Demand Planning Integration Best Practices: SAP SCM Perspective – PART 4

The growing number of global shocks, which include geopolitical instabilities, pandemics, and climate shock, among others, has revealed major weaknesses in the U.S. manufacturing supply chains. The need to increase supply chain resiliency and, at the same time, improve sustainability and resource efficiency has become a strategic necessity. The proposed study is an AI- and machine learning-controlled predictive analytics-based framework created to enhance the reliability and resilience of the U.S. manufacturing supply chain systems. The framework incorporates heterogeneous data sources such as operational, environmental, market, and logistics data to facilitate the proactive identification of risks, prediction of demand, and optimization of resources. Developed machine learning models are used to embrace non-linear relationships with complexities across the supply chain nodes to assist in real-time decision-making and adaptive response strategies. The proposed framework enables the identification of disruption early in the supply chain planning and execution, better management of inventory and capacity, reduced waste, and increased energy and material efficiency by integrating predictive analytics into the supply chain planning and execution. In theory, this study will be a step forward in the interplay of artificial intelligence, sustainable supply chain management, and resilience engineering. In practice, it provides decision-makers with a scalable and evidence-based instrument in order to enhance the continuity of operations, environmental performance, and competitiveness in the long run of the U.S. manufacturing supply chains.

Through the development of efficient data collecting technologies like RFID, and inter-enterprise collaboration platforms such as web services, companies which participate in supply chains can acquire visibility over the whole supply chain, and can make decisions to optimize the overall supply chain networks and processes, based on the extracted knowledge from historical data collected by the visibility system. Although not currently active, the MeatWatch system has been developed, and is used in part for this purpose, in the imported beef distribution network in Korea. However, the imported beef distribution network is too complicated to analyze its various aspects using ordinary process analysis approaches. In this paper, we suggest a novel approach, called RFID-based supply chain process mining, to automatically discover and analyze the overall supply chain processes from the distributed RFID event data, without any prior knowledge. The proposed approach was implemented and validated, by using a case study of the imported beef distribution network in Korea. Specifically we demonstrated that the proposed approach can be successfully applied to discover supply chain networks from the distributed event data, to simplify the supply chain networks, and to analyze anomaly of the distribution networks. Such novel process mining functionalities can reinforce the capability of traceability services like MeatWatch in the future.

Supply Chain Event Management (SCEM) is an approach aiming to fill the gap between Supply Chain Planning and Supply Chain Execution. The goal of SCEM applications is to monitor the states of supply chains by observing specific events and exceptions in real-time and alerting managers if problems occur. This paper presents an architecture for a mobile SCEM system based on software agents, Auto-ID technologies, and mobile computing. It introduces the main layers and components of this architecture. A special focus is placed on web services, mobile user interfaces, and integration of a multi-agent platform as innovative solutions to improve SCEM.

Supply chain management survey of Swedish manufacturing firms

The Internet of Things (IoT) envisions an ecosystem where smart and interconnected objects can sense surrounding changes, communicate with each other, process information and take active roles in decision making. Optimizing supply chain performance is a primary concern of manufacturing and logistics organizations. Radio Frequency Identification (RFID) is helping organizations to build automated and interconnected smart environment by object identification and tracking, motivating the first step towards an IoT-enabled world. This chapter attempts to understand extant literature studying applications of RFID in implementing the IoT in supply chain management. We categorize extant literature, firstly, based on research methodology and secondly, based on supply chain processes. We find that presently academic activity is around conceptualizing the usability of RFID in the IoT with limited analytical and empirical evidence. Supply chain processes such as demand planning, procurement, retail shelf space management and product returns are prospective areas for interesting future research.

Buy BookDesk / Exam Copy...MoreThe critical role that supply chain planning contributes to a firm’s financial well-being has never been greater. All too often, however, considerable managerial resources are directed toward planning activities with minimal results. In this book, we present proven, practical management frameworks used by the authors to support supply chain operations management and planning in private industry. These frameworks provide methodologies for managing critical activities such as supply chain strategic planning and project selection, manufacturing and distribution planning, performance measurement, supply chain risk management, and customer logistics and inventory deployment. The book is intended for supply chain professionals, as well as for graduate and advanced undergraduate students. Practitioners will obtain valuable new insights and examples of implementable frameworks and methods for managing their supply chain functions and organizations. Students will develop an understanding of real-world approaches for supply chain planning, decision support, and many other key activities. $(function() {$("#shortDescription").html(function() { if($("#fullDescription").html().length > 1800){ return $("#fullDescription").html().substr(0, 1800) + $("#shortDescription").html(); }else { return $("#fullDescription").html().substr(0, 1800); } });$("#showText").click(function() {$("#fullDescription").css("display", "inline");$("#shortDescription").css("display", "none");$("#showText").css("display", "none"); }); });

