Exploring the digital supply chain:implications and models for online software distribution

As a discipline, supply chain management (SCM) has traditionally been primarily concerned with the procurement, processing, movement and sale of physical goods. However an important class of products has emerged - digital products - which cannot be described as physical as they do not obey commonly understood physical laws. They do not possess mass or volume, and they require no energy in their manufacture or distribution. With the Internet, they can be distributed at speeds unimaginable in the physical world, and every copy produced is a 100% perfect duplicate of the original version. Furthermore, the ease with which digital products can be replicated has few analogues in the physical world. This paper assesses the effect of non-physicality on one such product – software – in relation to the practice of SCM. It explores the challenges that arise when managing the software supply chain and how practitioners are addressing these challenges. Using a two-pronged exploratory approach that examines the literature around software management as well as direct interviews with software distribution practitioners, a number of key challenges associated with software supply chains are uncovered, along with responses to these challenges. This paper proposes a new model for software supply chains that takes into account the non-physicality of the product being delivered. Central to this model is the replacement of physical flows with flows of intellectual property, the growing importance of innovation over duplication and the increased centrality of the customer in the entire process. Hybrid physical / digital supply chains are discussed and a framework for practitioners concerned with software supply chains is presented.

The impact of supply chain performance measurement systems on dynamic behaviour in supply chains

The amplification of demand variation up a supply chain widely termed ‘the Bullwhip Effect’ is disruptive, costly and something that supply chain management generally seeks to minimise. Originally attributed to poor system design; deficiencies in policies, organisation structure and delays in material and information flow all lead to sub-optimal reorder point calculation. It has since been attributed to exogenous random factors such as: uncertainties in demand, supply and distribution lead time but these causes are not exclusive as academic and operational studies since have shown that orders and/or inventories can exhibit significant variability even if customer demand and lead time are deterministic. This increase in the range of possible causes of dynamic behaviour indicates that our understanding of the phenomenon is far from complete. One possible, yet previously unexplored, factor that may influence dynamic behaviour in supply chains is the application and operation of supply chain performance measures. Organisations monitoring and responding to their adopted key performance metrics will make operational changes and this action may influence the level of dynamics within the supply chain, possibly degrading the performance of the very system they were intended to measure. In order to explore this a plausible abstraction of the operational responses to the Supply Chain Council’s SCOR® (Supply Chain Operations Reference) model was incorporated into a classic Beer Game distribution representation, using the dynamic discrete event simulation software Simul8. During the simulation the five SCOR Supply Chain Performance Attributes: Reliability, Responsiveness, Flexibility, Cost and Utilisation were continuously monitored and compared to established targets. Operational adjustments to the; reorder point, transportation modes and production capacity (where appropriate) for three independent supply chain roles were made and the degree of dynamic behaviour in the Supply Chain measured, using the ratio of the standard deviation of upstream demand relative to the standard deviation of the downstream demand. Factors employed to build the detailed model include: variable retail demand, order transmission, transportation delays, production delays, capacity constraints demand multipliers and demand averaging periods. Five dimensions of supply chain performance were monitored independently in three autonomous supply chain roles and operational settings adjusted accordingly. Uniqueness of this research stems from the application of the five SCOR performance attributes with modelled operational responses in a dynamic discrete event simulation model. This project makes its primary contribution to knowledge by measuring the impact, on supply chain dynamics, of applying a representative performance measurement system.

Combining analytic hierarchy process and goal programming for logistics distribution network design

Logistics distribution network design is one of the major decision problems arising in contemporary supply chain management. The decision involves many quantitative and qualitative factors that may be conflicting in nature. This paper applies an integrated multiple criteria decision making approach to design an optimal distribution network. In the approach, the analytic hierarchy process (AHP) is used first to determine the relative importance weightings or priorities of alternative warehouses with respect to both deliverer oriented and customer oriented criteria. Then, the goal programming (GP) model incorporating the constraints of system, resource, and AHP priority is formulated to select the best set of warehouses without exceeding the limited available resources. In this paper, two commercial packages are used: Expert Choice for determining the AHP priorities of the warehouses, and LINDO for solving the GP model. © 2007 IEEE.

