National Drug Stockout Risks in Africa: Analysis of the Global Fund Disbursement Process for Procurement from 2002 to 2013​

Despite substantial financial aid from international donors for procurement of health products, stockouts of life-saving drugs related to prevalent infectious diseases are still widespread in Africa. Addressing the lack of research on why these stockouts occur, we study the relationship between The Global Fund to Fight AIDS, Tuberculosis and Malaria and its grant recipients. Specifically, we leverage extensive historical fund disbursement and drug procurement data to build a discrete-event simulation model predicting the joint impact of procurement and grant disbursement processes on national drug availability for the Global Fund’s recipient countries in Africa. This model is validated against cumulative stockout levels inferred from historical grant implementation lengths, and used to evaluate potential high-level modifications of disbursement or procurement processes. Results show the existence of substantial intrinsic stockout risks in many countries, due to the unpredictability of fund disbursements and the frequency of grant performance monitoring performed by the Global Fund. Interventions increasing fund disbursement levels to protect against disbursement timing uncertainty are predicted to be more effective than others that include regional buffer stocks and bridge financing.http://deepblue.lib.umich.edu/bitstream/2027.42/107445/1/1241_Yadav.pd

Proactive inventory policy intervention to mitigate risk within cooperative supply chains

This exploratory paper will investigate the concept of supply chain risk management involving supplier monitoring within a cooperative supply chain. Inventory levels and stockouts are the key metrics. Key to this concept is the assumptions that (1) out-of-control supplier situations are causal triggers for downstream supply chain disruptions, (2) these triggers can potentially be predicted using statistical process monitoring tools, and (3) carrying excess inventory only when needed is preferable as opposed to carrying excess inventory on a continual basis. Simulation experimentation will be used to explore several supplier monitoring strategies based on statistical runs tests, specifically "runs up and down" and/or "runs above and below" tests. The sensitivity of these tests in detecting non-random supplier behavior will be explored and their performance will be investigated relative to stock-outs and inventory levels. Finally, the effects of production capacity and yield rate will be examined. Results indicate out-of-control supplier signals can be detected beforehand and stock-outs can be significantly reduced by dynamically adjusting inventory levels. The largest benefit occurs when both runs tests are used together and when the supplier has sufficient production capacity to respond to downstream demand (i.e., safety stock) increases. When supplier capacity is limited, the highest benefit is achieved when yield rates are high and, thus, yield loss does not increase supplier production requirements beyond its available capacity

Grocery omnichannel perishable inventories: performance measures and influencing factors

Purpose- Perishable inventory management for the grocery sector has become more challenging with extended omnichannel activities and emerging consumer expectations. This paper aims to identify and formalize key performance measures of omnichannel perishable inventory management (OCPI) and explore the influence of operational and market-related factors on these measures. Design/methodology/approach- The inductive approach of this research synthesizes three performance measures (product waste, lost sales and freshness) and four influencing factors (channel effect, demand variability, product perishability and shelf life visibility) for OCPI, through industry investigation, expert interviews and a systematic literature review. Treating OCPI as a complex adaptive system and considering its transaction costs, this paper formalizes the OCPI performance measures and their influencing factors in two statements and four propositions, which are then tested through numerical analysis with simulation. Findings- Product waste, lost sales and freshness are identified as distinctive OCPI performance measures, which are influenced by product perishability, shelf life visibility, demand variability and channel effects. The OCPI sensitivity to those influencing factors is diverse, whereas those factors are found to moderate each other's effects. Practical implications- To manage perishables more effectively, with less waste and lost sales for the business and fresher products for the consumer, omnichannel firms need to consider store and online channel requirements and strive to reduce demand variability, extend product shelf life and facilitate item-level shelf life visibility. While flexible logistics capacity and dynamic pricing can mitigate demand variability, the product shelf life extension needs modifications in product design, production, or storage conditions. OCPI executives can also increase the product shelf life visibility through advanced stock monitoring/tracking technologies (e.g. smart tags or more comprehensive barcodes), particularly for the online channel which demands fresher products. Originality/value- This paper provides a novel theoretical view on perishables in omnichannel systems. It specifies the OCPI performance, beyond typical inventory policies for cost minimization, while discussing its sensitivity to operations and market factors

