansions, retailers exercise a strategic choice between expanding assortment width or depth. Our understanding of how this choice affects the order fulfillment process is limited. Thus, we examine the impact of these dimensions of assortment strategy on order delivery timeliness. Academic/practical relevance: Order delivery timeliness is a critical measure of operational success in online retail. We contribute to theory and practice by adopting a multidimensional perspective of retailer assortment strategy and studying the relative impact of assortment width and depth on order delivery timeliness. Methodology: Employing a data set comprising more than 200 million orders, we study the effects of assortment strategy on delivery timeliness using an instrumental variable approach. We then utilize a two-stage model to estimate the impact of delivery performance on sales. Further, we employ a matched difference-in-differences and a novel Bayesian structural time-series model to confirm this relationship. Results: We find that assortment width has a greater negative impact on order delivery timeliness compared with assortment depth. A one-standard-deviation increase in assortment width increases average delivery times by 0.55 days. Further, we find this effect to be positively moderated (i.e., worsened) by the average size of orders and to be negatively moderated (i.e., improved) by the logistic service provider’s (LSP) experience. Finally, a one-day increase in delivery times for 10% of the orders results in a 2.7% reduction in sales. Managerial implications: Our findings suggest that online retailers focused on ensuring timely deliveries should be wary of widening product assortments, especially when facing larger average order sizes. We also find that experienced logistic service providers can help mitigate the dilatory effects of assortment width expansions. However, the benefits of experienced LSPs are limited for retailers deepening their assortments.History: This paper has been accepted as part of the 2018 MSOM Data Driven Research Challenge.Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.1156 .

E-grocery logistics: exploring the gap between research and practice

PurposeThis paper investigates the logistics management in the e-grocery sector. It contrasts the key issues faced by practitioners and the topics addressed in the academic literature, to identify potential misalignments between research and practice and propose avenues for future efforts.Design/methodology/approachThis work adopts a twofold methodological approach. From an academic perspective, a systematic literature review (SLR) is performed to define the topics addressed so far by scholars when analysing e-grocery logistics. From a managerial perspective, a Delphi study is accomplished to identify the most significant issues faced by logistics practitioners in the e-grocery context and the associated significance.FindingsThe study develops a conceptual framework, identifying and mapping the 9 main logistics challenges for e-grocery along 4 clusters, in the light of a logistics-related revision of the SCOR model: distribution network design (area to be served, infrastructures), order fulfilment process (picking, order storage, consolidation, delivery), logistics-related choices from other domains (product range, stock-out management) and automation. These elements are discussed along three dimensions: criticalities, basic and advanced/automation-based solutions. Finally, the main gaps are identified – in terms of both under-investigated topics (order storage and stock-out management) and investigated topics needing further research (picking and automation) – and research questions and hypotheses are outlined.Originality/valueThis paper provides a threefold contribution, revolving around the developed framework. First, it investigates the state of the art about e-grocery logistics, classifying the addressed themes. Second, it explores the main issues e-grocery introduces for logistics practitioners. Third, it contrasts the two outcomes, identifying the misalignment between research and practice, and accordingly, proposing research directions.