Variational Markov Chain Monte Carlo for Bayesian smoothing of non-linear niffusions

In this paper we develop set of novel Markov chain Monte Carlo algorithms for Bayesian smoothing of partially observed non-linear diffusion processes. The sampling algorithms developed herein use a deterministic approximation to the posterior distribution over paths as the proposal distribution for a mixture of an independence and a random walk sampler. The approximating distribution is sampled by simulating an optimized time-dependent linear diffusion process derived from the recently developed variational Gaussian process approximation method. Flexible blocking strategies are introduced to further improve mixing, and thus the efficiency, of the sampling algorithms. The algorithms are tested on two diffusion processes: one with double-well potential drift and another with SINE drift. The new algorithm's accuracy and efficiency is compared with state-of-the-art hybrid Monte Carlo based path sampling. It is shown that in practical, finite sample, applications the algorithm is accurate except in the presence of large observation errors and low observation densities, which lead to a multi-modal structure in the posterior distribution over paths. More importantly, the variational approximation assisted sampling algorithm outperforms hybrid Monte Carlo in terms of computational efficiency, except when the diffusion process is densely observed with small errors in which case both algorithms are equally efficient.

Supply chain management diffusion among firms in the Republic of Ireland

The changing business environment has sharpened the focus on the need for robust approaches to supply chain management (SCM) and the improvement of supply chain capability and performance. This is particularly the case in Ireland, which has the natural disadvantage of a location peripheral to significant markets and sources of raw materials which results in relatively high transport and distribution costs. Therefore, in order to gain insights into current levels of diffusion of SCM, a survey was conducted among 776 firms in the Republic of Ireland. The empirical results suggest that there is a need for more widespread adoption of SCM among Irish firms. This is particularly the case in relation to the four main elements of SCM excellence reported in this paper. The design of supply chain solutions is a highly skilled, knowledge-intensive and complex activity, reflected in a shift from 'box moving' to the design and implementation of customised supply chain solutions. Education and training needs to be addressed by stimulating the development of industry-relevant logistics and SCM resources and skills.

Logistical framework for last mile relief distribution in humanitarian supply chains:considerations from the field

Recent years large scale natural disasters: (e.g. 2004 Tsunami, 2005 Earthquake in South Asia, 2010 Earthquake in Haiti, 2010 flood in Pakistan, 2011 Earthquake in Japan etc.) have captured international attention and led to the advance of research of disaster management. To cope with these huge impact disasters, the involved stakeholders have to learn how quickly and efficiently the relief organisations are able to respond. After a disaster strikes, it is necessary to get the relief aid to the affected people by the prompt action of relief organisations. This supply chain process has to be very fast and efficient. The purpose of this paper is to define the last mile relief distribution in humanitarian supply chain and develop a logistical framework by identifying the factors that affect this process. Seventeen interviews were conducted with field officers and the data analysed to identify which are the critical factors for last mile relief distribution of disaster relief operation. A framework is presented classifying these factors according to the ability to implement them in an optimisation model of humanitarian logistics.

An Investigation into Outsourcing Practice in Ireland:A New Direction in Logistics and Supply Chain Management

Companies are increasingly focusing on the development of core competencies as an integral part of their overall strategy development and implementation. The corollary of this is that functions regarded as being non-core are being outsourced. This paper investigates the case for and against outsourcing and in addition what is happening in Ireland regards outsourcing. Furthermore to analysis of current literature in the field, an Irish-wide postal and e-mail survey, as well as three case studies revealed many interesting facts. The key findings of the work are manufacturing outsourcing is now the most popular function to be outsourced for both small to medium sized enterprises (SMEs) and large enterprises. Large enterprises (LEs) do not prepare or examine hidden costs more than SMEs, nor do they differ much in relation to the use of consultants. Furthermore, the importance of time spent on preparing or producing contract, and the impact the contract can have on the supplier-buyer relation do not differ significantly. It was found that most companies outsourced within Ireland, which led to further investigation in that area. In relation to logistics outsourcing specifically, this has become very important in the supply chain over the last 20 years as an activity that was traditionally handled by firms as a support function. At that time logistics activities such as warehousing, distribution, transportation and inventory management were given low priority compared with other business functions within the organisation. However, since the customer has become more demanding, the logistics function has now become a source of competitive advantage and there has been a growing emphasis on providing good customer service.

