Marketing/Manufacturing Trade-Offs in Product Line Management

A critical decision facing firms across industries is the selection of a mix of products to offer in the marketplace. Both in practice and in the academic literature, the product line design problem has typically been considered from a marketing perspective, with a focus on how alternative sets of products interact and compete in the marketplace. The operational implications of product line decisions have been largely ignored, even while the importance and complexity of interactions among products in the manufacturing environment increase with broadening product lines. Furthermore, consideration of manufacturing synergies among products in product line design is increasingly beneficial given efforts in many industries to improve co-ordination of manufacturing activities across products. In this work we examine the benefits of integrating marketing implications of product mix with more detailed manufacturing cost implications. Traditional product line models are extended to capture both individual product costs and relevant cost interactions among products. The relevant marketing and manufacturing elements are considered in a mathematical programming formulation that identifies a profit maximizing mix of products. The resulting normative model of the product line design problem is used to generate insights into important cross-functional issues in product line management. Specifically, we examine the impact of alternative manufacturing environment characteristics on the composition of the optimal product line.     

Integrated workforce capacity and inventory management under labour supply uncertainty

In a manufacturing environment with volatile demand, inventory management can be coupled with dynamic capacity adjustments for handling the fluctuations more effectively. In this study, we consider the problem of integrated capacity and inventory management under non-stationary stochastic demand and capacity uncertainty. The capacity planning problem is investigated from the workforce planning perspective where the capacity can be temporarily increased by utilising contingent workers from an external labour supply agency. The contingent capacity received from the agency is subject to an uncertainty, but the supply of a certain number of workers can be guaranteed through contracts. There may also be uncertainty in the availability of the permanent and contracted workers due to factors such as absenteeism and fatigue. We formulate a dynamic programming model to make the optimal capacity decisions at a tactical level (permanent workforce size and contracted number of workers) as well as the operational level (number of workers to be requested from the external labour supply agency in each period), integrated with the optimal operational decision of how much to produce in each period. We analyse the characteristics of the optimal policies and we conduct an extensive numerical analysis that helps us provide several managerial insights.     

VOLUME AND CAPACITY INTERACTION IN FACILITY DESIGN

This paper addresses the joint facilities design problem of determining both demand and capacity with stochastic demand arrivals and stochastic processing throughput. Using a simple M/M/1 queueing model of a profit maximizing firm, we link marketing and production decision variables by recognizing appropriate congestion costs, and show that coordinated decision-making provides results superior to making demand and capacity decisions sequentially. Sensitivity analysis indicates that the model is robust with respect to its assumptions and parameters. An example illustrates the approach and demonstrates the application of the model.     

Joint production and maintenance planning with machine deterioration and random yield

Production, yield and maintenance are three key components for sustaining the competitiveness of a manufacturing firm. In this paper, we investigate a joint production and maintenance planning problem in a periodic review environment subject to stochastic demand and random yields. The manufacturing system deteriorates from period to period according to a discrete-time Markov chain. The objective is to find an integrated lot sizing and maintenance policy for the system such that the aggregate cost associated with production, holding, backlogging and maintenance is minimised. We formulate this integrated planning problem as a Markov decision process and analyse the structural properties of the optimal policies. We prove that the optimal production and the maintenance policies both exhibit a control limit structure and show that the solution to the finite-horizon problems converges to that of the infinite-horizon problem.     

Advertising for price-sensitive products with multi-attribute considered

This study provides a framework for a marketing manager to make advertising decisions for price-sensitive products. One promotion, price discount, is offered at the same time as local advertising and national advertising. Unlike previous studies that mainly considered sales profit as the primary goal, this study considers two other attributes, goodwill and customer scale, in making advertising investments. In all cases, a strategy with larger values of these attributes is better. However, maximising these three attributes simultaneously is difficult or impossible, so managers must balance trade-offs between them. We utilise the stochastic multi-attributes analysis in this paper. This method is a multi-attributes decision support technique based on exploring the weight space, helping managers make optimal advertising decisions regarding these three attributes. A numerical example is presented to illustrate the proposed approach.     

