Where marketing planning fails

This article discusses the results of a recent study of marketing planning in U.K. manufacturing companies. A sample survey revealed some strengths in marketing planning, but also revealed many weaknesses. For example, only a small proportion of companies prepare a comprehensive marketing plan. These identified weaknesses suggest that, in many situations, marketing planning has failed and the author suggests where it has failed. After discussing the results in detail, the author summarizes the identified strengths and weaknesses and suggests several implications for managers who are responsible for marketing planning.     

Business drivers not sector membership determine the most effective production planning and control; a novel approach to a perennial problem

This paper outlines research aimed at developing novel planning and scheduling reference models for industrial sectors where the MRPII paradigm is not appropriate. It outlines the process mapping approach adopted, the data capture method developed for the case study companies, the use of ARIS (Scheer's) enterprise modelling tool, and the first stages in the production of sector reference models.     

Joint modified block replacement and production/inventory control policy for a failure-prone manufacturing cell

This paper considers a joint preventive maintenance (PM) and production/inventory control policy of an unreliable single machine, mono-product manufacturing cell with stochastic non-negligible corrective and preventive delays. The production/inventory control policy, which is based on the hedging point policy (HPP), consists in building and maintaining a safety stock of finished products in order to respond to demand and to avoid shortages during maintenance actions. Without considering the impact of preventive and corrective actions on the overall performance of the production system, most authors working in the reliability and maintainability domains confirm that the age-based preventive maintenance policy (ARP) outperforms the classical block-replacement policy (BRP). In order to reduce wastage incurred by the classical BRP, we consider a modified block replacement policy (MBRP), which consists in canceling a preventive maintenance action if the time elapsed since the last maintenance action exceeds a specified time threshold. The main objective of this paper is to determine the joint optimal policy that minimizes the overall cost, which is composed of corrective and preventive maintenance costs as well as inventory holding and backlog costs. A simulation model mimicking the dynamic and stochastic behavior of the manufacturing cell, based on more realistic considerations of the real behavior of industrial manufacturing cells, is proposed. Based on simulation results, the joint optimal MBRP/HPP parameters are obtained through a numerical approach that combines design of experiment, analysis of variance and response surface methodologies. The joint optimal MBRP/HPP policy is compared to classical joint ARP/HPP and BRP/HPP optimal policies, and the results show that the proposed MBRP/HPP outperforms the latter. Sensitivity analyses are also carried out in order to confirm the superiority of the proposed MBRP/HPP, and it is observed that for practitioners, the proposed joint MBRP/HPP offers not only cost savings, but is also easy to manage, as compared to the ARP/HPP policy.     

Production planning and control tools

There are numerous tools available to be used for production planning and control purposes. The number of tools is ever increasing, and so are the levels of sophistication as well as complexity. For the specific manufacturing firm, the task of selecting the most appropriate set of tools is not trivial. However, in recent years, the understanding of the relationship between tools and manufacturing environments for which they are suitable has increased. The purpose of this paper is to provide an overview of production planning and control tools available today, as well as new trends, issues and ideas.     

MULTISTAGE SAFETY STOCK PLANNING WITH ITEM DEMANDS CORRELATED ACROSS PRODUCTS AND THROUGH TIME

Determining safety stocks in multistage manufacturing systems with serial or divergent structures, where end-item demands are allowed to be correlated both between products as well as in time, is my focus. I show that these types of correlation have contrary effects on the distribution of safety stocks over the manufacturing stages and that neglecting the correlation of demand can lead to significant deviation from the optimal buffer policy. Using base-stock control and assuming total reliability for internal supplies, I present a procedure for integrated multilevel safety stock optimization that can be applied to arbitrary serial and divergent systems even when demand is jointly cross-product and cross-time correlated. As I demonstrate in an example for autocorrelated demands of a moving average type, there are specific solution properties that drastically reduce the computational effort for safety stock planning. Safety stocks determined in that way can be used as an appropriate protection against demand uncertainties in material requirements planning systems.     

Planning for manufacturing technology

It is charged that many U.S. and European manufacturers have neglected to invest in new manufacturing technology. Consequently, these manufacturers have sacrificed their long-range competitiveness. This problem has occurred partly due to the lack of a normal planning process for investment in manufacturing technology that can alert managers to the need for the modernization of manufacturing technology before it is overdue.This article recommends the use of a new approach for manufacturing technology planning based on a process that monitors the deterioration of manufacturing technology over time.     

Forecasting Product Returns for Remanufacturing Operations

Driven by legislative pressures, an increasing number of manufacturing companies have been implementing comprehensive recycling and remanufacturing programs. The accurate forecasting of product returns is important for procurement decisions, production planning, and inventory and disposal management in such remanufacturing operations. In this study, we consider a manufacturer that also acts as a remanufacturer, and develop a generalized forecasting approach to determine the distribution of the returns of used products, as well as integrate it with an inventory model to enable production planning and control. We compare our forecasting approach to previous models and show that our approach is more consistent with continuous time, provides accurate estimates when the return lags are exponential in nature, and results in fewer units being held in inventory on average. The analysis revealed that these gains in accuracy resulted in the most cost savings when demand volumes for remanufactured products were high compared to the volume of returned products. Such situations require the frequent acquisition of cores to meet demand. The results show that significant cost savings can be achieved by using the proposed approach for sourcing product returns.     

