Multi-echelon inventory control: an adjusted normal demand model for implementation in practice

This paper presents an approximation model for coordinated control of one-warehouse multiple-retailer inventory systems, where all locations use continuous review (R, nQ) policies. The motivation stems from close collaboration with a supply chain management software company, Syncron International, and one of their customers. A core objective has been to develop an accurate method for determining near-optimal reorder points that can be directly applied to real-life systems. The approach is based on decomposing the complex multi-echelon problem into N?+?1 single-echelon problems, using a near-optimal-induced backorder cost at the central warehouse. Important extensions made compared to earlier work include the addition of procedures to adjust for lead-time variability, and for undershooting the reorder point when customers’ order sizes vary. The result is a flexible model that is computationally and conceptually simple enough to be implemented in practice. A numerical study, including real data from the case company, illustrates that the new model outperforms existing methods in the literature. Compared to the current methods used by the case company, it offers significant improvements in both service-level fulfilment and system-wide inventory holding costs. Implementations of the model into the Syncron software are in progress.     

Average setup cost inventory model: performance and implementation issues

Now that just-in-time (JIT) concepts have become widely accepted and programs to reduce setup costs are becoming common, production managers need guidance in order to adapt to, as well as facilitate, the implementation of JIT goals. An inventory model is presented for the situation in which setup costs are subject to learning curve effects. The performance of this model is analysed and the conditions for its implementation are discussed.     

Design of an integrated supplier selection and multi-echelon distribution inventory model in a built-to-order supply chain environment

The paper deals with the development of an integrated supplier selection and multi-echelon distribution inventory model (MEDIM) for the original equipment manufacturing company in a built-to-order supply chain environment using fuzzy analytical hierarchy process (FAHP) and a genetic algorithm. The main objective is to design the integrated qualitative decision-making of the supplier selection model using FAHP with that of the quantitative mathematical model for the distribution inventory supply chain using a genetic algorithm to the built-to-order environment. The proposed model is validated by considering the case study in a tyre industry in Southern India. The software used for programming is C++ language.     

A utility-driven approach to supplier evaluation and selection: empirical validation of an integrated solution framework

Supplier evaluation and selection (SES) problems have long been studied, leading to the development of a wide range of individual and hybrid models for solving them. However, the lack of widespread diffusion of existing SES models in the industry points to a need for simpler models that can systematically evaluate both qualitative and quantitative attributes of potential suppliers while enhancing the flexibility decision-makers need to account for relevant situational factors. Furthermore, empirical validations of existing models in SES have been few and far between. With a view to addressing these issues, this paper proposes an integrated solution framework that can be used to evaluate both tangible and intangible attributes of potential suppliers. The proposed framework combines three individual methods, namely the fuzzy analytic hierarchy process, fuzzy complex proportional assessment and fuzzy linear programming. The framework is validated through application in a Turkish textile company. The results generated using the proposed framework is compared with the actual historical data collected from the company. Additionally, a feasibility assessment is conducted on the sample supplier selection criteria employed, as well as assessment of the results generated using the proposed model.     

A cash-constrained stochastic inventory model with consumer loans and supplier credits: the case of nanostores in emerging markets

We consider a small traditional retailer that is managing its inventory under strict cash constraints, mainly because typically informal loans are offered to customers. These stores are widely present in emerging markets, and we refer to them as nanostores (also called ‘mom-and-pop stores’). As the suppliers require immediate payments for goods delivered, a nanostore can only replenish products to the level for which it has on-hand cash available. To improve delivery efficiency, a supplier might offer a nanostore credit for its replenishments. However, currently, suppliers are often reluctant to do so as these nanostores quickly go bankrupt or disappear, hence defaulting on all outstanding credits. The objective of this paper is to determine when it is beneficial to offer supplier credits. We propose a multi-period, stochastic inventory model, and numerically compare scenarios with and without supplier credits. Our study shows that even in the presence of this risk, suppliers often have good incentives to provide these credits, even if interest is not incurred. For this to hold, the operations of the retailer should be (a little) profitable in the first place, for which we provide analytical conditions.     

