Beyond LIFO and FIFO: Exploring an Allocation-In-Fraction-Out (AIFO) policy in a two-warehouse inventory model

The classical formulation of a two-warehouse inventory model is often based on the Last-In-First-Out (LIFO) or First-In-First-Out (FIFO) dispatching policy. The LIFO policy relies upon inventory stored in a rented warehouse (RW), with an ample capacity, being consumed first, before depleting inventory of an owned warehouse (OW) that has a limited capacity. Consumption works the other way around for the FIFO policy. In this paper, a new policy entitled “Allocation-In-Fraction-Out (AIFO)” is proposed. Unlike LIFO and FIFO, AIFO implies simultaneous consumption fractions associated with RW and OW. That said, the goods at both warehouses are depleted by the end of the same cycle. This necessitates the introduction of a key performance indicator to trade-off the costs associated with AIFO, LIFO and FIFO. Consequently, three general two-warehouse inventory models for items that are subject to inspection for imperfect quality are developed and compared – each underlying one of the dispatching policies considered. Each sub-replenishment that is delivered to OW and RW incurs a distinct transportation cost and undertakes a 100 per cent screening. The mathematical formulation reflects a diverse range of time-varying forms. The paper provides illustrative examples that analyse the behaviour of deterioration, value of information and perishability in different settings. For perishable products, we demonstrate that LIFO and FIFO may not be the right dispatching policies. Further, relaxing the inherent determinism of the maximum capacity associated with OW, not only produces better results and implies comprehensive learning, but may also suggest outsourcing the inventory holding through vendor managed inventory

Efficient inventory control for imperfect quality items

In this paper, we present a general EOQ model for items that are subject to inspection for imperfect quality. Each lot that is delivered to the sorting facility undertakes a 100 per cent screening and the percentage of defective items per lot reduces according to a learning curve. The generality of the model is viewed as important both from an academic and practitioner perspective. The mathematical formulation considers arbitrary functions of time that allow the decision maker to assess the consequences of a diverse range of strategies by employing a single inventory model. A rigorous methodology is utilised to show that the solution is a unique and global optimal and a general step-by-step solution procedure is presented for continuous intra-cycle periodic review applications. The value of the temperature history and flow time through the supply chain is also used to determine an efficient policy. Furthermore, coordination mechanisms that may affect the supplier and the retailer are explored to improve inventory control at both echelons. The paper provides illustrative examples that demonstrate the application of the theoretical model in different settings and lead to the generation of interesting managerial insights

The value of regulating returns for enhancing the dynamic behaviour of hybrid manufacturing-remanufacturing systems

Several studies have determined that product returns positively impact on the dynamics of hybrid manufacturing-remanufacturing systems, provided that they are perfectly correlated with demand. By considering imperfect correlation, we observe that intrinsic variations of returns may dramatically deteriorate the operational performance of these closed-loop supply chains. To cope with such added complexity, we propose a structure for controlling the reverse flow through the recoverable stock. The developed mechanism, in the form of a prefilter, is designed to leverage the known positive consequences of the deterministic component of the returns and to buffer the harmful impact of their stochastic component. We show that this outperforms both the benchmark push system and a baseline solution consisting of regulating all the returns. Consequently, we demonstrate that the operation of the production system is greatly smoothed and inventory is better managed. By developing a new framework for measuring the dynamics of closed-loop supply chains, we show that a significant reduction in the net stock, manufacturing, and remanufacturing variances can be achieved, which undoubtedly has implications both for stock reduction and production stabilization. Thus, the known benefits of circular economy models are strengthened, both economically and environmentally

The effect of returns volume uncertainty on the dynamic performance of closed-loop supply chains

We investigate the dynamics of a hybrid manufacturing/remanufacturing system (HMRS) by exploring the impact of the average return yield and uncertainty in returns volume. Through modelling and simulation techniques, we measure the long-term variability of end-product inventories and orders issued, given its negative impact on the operational performance of supply chains, as well as the average net stock and the average backlog, in order to consider the key trade-off between service level and holding requirements. In this regard, prior studies have observed that returns may positively impact the dynamic behaviour of the HMRS. We demonstrate that this occurs as long as the intrinsic uncertainty in the volume of returns is low —increasing the return yield results in decreased fluctuations in production, which enhances the operation of the closed-loop system. Interestingly, we observe a U-shaped relationship between the inventory performance and the return yield. However, the dynamics of the supply chain may significantly suffer from returns volume uncertainty through the damaging Bullwhip phenomenon. Under this scenario, the relationship between the average return yield and the intrinsic returns volume variability determines the operational performance of closed-loop supply chains in comparison with traditional (open-loop) systems. In this sense, this research adds to the still very limited literature on the dynamic behaviour of closed-loop supply chains, whose importance is enormously growing in the current production model evolving from a linear to a circular architecture