This chapter explores novel ways of improving flexibility, responsiveness, and competitiveness via strategic information technology (IT) alliances among channel members in a supply chain network. To gain competitiveness, firms have to constantly update their operational strategies and information technologies through collaborative efforts of a “network” of supply chain members rather than the efforts of an individual firm. In sum, the foci of this chapter are: (1) an overview of supply chain management (SCM) issues and problems, (2) supply chain coordination and integration, (3) the latest IT applications for improved supply chain performance and coordination, and (4) strategic IT alliances. This chapter concludes with a discussion of business implications and recommendations of future research.

Retail shelf management means matching cost-efficient consumer demand with retail operations. This takes especially place at the point-of-sales in front of the retail shelf, where consumer demand meets retail offer. Therefore, we develop a comprehensive planning framework for retail demand and supply chain planning in this chapter, before we analyze and develop decision support systems for retail shelf management. The increasingly competitive retail environment requires greater customer orientation and operational efficiencies. Consumers are always demanding higher service levels and better purchase prices. Retailers are striving towards broader product variety, better prices and lower costs, and are growing vertically by taking over more logistical functions. Effective structures and planning tools for demand and supply chain planning (DSCP) are therefore the core technique for coordinating thousands of individual decisions in supply chain and customer management.

Since the onset of Supply chain management has gained maximum recognition, with many countries elevating supply chain levels at the strategic level. This article selects AH Company’s supply chain network as the research object, analyzes the main challenges faced by the company in the supply chain aspect, and evaluates its original supply chain network. It incorporates sustainable development factors into the current global supply chain management theme, taking into account four aspects: total supply chain cost, service level, corporate sustainable development vision (carbon emissions), and supply chain risks. The “Supply Chain Guru X (2022)” software is applied to propose optimization solutions for existing problems on the basis of the original supply chain network. Based on the existing data, a comprehensive comparison of the advantages and disadvantages of various optimization schemes is implemented to select the current optimal supply chain network optimization scheme. In the process of optimizing the plan, the multi-objective optimization theory and big data processing technology were emphasized, and the corresponding technical theories were integrated into the “Supply Chain Guru X (2022)” software. By optimizing the supply chain network of AH Company, the overall operating costs of the company’s supply chain network have been significantly reduced while meeting customer needs. Concurrently, it can provide a good example for optimizing the supply chain network of similar enterprises.

The supply chain (SC) network is prone to disturbance due to various uncertainties associated with their subsystems. The COVID-19 outbreak has exposed the global vulnerability of the supply chain network. The current pandemic has severely affected almost every SC network because its members are situated at the international level. One of the reasons for SC network failure is the deterministic assumptions of different parameters. A realistic SC network model requires the use of the uncertain value of the parameters, which can be further captured by fuzzy numbers. This paper discusses the possibilistic moment of several nonlinear types of fuzzy numbers that are important for SC network modeling. We give closed-form possibilistic moments’ expression for various types of fuzzy numbers that are very similar to the moment’s properties in probability theory and stochastic process. We then illustrate the application of proposed fuzzy numbers by solving an inventory model. This paper also provides results related to the EPQ inventory model in a fuzzy possibilistic setup.

There are many articles mentioning the advantages and benefits of supply chain alignment none, however, describe how to model such alignment in the supply chain. This paper offers a framework for examining and understanding the impact supply chain alignment has on performance. Based on supply chain mapping approach, a model describing alignment between processes/ flows in the supply chain network is developed. The model is then validated using a dataset of 316 enterprises operating in the Vietnam construction sector. Evidence indicates that the supply chain processes and flows were aligned. According to the results, the proposed supply chain alignment model is able to explain a 59.9% variance in operational performance, 58.9% in customer satisfaction, 34.5% in operating costs and 67.4% in business performance. To successfully align the supply chain network, companies can use the proposed model as a “road-map” to reduce high costs, to avoid the loss of control, management difficulties and/or vulnerability to opportunistic action, all of which may hinder efforts to align the supply chains.