Semiconductor capacity planning: stochastic modelingand computational studies

This paper presents a multistage stochastic programming model for strategic capacity planning at a major US semiconductor manufacturer. Main sources of uncertainty in this multi-year planning problem include demand of different technologies and capacity estimations for each fabrication (fab) facility. We test the model using real-world scenarios requiring the determination of capacity planning for 29 technology categories among five fab facilities. The objective of the model is to minimize the gaps between product demands and the capacity allocated to the technology specified by each product. We consider two different scenario-analysis constructs: first, an independent scenario structure where we assume no prior information and the model systematically enumerates possible states in each period. The states from one period to another are independent from each other. Second, we consider an arbitrary scenario construct, which allows the planner to sample/evaluate arbitrary multi-period scenarios that captures the dependency between periods. In both cases, a scenario is defined as a multi-period path from the root to a leaf in the scenario tree. We conduct intensive computational experiments on these models using real data supplied by the semiconductor manufacturer. The purpose of our experiments is two-fold: first to examine different degree of scenario aggregation and its effects on the independent model to achieve high-quality solution. Using this as a benchmark, we then compare the results from the arbitrary model and illustrate the different uses of the two scenario constructs. We show that the independent model allows a varying degree of scenario aggregation without significant prior information, while the arbitrary model allows planners to play out specific scenarios given prior information.     

Managing production level in new product diffusion: an agent-based simulation approach

Managing production level after the launch of a new product is a challenging problem and is critical to the overall profit of manufacturing firms. The problem involves concepts from different fields including production planning, manufacturing flexibility, forecasting, and marketing. In this paper, the gaps in the existing literature are first illustrated. With the goal of addressing the identified research gaps, agent-based modeling and simulation is employed to analyze the performance of different production planning strategies under various levels of volume flexibility and consumer social network structures. The key distinguishing feature of the developed model is the capability of the manufacturing firm to adjust its production level by forecasting the future demand. The analysis of the simulation outputs yields substantial results by challenging some intuitive and traditional understandings of manufacturing systems. The paper also provides a discussion on managerial implications of the results in order to provide the managers with guidelines on the implementation of the model in real-world diffusion environments.     

Sequential DOE via dynamic programming

The paper considers a sequential Design Of Experiments (DOE) scheme. Our objective is to maximize both information and economic measures over a feasible set of experiments. Optimal DOE strategies are developed by introducing information criteria based on measures adopted from information theory. The evolution of acquired information along various stages of experimentation is analyzed for linear models with a Gaussian noise term. We show that for particular cases, although the amount of information is unbounded, the desired rate of acquiring information decreases with the number of experiments. This observation implies that at a certain point in time it is no longer efficient to continue experimenting. Accordingly, we investigate methods of stochastic dynamic programming under imperfect state information as appropriate means to obtain optimal experimentation policies. We propose cost-to-go functions that model the trade-off between the cost of additional experiments and the benefit of incremental information. We formulate a general stochastic dynamic programming framework for design of experiments and illustrate it by analytic and numerical implementation examples.     

Optimal supply contract selection

Selection of supply contracts is a critical decision faced by manufacturing firms in a variety of industries. Manufacturing firms often have the option of selecting from several types of supply contracts that include long-term, medium-term, and short-term contracts. While extant literature has stressed the importance of such contracts, few methodologies have been proposed for optimally selecting contracts under various business conditions. To this end, this paper proposes a methodology for optimal contract selection based on a mixed-integer programming approach. We present specific insights to manufacturing managers on choosing the right contracts in the presence of market price uncertainty, supplier discounts, investment costs, and supplier capacity restrictions.     

Using a pseudo-stochastic approach for multiple-parts scheduling on an unreliable machine

In this paper we follow previous “pseudo-stochastic” approaches that solve stochastic control problems by using deterministic optimal control methods. In a similar manner to the certainty equivalence principle, the suggested model maximizes a given profit function of the expected system outcome. However, unlike the certainty equivalence principle, we model the expected influences of all future events (including those that are expected beyond the planning horizon), as encapsulated by their density functions and not only by their mean values. The model is applied to the optimal scheduling of multiple part-types on a single machine that is subject to random failures and repairs. The objective of the scheduler is to maximize the profit function of the produced multiple-part mix. A numerical study is performed to evaluate the suggested pseudo-stochastic solutions under various conditions. These solutions are compared to a profit upper bound of the stochastic optimal control solutions.     

