Customer Order Processing Research Articles

Study of the use of robotic process automation in supporting customer order process

The article presents a study of the impact of implementing robotic process automation in the customer service process for a manufacturing industry company. The study showed that the implementation of robotic process automation can contribute to shortening the implementation time of the analyzed process and eliminating ineffective activities. The presented conclusions suggest that the implementation of such solutions may eliminate limitations in repeatable company processes.

A decision support model for handling customer orders in business chain

One of the elements of the modern trade and services market is a business chain solution (chain store/retail chain). An example of a business chain is, e.g., a restaurant chain, where each restaurant in the chain has the same decor, organization, menu, and delivery method. Although such solutions have been known for decades, the rapid development of IT technology, the widespread access to the Internet as well as the development of mobile technologies have changed and modernized their formula. Many customers place orders remotely with the option of delivery to their door. This method of ordering and fulfilling orders is becoming more and more popular and ever more common in the recent period due to the pandemic and the resulting restrictions and limitations on the functioning of trade and services. The following key questions arise in relation to customer order processing for chain business managers: How to allocate individual customer orders to selected branches so that the cost of their processing (production and delivery) is the lowest?, How to deliver on time?, etc. To answer these questions, a decision support model has been developed, which combines routing, allocation and planning problems for restaurant/store chains. Two ways to implement the model have been proposed. The first one uses the methods of mathematical modeling and programming, and the other, which is a proprietary approach that integrates the mechanisms of evolution (specialized representations, repair mechanisms, genetic operators, etc.), uses constraint logic programming and dedicated heuristics. In addition, procedures for constraint handling and presolving have been developed.

Pricing during Disruptions: Order Variability versus Profit

ABSTRACTWhen supply disruptions occur, firms want to employ an effective pricing strategy to reduce losses. However, firms typically do not know precisely how customers will react to price changes in the short term, during a disruption. In this article, we investigate the customer's order variability and the firm's profit under several representative heuristic pricing strategies, including no change at all (fixed pricing strategy), changing the price only (naive pricing strategy), and adjusting the belief and price simultaneously (one‐period correction [1PC] and regression pricing strategies). We show that the fixed pricing strategy creates the most stable customer order process, but it brings lower profit than the naive pricing strategy in most cases. The 1PC pricing strategy produces a more volatile customer order process and smaller profit than the naive one does. Although the regression pricing strategy is a more advanced approach, it leads to lower profit and greater customer order variability than the naive pricing strategy (but the opposite when compared to the 1PC strategy). We conclude that (i) completely eliminating the customer order variability by employing a fixed pricing strategy is not advisable and adjusting the price to match supply with demand is necessary to improve the profit; (ii) frequently adjusting the belief about customer behaviors under imperfect information may increase the customer's order variability and reduce the firm's profit. The conclusions are robust to the inventory assumption (i.e., without or with inventory carryover) and the firm's objective (i.e., market clearance or profit maximization).

Online Expedition Services System for Customer at XYZ Ltd.

XYZ Ltd. is an expedition company that uses container ships and container trucks as transportation modes. There are three business process transactions that are carried out by the company, i.e. customer registration, ordering expedition services, and payment expedition services. Currently, all business process transactions are carried out manually, so process automation is needed in order for the business processes to run more effectively and efficiently. For this purpose, an Online Expedition Services Customer Service Information System was proposed to be built. This system consists of three main processes, namely the customer registration process, the customer order process, and the payment process and report generation. XYZ Ltd. Customer Service Information System was built following the waterfall development model. The stages of system development consist of five stages, namely planning, requirements analysis, database, and interface design, implementation of program code writing and testing and system maintenance. In implementing and testing the system, whole system requirements have been checked; the three main business processes in the proposed online system were running precisely, effectively and efficiently.

Analytical approximations to predict order picking times at a warehouse

The primary objective of this research was to develop analytical approximations to predict the time taken to complete a customer order in a warehouse. The problem under study was modelled as a network of queues and the parametric decomposition approach was adopted to develop the approximations. Under this method, the queuing network is decomposed into individual queues allowing to analyse single (i.e., customer order processing, pallet building, and re-pick process) and fork-join stations (i.e., order picking with synchronisation constraints) within the same framework. Analytical formulations to estimate order picking times were developed and compared with equivalent simulation models to estimate the accuracy of the formulations. Appropriate correction terms were also developed to bridge the gap between the analytical approximations and the simulation models. The experimental study conducted indicates that the analytical approximations along with the correction terms can serve as a good estimate for the order picking times in a warehouse.