Supply chain integration in the UK bioenergy industry:findings from a pilot study

Interest in bioenergy as a viable alternative to fossil fuels is increasing. This emergent sector is subject to a range of ambitious initiatives promoted by National Governments to generate energy from renewable sources. Transition to energy production from biomass still lacks a feasible infrastructure particularly from a supply chain and business perspective. Supply chain integration has not been studied widely providing a deficit in the literature and in practice. This paper presents results from a pilot study designed to identify attributes that helps optimise such supply chains. To consider this challenge it is important to identify those characteristics that integrate bioenergy supply chains and ascertain if they are distinct from those found in conventional energy models. In general terms the supply chain is defined by upstream at the point of origin of raw materials and downstream at the point of distribution to final customer. It remains to be seen if this is the case for bioenergy supply chains as there is an imbalance between knowledge and practice, even understanding the terminology. The initial pilot study results presented in the paper facilitates understanding the gap between general supply chain knowledge and what is practiced within bioenergy organisations. © 2014 Elsevier Ltd. All rights reserved.

Multiple criteria optimization of contemporary logistics distribution network problems

In the contemporary customer-driven supply chain, maximization of customer service plays an equally important role as minimization of costs for a company to retain and increase its competitiveness. This article develops a multiple-criteria optimization approach, combining the analytic hierarchy process (AHP) and an integer linear programming (ILP) model, to aid the design of an optimal logistics distribution network. The proposed approach outperforms traditional cost-based optimization techniques because it considers both quantitative and qualitative factors and also aims at maximizing the benefits of deliverer and customers. In the approach, the AHP is used to determine the relative importance weightings or priorities of alternative warehouses with respect to some critical customer-oriented criteria. The results of AHP prioritization are utilized as the input of the ILP model, the objective of which is to select the best warehouses at the lowest possible cost. In this article, two commercial packages are used: including Expert Choice and LINDO.

Pseudoperiodic "Living" and/or controlled cationic ring-opening copolymerization of oxetane with tetrahydropyran:microstructure of polymers vs kinetics of chain growth

The "living" and/or controlled cationic ring-opening bulk copolymerization of oxetane (Ox) with tetrahydropyran (THP) (cyclic ether with no homopolymerizability) at 35°C was examined using ethoxymethyl-1 -oxoniacyclohexane hexafluoroantimonate (EMOA) and (BF3 · CH3OH)THP as fast and slow initiator, respectively, yielding living and nonliving polymers with pseudoperiodic sequences (i.e., each pentamethylene oxide fragment inserted into the polymer is flanked by two trimethylene oxide fragments). Good control over number-average molecular weight (Mn up to 150000 g mol-1) with molecular weight distribution (MWD ∼ 1.4-1, 5) broader than predicted by the Poison distribution (MWDs > 1 +1/DPn) was attained using EMOA as initiating system, i.e., C 2H5OCH2Cl with 1.1 equiv of AgSbF6 as a stable catalyst and 1.1 equiv of 2,6-di-tert-butylpyridine used as a non-nucleophilic proton trap. With (BF3 · CH 3OH)THP, a drift of the linear dependence M n(GPC) vs Mn(theory) to lower molecular weight was observed together with the production of cyclic oligomers, ∼3-5% of the Ox consumed in THP against ∼30% in dichloromethane. Structural and kinetics studies highlighted a mechanism of chains growth where the rate of mutual conversion between "strain ACE species" (chain terminated by a tertiary 1-oxoniacyclobutane ion, Al) and "strain-free ACE species" (chain terminated by a tertiary 1-oxoniacyclohexane ion, Tl) depends on the rate at which Ox converts the stable species T1 (kind of "dormant" species) into a living "propagating" center A1 (i.e., k aapp[Ox]). The role of the THP solvent associated with the suspension of irreversible and reversible transfer reactions to polymer, when the polymerization is initiated with EMOA, was predicted by our kinetic considerations. The activation -deactivation pseudoequilibrium coefficient (Qt) was then calculated in a pure theoretical basis. From the measured apparent rate constant of Ox (kOxapp) and THP (kTHPapp = ka(endo)app) consumption, Qt and reactivity ratio (kp/kd, k a(endo)/ka(exo), and ks/ka(endo) were calculated, which then allow the determination of the transition rate constant of elementary step reactions that governs the increase of Mu with conversion. © 2009 American Chemical Society.