Dynamic Modeling and Analysis for Supply Chain

The objective of this study is to use system dynamics methodology to model the supply chain system and then present the optimal control to optimize the performance of supply chain by minimize the quadratic cost function while tracking and keeping the inventory close to target level. Under the system dynamics point of view, the supply chain was modeled as the continuous differential equation with lead time delay modeled as the first order delay model. In contrast to the frequency domain analysis of the classical control approach, the proposed control utilizes the time-domain state space representation with a set of input, output and state variables to build the dynamic system. On the other hand, by using the system dynamics it allows us to apply different control laws and analyze the dynamic behavior of system so that the decision policies can be found to improve the performance of supply chain. In this paper we employ the linear quadratic optimal control for such kind of supply chain dynamic system, the aim of controller is to find the control input as the order quantity to minimize the cost function and keep high customer satisfaction by tracking the target inventory level. Finally, the numerical simulation results are carried out in Matlab/Simulink environment and the performance of optimal controller will be compared with some classical control policies such as proportional and order-up-to level control policy. It is shown that our approach can obtain some good performances.Chapter 1. Introduction 1 1.1 Background 1 1.2 Motivation of this research 3 1.3 Research objectives 5 Chapter 2. Supply chain management and performance measurements 7 2.1 Supply chain management 7 2.2 Structure of supply chain management 10 2.3 Performance measurement of supply chain management 13 2.4 Process in supply chain management 15 Chapter 3. Dynamic modeling of supply chain 21 3.1 Production model 21 3.2 Transportation model 27 3.3 Distribution model 29 3.4 State-space of supply chain model 30 3.5 Costs function of supply chain 31 Chapter 4. Controller design 33 4.1 State-space model 33 4.2 Linear Quadratic Regular control design 33 4.3 Optimal tracking controller 35 Chapter 5. Simulation results 37 5.1 Demand and control parameters 37 5.2 Simulation results and analysis 38 Chapter 6. Conclusion 44 References 4

Modeling and analyzing a health care supply chain system

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.A Thesis presented on Improving and Redefining the Supply Chain in Healthcare, defining existing problems with current Supply Chain applications, and reviewing current applications and trends in the Supply Chain culture within the Manufacturing Industries and Healthcare Industries. Research of successful applications of new, and improvements to existing supply chain methodologies are presented. The concept of a future Supply Chain Management System, extending the boundaries of conventional Healthcare Supply Chain to include both conventional customer (Healthcare Materiel and Capital Assets Requestors and Distributors) and the nonconventional (the Healthcare Beneficiary), is presented in detail. Final discussions and conclusions of this concept are offered as a review of this concept for the reader of this manuscript