RFID Implementation of Supply Chain: Comparison of Three Case Studies

Abstract: This paper integrates the impact of different approaches in implementing Radio Frequency Identification (RFID) technology, namely supply chain inventory management system based on RFIDs to address the challenges encountered in the operation of the warehouses, item-level inventory tracking at retail stores, and customer order processing and distribution using RFIDs; to eventually propose RFID based supply chain management. Supply chain management (SCM) involves an effective information sharing, inventory management, transportation, sourcing and pricing strategies. This paper highlights upon how RFID can improve SCM drivers, one by one, and together that improve overall synergy. This paper extends the solutions of case studies and proposed improvements to RFID solutions in ERP applications. The paper also highlights the motivation to such implementations as these solutions offer further cost savings and performance gains. Consequently we conclude that RFID is a vital technology for the success of SCM and bridging the gaps among the existing enterprise applications.

Integrated Vehicle Configuration System—Connecting the domains of mass customization

Configuration design for mass customized vehicles necessitates the coordination of customer requirements, product characteristics, production processes, and logistics networks, in order to achieve rapid response to customer orders. Existing product configurators are mainly used as sales tools, and fail to account for the requirements of the entire customer order fulfillment process. In this regard, this paper proposes an Integrated Vehicle Configuration System (IVCS) to facilitate customer order processing based on information from multiple domains in a mass customization environment. An IVCS business model is proposed to incorporate the decision factors for configuration design related to different planning stages. The business model is supported by a comprehensive ontology framework, which enhances communications between different stakeholders involved in the order fulfillment process. The configuration approach is based on combinations of selective and generative rules and can be integrated with existing ERP systems. It also provides mechanisms to handle configuration rules that allow users to convert soft preferences into product specifications, bill-of-materials, and, furthermore, into logistics configurations. An example of a computerized configuration system showcases the process from customer engineering to design and production.

Modelling process platforms based on an object-oriented visual diagrammatic modelling language

Process platforms have been recognised as a promising means of dealing with product variety while achieving a near mass production efficiency. To assist practitioners to better understand, implement and use process platforms, this study addresses the underlying logic for coping with the challenges in high variety production by adopting process platforms. Accordingly, this paper proposes to model process platforms with focus on the application processes. In view of the significance of dynamic modelling and visualisation in shedding light on the logic of any processes, this study introduces a visual diagrammatic modelling language based on object-oriented (OO) techniques, named as OOVDML. With the graphical notations, uniquely shaped symbols, syntax and semantics, control mechanisms and arrangement rules, the OOVDML not only captures the logic of process platform's application but also provides a visualisation of their behaviours in a holistic view. Moreover, incorporating OO modelling allows readers to focus on their own interests. This study approaches to modelling process platform's application with respect to activities pertaining to customer order processing, engineering change control and production job planning. An industrial example of electronics products is also reported. The results of the case study not only show the suitability of the OOVDML but also shed light on the dynamic behaviours of process platforms.

Pricing during Disruptions: A Cause of the Reverse Bullwhip Effect

When supply disruptions occur, firms want to employ an effective pricing strategy to reduce losses. However, firms typically don't know precisely how customers will react to price changes in the short term, during a disruption. In this paper, we investigate three different pricing strategies, which we call naive, one-period correction (1PC), and regression, each of which makes progressively more sophisticated assumptions about customer behavior. We prove that strategies that appear to be more sophisticated may in fact lead to reduced profits and greater volatility. In particular, the 1PC pricing strategy produces a more volatile demand process and smaller revenue than the naive one does. Moreover, when customer behavior is sufficiently strategic, the customer order process under the 1PC pricing strategy is more volatile than the capacity process, a phenomenon known as the reverse bullwhip effect (RBWE). As supply disruptions become longer or more severe, the magnitude of the variability difference between the customer's orders and the capacity increases under the 1PC strategy but decreases under the naive one, while the firm's revenue decreases under both strategies. Furthermore, although the regression pricing strategy is a more advanced approach, it leads to smaller profit and greater customer's order variability than the naive pricing strategy (but the opposite when compared to the 1PC strategy). We conclude that the naive pricing strategy is superior to either of the more sophisticated ones, both in terms of the firm's profit and the magnitude of the customer's order variability in the supply chain as a whole.