Decision support system for vendor managed inventory supply chain:a case study

Vendor-managed inventory (VMI) is a widely used collaborative inventory management policy in which manufacturers manages the inventory of retailers and takes responsibility for making decisions related to the timing and extent of inventory replenishment. VMI partnerships help organisations to reduce demand variability, inventory holding and distribution costs. This study provides empirical evidence that significant economic benefits can be achieved with the use of a genetic algorithm (GA)-based decision support system (DSS) in a VMI supply chain. A two-stage serial supply chain in which retailers and their supplier are operating VMI in an uncertain demand environment is studied. Performance was measured in terms of cost, profit, stockouts and service levels. The results generated from GA-based model were compared to traditional alternatives. The study found that the GA-based approach outperformed traditional methods and its use can be economically justified in small- and medium-sized enterprises (SMEs).

Variational Markov chain Monte Carlo for Bayesian smoothing of non-linear diffusions

In this paper we develop set of novel Markov Chain Monte Carlo algorithms for Bayesian smoothing of partially observed non-linear diffusion processes. The sampling algorithms developed herein use a deterministic approximation to the posterior distribution over paths as the proposal distribution for a mixture of an independence and a random walk sampler. The approximating distribution is sampled by simulating an optimized time-dependent linear diffusion process derived from the recently developed variational Gaussian process approximation method. The novel diffusion bridge proposal derived from the variational approximation allows the use of a flexible blocking strategy that further improves mixing, and thus the efficiency, of the sampling algorithms. The algorithms are tested on two diffusion processes: one with double-well potential drift and another with SINE drift. The new algorithm's accuracy and efficiency is compared with state-of-the-art hybrid Monte Carlo based path sampling. It is shown that in practical, finite sample applications the algorithm is accurate except in the presence of large observation errors and low to a multi-modal structure in the posterior distribution over paths. More importantly, the variational approximation assisted sampling algorithm outperforms hybrid Monte Carlo in terms of computational efficiency, except when the diffusion process is densely observed with small errors in which case both algorithms are equally efficient. © 2011 Springer-Verlag.

A branch and efficiency algorithm for the optimal design of supply chain networks

Supply chain operations directly affect service levels. Decision on amendment of facilities is generally decided based on overall cost, leaving out the efficiency of each unit. Decomposing the supply chain superstructure, efficiency analysis of the facilities (warehouses or distribution centers) that serve customers can be easily implemented. With the proposed algorithm, the selection of a facility is based on service level maximization and not just cost minimization as this analysis filters all the feasible solutions utilizing Data Envelopment Analysis (DEA) technique. Through multiple iterations, solutions are filtered via DEA and only the efficient ones are selected leading to cost minimization. In this work, the problem of optimal supply chain networks design is addressed based on a DEA based algorithm. A Branch and Efficiency (B&E) algorithm is deployed for the solution of this problem. Based on this DEA approach, each solution (potentially installed warehouse, plant etc) is treated as a Decision Making Unit, thus is characterized by inputs and outputs. The algorithm through additional constraints named “efficiency cuts”, selects only efficient solutions providing better objective function values. The applicability of the proposed algorithm is demonstrated through illustrative examples.