The demand and supply chain planning process for manufacturers, distributors, and retailers has evolved over the years. It has gone from a disjointed, unconnected, slow, inaccurate, fairly manual set of processes to an integrated, timely process enabled by the use and coordination of highly trained people, lean, agile processes, and cutting-edge technology. To make this set of processes work effectively, one has to fully understand and appreciate that there is an "art and science" aspect to the process which can take years of education and experience to fully understand. Essentially, this book will offer the reader a chance to fully understand the interconnected set of processes in a "best-practice" application. Furthermore, examples and cases will be used to illustrate its practical application in today’s complex global supply chain. In addition, readers will understand and be able to apply and articulate the concepts, tools, and techniques used in the efficient supply of goods and services in today’s changing global economy. It will help them to learn how businesses, through their supply chain, work both internally and with their trading partners – both upstream and downstream – to build strong relationships and integrate demand and supply planning activities across the supply chain to deliver customer value efficiently and effectively. They will learn about the tools and technologies enabling integration, and the critical drivers and key metrics of supply chain performance.

PurposeThe COVID-19 crisis has caused major supply chain disruptions, and these can be traced back to basic supply chain risks that have previously been well identified in literature. The purpose of this paper is to suggest a pathway for closing the gap between supply chain resilience research and efforts in industry to develop a more resilient supply chain.Design/methodology/approachBased upon virtual roundtables with supply chain executives, supplemented with interviews and publicly available datapoints about COVID-19 impact on the supply chain, we explore challenges in industry and suggest opportunity areas where research can support efforts in industry to improve supply chain resilience.FindingsDuring the COVID-19 crisis, participating supply chain executives are experiencing textbook supply, demand and control risks in the supply chain. They also observe a lack of preparedness, shortcomings of current response plans and the need for greater supply chain resilience. Focus areas in improving resilience mirror generic recommendations from literature and provide a rich opportunity to reduce the gap between research findings and efforts in industry.Research limitations/implicationsMore empirical, event-based and less conceptual research into supply chain resilience has been called for several times during the last two decades. COVID-19 provides a very rich opportunity for researchers to conduct the type of research that has been called for. This research may contribute to the structurally de-risking of supply chains. Areas of research opportunity include decision models for supply chain design that avoid overfocusing on costs only, and that consider the value of flexibility, short response times and multiple sources as well as methods for enriching supplier segmentation and evaluation models to reduce a focus on savings and payment terms only.Practical implicationsKey levers for de-risking the supply chain include the need to balance global sourcing with nearshore and local sourcing, the adoption of multiple sources and a greater utilization of information technology to drive more complete and immediate information availability. Perhaps most importantly, talent management in supply chain management needs to promote a focus not just on costs, but also on resilience as well as on learning from current events to improve decision-making.Social implicationsThere is a great opportunity for supply chain managers to grow their contribution to society beyond risk response into the proactive reduction of risks for the future. Researchers can serve society by informing this progress with impactful research.Originality/valueThis article offers initial empirical exploration of supply chain risks experienced in the context of COVID-19 and approaches considered in industry to improve supply chain resilience. Opportunities for empirical, event-based and less conceptual research that has been called for years, are identified. This research can help close the gap between supply chain resilience research and efforts in industry to improve supply chain resilience. Hopefully the research opportunities identified can inspire the flurry of research that can be expected in response to the multiple special issues planned by journals in our field.

There are several supply chain-related problems facing the construction industry, such as poor construction site logistics, lack of communication and trust. These problems can jeopardize construction projects through delays and cost overruns. Supply chain planning, a part of supply chain management (SCM), can be used as a tool to deal with these problems. The purpose of this paper is to study how linkages between common supply chain-related problems in construction can be illustrated and to demonstrate how they could be resolved through supply chain planning. Firstly, we identify how the linkages between common problems can be illustrated, and secondly the role of supply chain planning in resolving these problems. A conceptual model is developed that was verified using three cases with Swedish gypsum and kitchen supply chains. The model is shown to be useful in illustrating how supply chain problems occurring at executional level on-site are related to problems originating in lack of planning at company/pre-construction level. The study thus demonstrates how supply chain planning can aid in resolving supply chain problems. The paper contributes by bringing planning back into the picture and by showing how supply chain planning can help to adopt SCM in construction.

Application of fuzzy goal programming and genetic algorithm is considered in this paper. We extend a multi-objective model for integrated inventory-production-distribution planning in supply chain (SC). We consider a supply chain network which consists of a manufacture, with multiple plants, multiple distribution centers (DCs), multiple retailers and multiple customers. The manufacturer produces several items. Decision maker’s imprecise aspiration levels of goals are incorporated into model using fuzzy goal programming approach. Due to the complexity of problem in large size and in order to get a satisfactory near optimal solution with great speed, a new genetic algorithm is proposed to solve constrained problems. To show the efficiency of the used model and fuzzy goal programming approach and genetic algorithm for the collaborative inventory-production-distribution problem, computational experiments are performed on a hypothetically constructed case problem.