Antecedents of implementation success in closed-loop supply chain: an empirical investigation

Implementation of closed-loop supply chain (CLSC) has gained increased consideration in the last few years owing to an increase in environmental concerns, product returns and scarcity of natural resources. It aids in improving environmental, economic and social performances. The purpose of this study is to examine the impact of CLSC critical success factors (CSFs) on performance outcomes. Firstly, CSFs and performance outcomes are extracted by conducting exploratory factor analysis using SPSS software. Then, the relationships between CLSC CSFs and performance outcomes are empirically tested by Partial Least Squares-Structural Equation Modelling (PLS-SEM) approach, using the data collected from 138 professionals working in remanufacturing, refurbishing and recycling operations in North American manufacturing organisations. Empirical analysis demonstrates that CSFs, ‘environmental concerns’, ‘sustainable production’ and ‘product design and collection’ have a significant positive effect on environmental performance. Results also validate the significant positive effect of CSFs, ‘demand and inventory management’ and ‘raw material prices’ on economic performance. In our knowledge, this is the first study that examines the impact of CLSC CSFs on performance outcomes. The results provide managers in manufacturing organisations with insights on most important CSFs that improve performance.     

Interactive selection model for supplier selection process: an analytical hierarchy process approach

Supplier Selection Process (SSP) becomes increasingly important for most manufacturing firms as it helps to reduce directly cost to the bottom line. The selection process involves the determination of quantitative and qualitative factors so as to select the best possible suppliers. It is essential to identify the relationship with the suppliers in terms of tangible factors. Owing to subjective human judgement in determining the relative importance of those selection factors, a method called Chain of Interaction is proposed to solve the problems associated with the dynamic nature of supply chain management. Focusing on the goodwill of an Analytic Hierarchy Process, an Interactive Selection Model is suggested to systemize the earlier steps, such as the determination of buyer-supplier relationships and formation of selection criteria, before the implementation of the Analytic Hierarchy Process with the help of Multi-Criterion Decision Making software called Expert Choice. The proposed Interactive Selection Model can be applied to supplier selection through the identification of buyer-supplier interactions and the valid data-collection methods. A numerical example is presented and the pros and cons of the model examined to seek further modification.     

Greener supplier selection: state of the art and some empirical evidence

In recent years, an increasing environmental awareness has favoured the emergence of the new green supply chain paradigm; thus, also in the supplier selection problem, green criteria were incorporated. The aim of this paper is twofold. First, a careful scrutiny of the papers appearing in international scientific journals in recent years on the greener supplier selection problem is provided, highlighting utilised methodologies and current issues; second, a verification of the penetration of environmental and green criteria for the supplier selection in corporate practice is performed, using a questionnaire survey targeting the top 100 manufacturing companies operating in South Yorkshire (UK) and two in-depth interviews at large MNE firms operating in complex industries. Results show that, while interest in the literature is growing, there is little empirical evidence of the transfer of these applications into the real world, highlighting a persistent dichotomy between theory and practice. The reasons for this dichotomy are also investigated.     

Quality management implementation across different scenarios of competitive structure: an empirical investigation

This study aims to determine if there are differences in quality management (QM) implementation across competitive environments and if so, how and why they differ. With these objectives, we develop several propositions relating the competitive environment to QM implementation practices. To test our propositions, we gathered a sample of 273 European managers. We presented 16 different factors of competitive structure to members of the sample and elicited their likelihood of implementing different QM practices. The research findings indicated that high buyer and customer power were associated with a preference for cost reducing-oriented QM practices. We also found that the managers are more likely to try to boost profitability by focusing on QM practices aimed at differentiation when intensity of competitive rivalry or threat of new entrants is high. We conclude with a discussion of theoretical and managerial implications of our results.     

On the inventory performance of multi-criteria classification methods: empirical investigation

A number of multi-criteria inventory classification (MCIC) methods have been proposed in the academic literature. However, most of this literature focuses on the development and the comparison of ranking methods of stock keeping units (SKUs) in an inventory system without any interest in the original and most important goal of this exercise which is the combined service-cost inventory performance. Moreover, to the best of our knowledge these MCIC methods have never been compared in an empirical study. Such an investigation constitutes the objective of this paper. We first present the inventory performance evaluation method that we illustrate based on an example commonly used in the relevant literature which consists of 47 SKUs. Then, we present the empirical investigation that is conducted by means of a large data-set consisting of more than 9086 SKUs and coming from a retailer in the Netherlands that sells do-it-yourself products. The results of the empirical investigation show that the MCIC methods that impose a descending ranking of the criteria, with a dominance of the annual dollar usage and the unit cost criteria, have the lowest combined cost-service performance efficiency.     