Numerical methods for queues with shared service

A queueing system is a mathematical abstraction of a situation where elements, called customers, arrive in a system and wait until they receive some kind of service. Queueing systems are omnipresent in real life. Prime examples include people waiting at a counter to be served, airplanes waiting to take off, traffic jams during rush hour etc. Queueing theory is the mathematical study of queueing phenomena. As often neither the arrival instants of the customers nor their service times are known in advance, queueing theory most often assumes that these processes are random variables. The queueing process itself is then a stochastic process and most often also a Markov process, provided a proper description of the state of the queueing process is introduced. This dissertation investigates numerical methods for a particular type of Markovian queueing systems, namely queueing systems with shared service. These queueing systems differ from traditional queueing systems in that there is simultaneous service of the head-of-line customers of all queues and in that there is no service if there are no customers in one of the queues. The absence of service whenever one of the queues is empty yields particular dynamics which are not found in traditional queueing systems. These queueing systems with shared service are not only beautiful mathematical objects in their own right, but are also motivated by an extensive range of applications. The original motivation for studying queueing systems with shared service came from a particular process in inventory management called kitting. A kitting process collects the necessary parts for an end product in a box prior to sending it to the assembly area. The parts and their inventories being the customers and queues, we get ``shared service'' as kitting cannot proceed if some parts are absent. Still in the area of inventory management, the decoupling inventory of a hybrid make-to-stock/make-to-order system exhibits shared service. The production process prior to the decoupling inventory is make-to-stock and driven by demand forecasts. In contrast, the production process after the decoupling inventory is make-to-order and driven by actual demand as items from the decoupling inventory are customised according to customer specifications. At the decoupling point, the decoupling inventory is complemented with a queue of outstanding orders. As customisation only starts when the decoupling inventory is nonempty and there is at least one order, there is again shared service. Moving to applications in telecommunications, shared service applies to energy harvesting sensor nodes. Such a sensor node scavenges energy from its environment to meet its energy expenditure or to prolong its lifetime. A rechargeable battery operates very much like a queue, customers being discretised as chunks of energy. As a sensor node requires both sensed data and energy for transmission, shared service can again be identified. In the Markovian framework, "solving" a queueing system corresponds to finding the steady-state solution of the Markov process that describes the queueing system at hand. Indeed, most performance measures of interest of the queueing system can be expressed in terms of the steady-state solution of the underlying Markov process. For a finite ergodic Markov process, the steady-state solution is the unique solution of $N-1$ balance equations complemented with the normalisation condition, $N$ being the size of the state space. For the queueing systems with shared service, the size of the state space of the Markov processes grows exponentially with the number of queues involved. Hence, even if only a moderate number of queues are considered, the size of the state space is huge. This is the state-space explosion problem. As direct solution methods for such Markov processes are computationally infeasible, this dissertation aims at exploiting structural properties of the Markov processes, as to speed up computation of the steady-state solution. The first property that can be exploited is sparsity of the generator matrix of the Markov process. Indeed, the number of events that can occur in any state --- or equivalently, the number of transitions to other states --- is far smaller than the size of the state space. This means that the generator matrix of the Markov process is mainly filled with zeroes. Iterative methods for sparse linear systems --- in particular the Krylov subspace solver GMRES --- were found to be computationally efficient for studying kitting processes only if the number of queues is limited. For more queues (or a larger state space), the methods cannot calculate the steady-state performance measures sufficiently fast. The applications related to the decoupling inventory and the energy harvesting sensor node involve only two queues. In this case, the generator matrix exhibits a homogene block-tridiagonal structure. Such Markov processes can be solved efficiently by means of matrix-geometric methods, both in the case that the process has finite size and --- even more efficiently --- in the case that it has an infinite size and a finite block size. Neither of the former exact solution methods allows for investigating systems with many queues. Therefore we developed an approximate numerical solution method, based on Maclaurin series expansions. Rather than focussing on structural properties of the Markov process for any parameter setting, the series expansion technique exploits structural properties of the Markov process when some parameter is sent to zero. For the queues with shared exponential service and the service rate sent to zero, the resulting process has a single absorbing state and the states can be ordered such that the generator matrix is upper-diagonal. In this case, the solution at zero is trivial and the calculation of the higher order terms in the series expansion around zero has a computational complexity proportional to the size of the state space. This is a case of regular perturbation of the parameter and contrasts to singular perturbation which is applied when the service times of the kitting process are phase-type distributed. For singular perturbation, the Markov process has no unique steady-state solution when the parameter is sent to zero. However, similar techniques still apply, albeit at a higher computational cost. Finally we note that the numerical series expansion technique is not limited to evaluating queues with shared service. Resembling shared queueing systems in that a Markov process with multidimensional state space is considered, it is shown that the regular series expansion technique can be applied on an epidemic model for opinion propagation in a social network. Interestingly, we find that the series expansion technique complements the usual fluid approach of the epidemic literature

Inventory Management Practices and Business Performance for Small-Scale Enterprises in Kenya

Small-Scale Enterprises (SSEs) are acknowledged as significant contributors to economic growth through their perceived critical role in providing job opportunities, poverty reduction and their acting as intermediaries in trade. However, the International Labor Organization (2010) estimates that two-thirds of the enterprises generate incomes equal to or below the minimum wage, a sobering finding that must temper one’s enthusiasm for the growth of SSE’s as a solution to the country’s poverty and employment problems. Inventory constitutes much of the working capital held by SSEs and poor working capital management has been identified as one of the major causes of SSE failures. With this backdrop, this study investigated the relationship between inventory management practices and the business performance of SSEs in Kisii Municipality, Kenya. The relationship was probed based on primary data gathered by use of a structured questionnaire from 70 SSEs. The empirical results revealed a positive significant relationship between business performance and effective inventory management practices at 0.05 significance level. Further, they showed that inventory budgeting had the largest effect on business performance with a beta coefficient of 0.329, followed by shelf-space management with a beta coefficient of 0.30. Inventory level management had the least but significant effect with a beta coefficient of 0.297. The study suggests that owners/managers of SSEs embrace effective inventory management practices as a tactic to further their business performance.Keywords: Inventory Management practices, Business Performance, Small Scale Enterprise