Supply chain coordination under trade credit and retailer effort

In this paper, we study the role of trade credit in coordinating a Capital Constrained Supply Chain in the presence of retailer Effort (CCSCE), essentially because of the impact of its related default risks on the relationship between the chain’s members. We consider a CCSCE consisting of a supplier and a retailer where the retailer may exert costly promotional efforts to increase the market demand but has limited capital and no access to bank financing due to low credit rating. Conversely, the supplier has adequate funds to offer trade credit to the retailer without borrowing from external channels. We then examine whether the existing coordination contracts can still coordinate the CCSCE under trade credit. Our result shows that these contracts can achieve coordination of the supply chain when the interest rate of trade credit is competitively priced. Nevertheless, this position cannot always be reached. That’s why we propose a generalised contract based on risk compensation to coordinate the CCSCE. Using our proposed coordinating contract, the supplier perfectly coordinates the retailer’s decisions for the largest joint profit, and arbitrarily allocates the maximised joint profit among supply chain members. Finally, the numerical study allows to verify this finding. From managerial insights, our results provide the supply chain managers with novel insights on how to combine trade credit with the existing coordination contracts in order to improve the profitability of the entire supply chain as well as the individual member.     

A price-directed decomposition approach for solving large-scale capacitated part-routing problems

We consider the problem of planning the production steps of several parts through a manufacturing system with both process and routing flexibilities. The problem is formulated as a network flow-based linear programming model which seeks to minimise the total material handling, production, and outsourcing costs subject to satisfying all the part demands and not exceeding any of the machine capacity limits. We develop a price-directed decomposition-based approach that exploits the special structure of the model in order to solve it. An extensive computation experiment is carried out in order to gain some insights into the impacts of flexibility in the manufacturing system on the optimal decision and cost, and to test the efficiency of the procedure in handling large scale problems.     

Coordinated optimisation of platform-driven product line planning by bilevel programming

Product line planning (PLP) aims at an optimal combination of product feature offerings, suggesting itself to be a determinant decision for a company to satisfy diverse customer needs and gain competitive advantages. Fulfilment of planned product lines must make trade-offs between product variety and production costs. To balance the costs of product lines, manufacturers often adopt a product platform configuration (PPC) approach to redesign product and process platforms by adding new modules to the legacy platforms. The PPC is an effective means of providing product variety while controlling the manufacturing costs. The PLP and PPC problems have traditionally been investigated separately in the marketing research and engineering design fields. It is important to coordinate PLP and PPC decisions within a coherent optimisation framework. This paper proposes a bilevel mixed 0–1 nonlinear programming model to formulate coordinated optimisation for platform-driven product line planning. The upper level deals with the PLP problem by maximising the profit of an entire product line, whilst the lower level copes with the multiple product platforms optimisation for the optimal PPC in accordance with the upper level decisions of product line structure. To solve this bilevel programming model, a bilevel genetic algorithm is developed to find the optimal solution. A case study of coordinated optimisation between an automobile line and its product platforms is presented to demonstrate the feasibility and effectiveness of the proposed bilevel programming in comparison with a typical ‘all-in-one’ approach and a non-joint optimisation programming.     

Coordinating production quantities and demand forecasts through penalty schemes

In this paper, we study coordination mechanisms through penalty schemes between manufacturing and marketing departments which enable organizations to match demand forecasts with production quantities. This research was motivated by our interactions with a leading electronics and computer manufacturer. We consider two possible organizational structures - centralized and decentralized. In the decentralized case we model a single period problem where demand is uncertain and the marketing department provides a forecast to manufacturing which in turn produces a quantity based on the forecast and the demand distribution. In the centralized case, marketing and manufacturing jointly decide on the production quantity. Among other results we show that by setting suitable penalties one can generate the same result in a decentralized system as that obtained from a centralized system. We also show that setting the correct penalty for marketing is essential for coordination. Subsequently, we analyze models where the marketing department has the ability to change the distribution of demand based on efforts (through promotion, advertising and personal relationship with customers). An interesting result indicates that ii. is possible to set penalties so that a coordinated decentralized system outperforms a centralized system when there are no tangible costs to the firm for the efforts expended by the marketing department.     

The effect of supply and demand uncertainties on the optimal production and sales plans for new products

When introducing a new product, firms face a hierarchy of decisions at the strategic and operational levels including capacity sizing, time to market or starting sales, initial inventory required by the product’s release time and production management in response to changes in the demand (hereafter referred to as production-sales policies). The goal of this paper was to show the importance of considering both supply and demand uncertainties in the determination of the production-sales policy which has been overlooked in the existing literature. More specifically, we test two main hypotheses: (1) ignoring supply and demand uncertainties may lead to potentially incorrect decisions; and, (2) the decision could be different if risk is used as the primary performance measure instead of the commonly used expected (mean) profit. We perform extensive experimentation with a Monte Carlo simulation model of the stochastic supply-restricted new product diffusion and use different statistical procedures, namely, the Welch’s t-test and a nonparametric double-bootstrap method to compare the average and percentiles of the profit for different policies, respectively. The results indicate that the correctness of the two hypotheses depends on the diffusion speed, consumers’ backlogging behaviour, production capacity, price and variable production and inventory costs. The findings also have important implications for managers regarding market entry time, parameter estimation, production strategy and the implementation of the proposed model.     