Cumulative manufacturing capabilities: an extended model and new empirical evidence

There has been intense debate in the manufacturing strategy literature on the way in which firms work on different manufacturing capabilities, with two opposing approaches considered – the trade-off model and the sand cone model. Analysis of these models has essentially been based on study of the links amongst four classic manufacturing capabilities (quality, delivery, flexibility, and cost efficiency) and has obviated the need to consider environmental protection as an important manufacturing capability. This study analyses the theoretical arguments and the prior empirical evidence on the two models, and proposes and tests an extended sand cone model which includes the environmental protection objective alongside the four traditional ones. The research uses structural equation modelling and data from a sample of 274 manufacturers to contribute additional empirical evidence on the existence of cumulative effects amongst manufacturing capabilities. It is observed that the predominant strategic model in these firms is one of multiple, non-incompatible capabilities with cumulative effects according to the following sequence: quality, delivery, flexibility, environmental protection, and cost efficiency.     

Optimal implementation and benefits of rolling inventory

We study a warehouse management problem in which the schedule of incoming supplies and customer orders for a wide variety of products is known over a number of periods. In addition to storage at the warehouse, products can be kept in the shipping trailers (rolling inventory) parked in the warehouse yard, avoiding material handling costs but incurring trailer handling and opportunity costs. Our objective is to determine the amount of product (if any) to leave in each of the incoming trailers, so that it does not have to be stored and then reloaded for an outgoing delivery, in order to minimize overall warehousing costs. We propose three possible implementation policies and show that the search for optimal solutions can be restricted to these three basic policies without loss of generality. Using this result, we formulate the problem as an integer program, in which incoming trailers are assigned to outgoing deliveries. Under one of the proposed policies incoming trailers can only be stored in the yard directly upon arrival, with their original contents. In this case, we show that our formulation possesses the integrality property and thus the optimal solution can be easily obtained. When the three policies are considered jointly, however, this is no longer the case. Nevertheless, computational tests show that the linear programming bound is very strong and commercial integer programming solvers generate an optimal solution very quickly. In most cases, no branch-and-bound nodes are required. Finally, we perform a computational study based on realistic data provided by our industry partner to evaluate the benefits of rolling inventory, the effectiveness of the different implementation policies and the viability of our proposed solution approaches.     

Antecedents of effective performance measurement system implementation: an empirical study of UK manufacturing firms

This paper examines the antecedents to effective performance measurement system (PMS) implementation. There have been only a few studies which have investigated this topic, and to-date there is no consensus about what factors are significant. Using empirical data collected from 349 UK manufacturing firms, we identify six potential factors from an item pool derived from an extensive literature review. Subsequent regression analysis reveals that all six factors have a significant influence on PMS implementation effectiveness. We characterise these factors into two sub-sets, with the technical sub-set accounting for most of the variance in the dependent variable. We also demonstrate that ‘softer’ social factors additionally have a significant, albeit comparatively small, effect. These, we argue, are essential in order to bind an organisation’s approach to performance measurement into one cohesive whole. Our findings provide managers with a clearer view of the factors which may influence the effectiveness of their PMS implementation. Moreover, we anticipate that the findings should provide empirical stimulation of an important debate about how to ensure that organisations can implement PMSs that are adequate for their purposes. As such, while the findings have current utility, their additional value is in providing a platform for subsequent research.     

Dynamic re-order point inventory control with lead-time uncertainty: analysis and empirical investigation

A new forecast-based dynamic inventory control approach is discussed in this paper. In this approach, forecasts and forecast uncertainties are assumed to be exogenous data known in advance at each period over a fixed horizon. The control parameters are derived by using a sequential procedure. The merits of this approach as compared to the classical one are presented. We focus on a single-stage and single-item inventory system with non-stationary demand and lead-time uncertainty. A dynamic re-order point control policy is analysed based on the new approach and its parameters are determined for a given target cycle service level (CSL). The performance of this policy is assessed by means of empirical experimentation on a large demand data set from the pharmaceutical industry. The empirical results demonstrate the benefits arising from using such a policy and allow insights to be gained into other pertinent managerial issues.     