Performers and performance

PurposeThis paper seeks to explore the question of how to investigate the contribution of the operational network (comprising sales service, logistics, planning, production, etc.) to operational performance. In doing so, the paper aims to link concepts from organisational and social psychology to production planning and control.Design/methodology/approachThe paper introduces the concept “operational network”, e.g. the network of people involved in customer and production order processing. Members of the operational network need some autonomy, but this autonomy may also lead to dysfunctional behaviour, due to conflicts of interest, ambiguities and individual preferences. The contribution of the operational network seems difficult to investigate. This is illustrated by an empirical study of the role of operational people in a semi‐process industry. This study shows that concepts of organisational psychology are very applicable here. However, most results relate perceived behaviour to perceived performance; it appeared to be difficult to relate actual organisation characteristics to actual performance. This paper discusses the research approach that is necessary to be able to fill the gap between “perceived” and “actual”.FindingsTo fill the gap between “perceived” and “actual” behaviour, it is necessary to formalize the behaviour of the members of the operational network by adopting an adequate planning and control framework. In most situations such a planning and control framework is not available. Therefore, a study of the role of the operational network needs to be accompanied by a participative design of the planning and control framework.Originality/valueThis paper conducts a convincing investigation into the contribution of the operational network to operational performance, a subject which hitherto has been only marginally addressed.

Specification for a dynamic vehicle routing and scheduling system

Abstract In an e-commerce environment, order fulfilment is driven by customer demands and expectations. A dynamic vehicle routing and scheduling system may be specified which allows e-commerce customers to select their own delivery Time Windows and have these confirmed on-line as they place their order. The methodology is based upon demand forecasting, which leads to the generation of phantom orders and phantom routes. Subsequently, actual orders substitute for phantom orders in an on-line customer order process. The routing and scheduling method includes using both parallel tour-building and parallel insertion algorithms. Customer service levels are confirmed using GPS tracking and tracing, and a feedback loop uses expert systems or artificial intelligence as an input to the demand forecasting data to restart the whole process.

Customer order lead times for production based on lead time and tardiness costs

In practice decisions regarding customer order lead time and customer order processing are taken in different parts of the organization. Definite lead times are quoted by the sales department and job flow decisions are taken by the production department. This split up of decision responsibilities is in accordance with the differences in scope and control areas in complex organizations like job-shops or engineer-to-order firms. In this paper we present a simple economic model of sales and production in a job shop. Production aims at realizing the jobs by using its capacity to perform the operations of the jobs. Sales, using a model of the performance of production, aimes at maximizing expected profit by quoting lead times to customer orders, given that penalties are put on quoting long lead times and on tardy deliveries. Analysis of the model shows that for any combination of penalties on lead times and tardiness, there exist job flow times distributions for which it is optimal for sales to quote unrealistically short customer order lead times. Specifically if the job flow time has a high mean value and/or a high variance sales tends to quote a lead time with zero lead time penalty. In practice we often observe that the job flow times are long and have a high variance. The research presented in this paper shows why this may stimulate sales to neglect flow time information and to quote unrealistically short lead times. Our model shows that under a wide range of economic conditions a job flow time distribution with a small mean and a small variance seems to be a necessary condition for sales to quote realistic customer order lead times.

Operational competitive requirements for the twenty‐first century

To stay competitive in future global markets, manufacturers must face the challenge to sustain superior performance in three important aspects of operational capability ‐ flexibility, speed, and the ability to serve the customer. These capabilities are achieved by the manufacturer’s ability to excel in product development, production operations, customer order processing and delivery, and the development of supply networks. The specific requirements for each of these systems are described. Examples of techniques that will be beneficial in achieving excellence in each of these critical areas of operations are given.

Sales order processing

GPSS simulation of the customer order processing through the warehouse containing packaged items at a large chemical plant in St. Louis, Missouri, is described. Objective is to determine the service rate on a certain type of order and by varying the number of warehouse personnel at key positions to obtain comparison service rates for management review.