A new method for order release

A new method for determining when to release jobs into a probabilistic manufacturing line is described. This new heuristic releases jobs only if their predicted waiting times are sufficiently estimated using simulation. Moreover, we use simulation to examine the performance of this heuristic and others CONWIP, Starvation Avoidance for a manufacturing flow line with exponential machine processing, failure, and repair times. The reasons why it is appropriate to compare order release mechanisms using tradeoff curves of lead time versus customer service are described. The simulation experiments show that the new 'waiting time heuristic' is superior to other order release mechanisms in situations where low lead time is required. small. Waiting times are     

Production planning of mixed-model assembly lines: a heuristic mixed integer programming based approach

Process technology capabilities are becoming increasingly important as flexible manufacturing continues to be more prevalent, and as competition compels companies to provide expanded variety, at ever lower cost, so introducing plant and processes technological constraints. Model flexibility can also benefit from an appropriate production planning process, especially concerning mixed-model assembly lines, since it can facilitate master scheduling and line balancing activities, which are essential aspects of flexibility. Robust and practical planning approaches have to take into account two different aspects: the first consists in ensuring that the elaborated aggregate plan can be disaggregated into at least one detailed feasible plan for the realised demand, whereas the second in ensuring that this detailed plan is feasible at the operational level. This article faces the model flexibility challenge, reviewing and discussing the planning problem of a real world assembly manufacturing system, producing high volume and a variety of agricultural tractors and machines, analysing and resolving some important issues related to technological, organisational and managerial constraints. This article illustrates the implementation of an Advanced Planning System integrated with a mixed integer-programming model, which is solved by a new iterative heuristic approach capable of achieving interesting planning improvements for model-flexibility management.     

Managing green productivity: Some strategic directions

The twentieth century has been the hallmark for managing total productivity, where the emphasis is towards maximization of outputs commensurate with the consumption of inputs. This is being challenged now to evolve the world class manufacturing strategy requiring a paradigm shift. This shift is towards green productivity, which looks forward to socially appropriate production and consumption aiming at value innovation and resource conservation. This paper is an attempt to outline the concepts and experiential learning about green productivity.     

A Capacitated Multi‐echelon Inventory Placement Model under Lead Time Constraints

We develop an inventory placement model in the context of general multi‐echelon supply chains where the delivery lead time promised to the customer must be respected. The delivery lead time is calculated based on the available stocks of the different input and output products in the different facilities and takes into account the purchasing lead times, the manufacturing lead times, and the transportation lead times. We assume finite manufacturing capacities and consider the interactions of manufacturing orders between time periods. Each facility manages the stocks of its input and output products. The size of customer orders and their arrival dates and due dates are assumed to be known as in many B2B situations. We perform extensive computational experiments to derive managerial insights. We also derive analytical insights regarding the manufacturing capacities to be installed and the impacts of the frequency of orders on the system cost.     

An evaluation of product commonality and group technology production methods in a pre-defined multiple-machine scenario

Group technology has been used successfully for a number of years as a setup reduction tool. It is especially valuable in low-volume high-mix manufacturing environments where products and machines may be partitioned into product families and machine cells. The partitioning of machines or processes into cells may be limited by practical constraints, and the partitioning of products is complicated as the number of products and processes increases. In this paper, the authors examine the behaviour of various grouping strategies lauded as being appropriate in situations where machine partitioning and product routeing are determined by technological constraints. Of specific experimental concern in this paper is the effect of the mean and variance of component part commonality between products on system performance under various grouping strategies in a multiple-machine environment.     

Simulation-based performance assessment of master planning approaches in semiconductor manufacturing

Semiconductor manufacturing is confronted with a large number of products whose mix is changing over time, heterogeneous fabrication processes, re-entrant flows of material, and different sources of environmental and system uncertainty. In this context, the mid-term production planning approach, i.e., master planning, typically does not capture the entire complexity of the shop-floor. It deals with an aggregated representation of the production system. There is a need for evaluating the planning algorithm in use while taking the execution level into account. Therefore, we introduce in this paper a simulation-based framework that allows for modeling the behavior of the market demand and the production system. An appropriate performance assessment methodology is proposed. The performance of two heuristic approaches for master planning in semiconductor manufacturing, a genetic algorithm and a rule-based assignment procedure, is evaluated within a rolling horizon setting while considering demand and execution uncertainty. A reduced discrete-event simulation model is used to mimic a one-stage network of wafer fabrication facilities. The results of simulation experiments are discussed.     