The analysis of an inventory control model using posynomial geometric programming

We consider a generalization of the well-known ‘sawtooth’ inventory control model which is used to determine optimal policies for stocking a single item under certain conditions. The case where several items are to be stocked simultaneously and subject to a number of constraints give rise to the generalization that is of interest. We show that the problem of analysing the model may he viewed as a problem in posynomial Geometric Programming. We then point out some of the advantages to be realized from viewing it this way and from using Geometric Programming techniques to solve it. These include, the ability to obtain feasible solutions with known upper bounds on the difference between their associated costs and the minimum cost, a capability for performing an analysis of the sensitivity of the values of the optimized parameters to variations in input parameters, availability of a solution method that is computationally more attractive than methods previously known and, arising out of this last benefit, an ability to deal with much more complicated problems to which the model is applied than previously seemed practical. A simple example problem is solved in detail to illustrate many of these benefits and results are presented for a much more complex case. In addition some possible extensions of the model, which can also be dealt with in this framework, are pointed out.     

Highway accident severities and the mixed logit model: an exploratory empirical analysis

Many transportation agencies use accident frequencies, and statistical models of accidents frequencies, as a basis for prioritizing highway safety improvements. However, the use of accident severities in safety programming has been often been limited to the locational assessment of accident fatalities, with little or no emphasis being placed on the full severity distribution of accidents (property damage only, possible injury, injury)-which is needed to fully assess the benefits of competing safety-improvement projects. In this paper we demonstrate a modeling approach that can be used to better understand the injury-severity distributions of accidents on highway segments, and the effect that traffic, highway and weather characteristics have on these distributions. The approach we use allows for the possibility that estimated model parameters can vary randomly across roadway segments to account for unobserved effects potentially relating to roadway characteristics, environmental factors, and driver behavior. Using highway-injury data from Washington State, a mixed (random parameters) logit model is estimated. Estimation findings indicate that volume-related variables such as average daily traffic per lane, average daily truck traffic, truck percentage, interchanges per mile and weather effects such as snowfall are best modeled as random-parameters-while roadway characteristics such as the number of horizontal curves, number of grade breaks per mile and pavement friction are best modeled as fixed parameters. Our results show that the mixed logit model has considerable promise as a methodological tool in highway safety programming.     

Consignment stock inventory policy: methodological framework and model

We consider the hierarchical coordinated control of a multi-procedure conveyor-serviced production station system with flexible stations deployed between adjacent procedures, which includes a dynamic intra-procedure switching control of the flexible stations for the goal of balancing different procedures and a dynamic inter-procedure production coordination of all of the stations within each procedure. It is complicated in terms of modelling and optimisation, and thus, it is difficult to find a solution using numerical methods; as a result, we refer to model-free learning optimisation methods. First, we establish a neuro-dynamic programming algorithm by utilising cerebellar model articulation controllers (CMACs) to approximate state-action values at an upper hierarchy. Second, according to the reaction-diffusion phenomenon, we combine a Wolf-PHC algorithm with a local information-interaction scheme to learn look-ahead control policies at the lower hierarchy. Simulation results show that, compared with traditional Q-learning and the backward Q-learning based Q-learning, our proposed CMAC-based learning optimisation methods have the advantages of yielding a higher processing rate and having a faster optimisation speed with a lower storage requirement.     

The job characteristic inventory: an examination.

Psychometric properties of the Sims, Szilagyi, and Keller (1976) Job Characteristic Inventory (JCI) were evaluated. In particular, internal consistency reliability of JCI subscales, convergent and discriminant validity as assessed by use of a multitrait-multirater (superior and subordinate) matrix, relationships of subscale scores to measures of social desirability of responses and of acquiescence tendencies, and substantive validity were considered. The JCI was seen to be largely independent of social desirability and acquiescence tendencies. However, the results suggest some overlap between subscales, little convergence between superior and subordinate ratings of the subordinate's job, and that substantive validity is in question.     

Setup cost reduction in an inventory model with finite-range stochastic lead times

Traditionally, upon solution, independent demand inventory models result in the determination of a closed form for the economic lot size. Generally, this is obtained from the result that holding costs and setup costs are constant and equal at the optimum. However, the experience of the Japanese indicates that this need not be the case. Specifically, setup cost may be reduced by investing in reduced setup times resulting in smaller lot sizes and increased flexibility. Various authors have investigated the impact of such investment on classical lot sizing formulas which has resulted in the derivation of modified relationships. A common assumption of this research has been that demand and lead time are deterministic. This paper extends this previous work by considering the more realistic case of investing in decreasing setup costs where lead time is stochastic. Closed form relationships for optimal lot size, optimal setup cost, optimal total cost, etc. are derived. Numerical results are presented for cases where lead times follow uniform and normal distributions. Sensitivity analysis is performed to indicate under what conditions investment is warranted.