An integrated approach to inventory and flexible capacity management subject to fixed costs and non-stationary stochastic demand

In a manufacturing system with flexible capacity, inventory management can be coupled with capacity management in order to handle fluctuations in demand more effectively. Typical examples include the effective use of temporary workforce and overtime production. In this paper, we discuss an integrated model for inventory and flexible capacity management under non-stationary stochastic demand with the possibility of positive fixed costs, both for initiating production and for using contingent capacity. We analyze the characteristics of the optimal policies for the integrated problem. We also evaluate the value of utilizing flexible capacity under different settings, which enable us to develop managerial insights.     

TOC-based performance measures and five focusing steps in a job-shop manufacturing environment

Theory of constraints (TOC) views a company as a set of interdependent processes working in harmony to achieve the profit goal of the company as a whole, and thus it emphasizes total system performance over localized measures to guide operational decisions. This paper demonstrates the usefulness of employing TOCbased global performance measures to make operational-decisions (e.g. product mix, continuous improvement, inventory management, production planning and scheduling) to strengthen the internal supply chain in a relatively complex manufacturing environment, i.e. a job shop. An ARENA-based simulation model is presented and a number of scenarios are discussed that provide insights regarding the characteristic features of TOC, such as goals and necessary conditions, performance measures, five-focusing steps for continuous improvement, and drumbuffer-rope scheduling. These insights will assist managers in making important decisions regarding approaches to successful TOC implementations, and will provide academics with a broad range of future research opportunities.     

Risk control for staff planning in e-commerce warehouses

Internet sale supply chains often need to fulfil quickly small orders for many customers. The resulting high demand and planning uncertainties pose new challenges for e-commerce warehouse operations. Here, we develop a decision support tool to assist managers in selecting appropriate risk policies and making staff planning decisions in uncertain conditions. Multistage stochastic modelling has been used to analyse risk optimisation approaches and expected value-based optimisation. Exhaustive numerical and practical validations have been performed to test the tool’s applicability. We demonstrate, using a Dutch e-commerce warehouse, that the multi-period conditional value at risk appears to be most applicable.     

Product portfolio planning with customer-engineering interaction

A critical decision that faces companies across all industrial sectors is the selection of an optimal mix of product attributes to offer in the marketplace, namely product portfolio planning. The product portfolio planning problem has to date generally been considered from a marketing perspective, with the focus being on customer concerns i.e., how alternative sets of product attributes and attribute-level options interact and compete among the target customer segments. From the engineering perspective, the operational implications of product portfolio decisions have been tackled with a primary concern about the cost and complexity of interactions among multiple products in a manufacturing environment with increasing variety. Consideration of the customer and engineering interaction in product portfolio planning is becoming increasingly important, manifested by the efforts observed in many industries to improve the coordination of marketing, design and manufacturing activities across product and process platforms. This paper examines the benefits of integrating customer concerns over product offerings with more engineering implications. To leverage both the customer and engineering concerns, a maximizing shared-surplus model that considers customer preferences, choice probabilities and platform-based product costing, is proposed to address the product portfolio planning problem. A heuristic genetic algorithm procedure is applied to solve the mixed-integer combinatorial optimization problem involved in product portfolio planning. Initial findings from a case study on notebook computer portfolio planning suggests the importance of the research problem, as well as the feasibility and potential of the proposed framework.     

PRODUCTION PLANNING AND CONTROL HIERARCHY USING A GENERIC CONTROLLER

An enhanced decision-making and control hierarchy is introduced for the production planning problem. The proposed decomposition schema first combines the principles of both a temporal decomposition and a product disaggregation to define the production planning hierarchy. The concept of a generic controller is then applied to two levels of hierarchical decision making. To further provide for enhanced decision making and control, both the frequency and the content of coordinative information exchanges among the hierarchical levels have been significantly augmented. An integrated, stochastic decision-making approach will be employed within each implemented generic controller to rigorously address the uncertainties that arc inherent to the production planning problem. The proposed two-level application of the generic controller is then expanded to outline the interactions among additional planning levels as well as other functions comprising a computer-integrated manufacturing system. Finally, methods are proposed for determining the essential planning horizon lengths that are required for each production-planning level to interact with other manufacturing functions.