Strategic Manufacturing Planning Systems and Their Linkage to Planning System Success

Academics and practitioners alike are focusing more attention on manufacturing strategy after having recognized the important role it plays in shaping the success of industrial firms. Even though research in this area has increased in the last decade, the focus of much of that work has been on the content rather than the process of the manufacturing strategy. Consequently, this study attempts to understand the important elements of the strategic manufacturing planning process and its effectiveness. Borrowing from the extant literature in the fields of strategic management and information systems, we propose a research model that relates strategic manufacturing planning system design to planning system success. Using structured questionnaires, empirical data is collected from over 200 manufacturing executives to test the model hypotheses. Planning process in manufacturing was found to be a bottom‐up approach from a corporate or business perspective, which differs from the top‐down planning process prevalent in strategic information systems planning process. Findings also indicate that greater planning system success in manufacturing is associated with a planning system that combines some “rational” elements (formality, comprehensiveness, control focus, longer horizon) with others that lend adaptability (wider participation and more intense interaction). But the strategic manufacturing planning system is more than just a collection of independent planning characteristics. Instead, it can be viewed as a gestalt planning system whereby planning characteristics move together in affecting overall planning system success.     

PLANNING PROCESS ARCHETYPES AND THEIR EFFECTIVENESS

Case studies of planning were profiled to identify how practitioners carry out a planning process. Five archetype processes emerged from the analyses which were named evaluative, historical model, off-the-shelf, search, and nova. These five processes were compared in terms of their adoption rate and perceived quality. The evaluative process was found to be the most successful, followed by the historical model and off-the-shelf processes. Nova and search processes had the least success. Contextual factors, such as time pressure and resources available, were included in the analyses to identify conditions of use for each process type. In situations where time pressure was high or the planning concerned services, the off-the-shelf model produced the best results; in situations where time pressure was low or the planning concerned internal operations, the historical model produced the best results. Most of the planning activity in organizations appears to be informal and heuristic, ignoring the methods described in the planning literature.     

Aligning manufacturing strategy and levels of automation: A case study

Research has shown that alignment between manufacturing strategy and decisions regarding automation are often of an ad hoc nature, i.e. the support for automation decisions is poor. Support tools to find an appropriate level of automation are thus needed in order to achieve more efficient and robust production systems. The methodology presented in this paper contains five sub-processes where the chosen level of automation is aligned with the manufacturing strategy. Together they form an automation strategy, which secures a desired direction of the firm and also supports robustness and reliability of the manufacturing system due to the holistic approach chosen.     

Bottleneck management: theory and practice

Advocates of TOC believe that bottleneck resource restricts an operation's ability to make money, and the best way to maximise income is to fully exploit the bottleneck resource. Almost all TOC literature focuses on situations where 100% bottleneck utilisation is applied. Based on the implementation experience, the finding is that the optimal level of bottleneck utilisation should be less than 100% and any attempt to increase utilisation beyond the optimal level brings disastrous results for a door manufacturing plant. In order to improve and maintain the performance of the plant effective bottleneck management is critical. The experience provides a deeper understanding of how to design such plants, which could be beneficial for practising managers and academics working with TOC concepts.     

Production planning in practice

The changes and pressures facing the manufacturing and engineering industries today are increasing the importance of effective aggregate manpower and production planning. Several different theoretical optimisation models to tackle this problem have been described in detail in the literature but there have been few applications of them in practice.The reasons for this are many but include: the difficulty in expressing managements' conflicting and mixed objectives in an objective function; the necessity to oversimplify real life systems to enable these methods to be used; the simplistic approach to manpower planning used in these models; the difficulty in gaining managements' acceptance and finally the fact that what management actually wants is a tool to assist them in planning and decision making.What is being used by many managements is a case-study deterministic simulation model. Many companies are adopting this type of model for all types of planning and twelve out of twenty-seven companies visited in a research project described in this paper were using this type of model for aggregate manpower and production planning. It is proving to be an effective management tool and is being readily accepted principally because modern specialised financial modelling languages are enabling these models to be built, understood and used by non-specialist managers.     

Cloud manufacturing system for sheet metal processing

Abstract

Cloud computing is changing the way industries and enterprises run their businesses. Cloud manufacturing is emerging as an approach to transform the traditional manufacturing business model, while helping the manufacturer to align production efficiency with its business strategy, and creating intelligent factory networks that enable collaboration across the whole enterprise. Many production planning and control (PPC) problems are essentially optimisation problems, where the objective is to develop a plan that meets the demand at minimum cost or maximum profit. Because the underlying optimisation problem will vary in the different business and operation phases, it is important to think about optimisation in a dynamic mechanism and in a number of interlinked sub-problems at the same time. Cloud manufacturing has the potential to offer decision support as a service and medium of communication in PPC. To solve these problems and produce collaboration across the supply chain, this paper provides an overview of the state of the art in cloud manufacturing and presents a model of cloud-based production planning and production system for sheet metal processing.