Reproducibility in forecasting research

The importance of replication has been recognised across many scientific disciplines. Reproducibility is a necessary condition for replicability, because an inability to reproduce results implies that the methods have not been specified sufficiently, thus precluding replication. This paper describes how two independent teams of researchers attempted to reproduce the empirical findings of an important paper, ‘‘Shrinkage estimators of time series seasonal factors and their effect on forecasting accuracy’’ (Miller & Williams, 2003). The two teams proceeded systematically, reporting results both before and after receiving clarifications from the authors of the original study. The teams were able to approximately reproduce each other’s results, but not those of Miller and Williams. These discrepancies led to differences in the conclusions as to the conditions under which seasonal damping outperforms classical decomposition. The paper specifies the forecasting methods employed using a flowchart. It is argued that this approach to method documentation is complementary to the provision of computer code, as it is accessible to a broader audience of forecasting practitioners and researchers. The significance of this research lies not only in its lessons for seasonal forecasting but also, more generally, in its approach to the reproduction of forecasting research

Measuring the sales impact of improving inventory records: How improving the accuracy of inventory records can grow sales by 4-8%

There is a growing body of evidence to suggest that retailers’ inventory records are inaccurate to a significant extent. And it is reasonable to assume that the higher the inventory record inaccuracy (IRI), the higher the impact on sales. But what does this mean in real terms? This report describes the outcome of a 3-year project (conducted with the participation of 7 of Europe’s largest retailers) the aim of which is to quantify the IRI problem and demonstrate the sales lift resulting from fixing it. A structured test-control type experiment is used, according to which test stores are subjected to stock counts at some particular point in time, whereas control stores are not, allowing us to measure the effect of reconciling (or not) the stock records on sales. The analysis covers approximately 1 Million stock keeping units (SKUs) sold in about 100 stores; such data is of a different order of magnitude to anything previously attempted in the academic and practitioner literature, leading to important, reliable and trustworthy conclusions. We find that about 60% of the SKUs analysed are affected by inventory record inaccuracies. We also find that positive IRI is as prevalent as negative IRI, with the same detrimental effects though on sales. Very importantly, correcting inventory inaccuracies is found to lead to approximately 4% to 8% of increased sales in the participating retailers. Interestingly, this applies to all retailers including the particularly ‘accurate’ ones. The results demonstrate that the biggest opportunity for improvement comes from high-volume expensive items, and detailed analysis by product category shows which categories should attract most attention. Finally, we discuss and show results on how inventory accuracy deteriorates over time following a stock count. This has implications for deciding how often and when stocktakes should take place. Our findings should be of great value to retailers to: i) inform their decisions on the appropriate levels of resource and investment against improving inventory records accuracy; ii) prioritise investments per product category and class; iii) appreciate the behaviour of positive and negative discrepancies; iv) discuss counting as a sales increase strategy rather than a cost-intensive necessity

The effects of integrating management judgement into OUT levels: in or out of context?

Physical inventories constitute a significant proportion of companies’ investments in today's competitive environment. The trade-off between customer service levels and inventory reserves is addressed in practice by statistical inventory software solutions; given the tremendous number of Stock Keeping Units (SKUs) that contemporary organizations deal with, such solutions are fully automated. However, empirical evidence suggests that managers habitually judgementally adjust the output of such solutions, such as replenishment orders or re-order levels. This research is concerned with the value being added, or not, when statistically derived inventory related decisions (Order-Up-To, OUT, levels in particular) are judgementally adjusted. We aim at developing our current understanding on the effects of incorporating human judgement into inventory decisions; to our knowledge such effects do not appear to have been studied empirically before and this is the first endeavour to do so. A number of research questions are examined and a simulation experiment is performed, using an extended database of approximately 1,800 SKUs from the electronics industry, in order to evaluate human judgement effects. The linkage between adjustments and their justification is also evaluated; given the apparent lack of comprehensive empirical evidence in this area, including the field of demand forecasting, this is a contribution in its own right. Insights are offered to academics, to facilitate further research in this area, practitioners, to enable more constructive intervention into statistical inventory solutions, and software developers, to consider the interface with human decision makers

Enriching demand forecasts with managerial information to improve inventory replenishment decisions: exploiting judgment and fostering learning

This paper is concerned with analyzing and modelling the effects of judgmental adjustments to replenishment order quantities. Judgmentally adjusting replenishment quantities suggested by specialized (statistical) software packages is the norm in industry. Yet, to date, no studies have attempted to either analytically model this situation or practically characterize its implications in terms of ‘learning’. We consider a newsvendor setting where information available to managers is reflected in the form of a signal that may or may not be correct, and which may or may not be trusted. We show the analytical equivalence of adjusting an order quantity and deriving an entirely new one in light of a necessary update of the estimated demand distribution. Further, we assess the system’s behavior through a simulation experiment on theoretically generated data and we study how to foster learning to efficiently utilize managerial information. Judgmental adjustments are found to be beneficial even when the probability of a correct signal is not known. More generally, some interesting insights emerge into the practice of judgmentally adjusting order quantities

Revisiting the value of information sharing in two-stage supply chains

There is a substantive amount of literature showing that demand information sharing can lead to considerable reduction of the bullwhip effect/inventory costs. The core argument/analysis underlying these results is that the downstream supply-chain member (the retailer) quickly adapts its inventory position to an updated end-customer demand forecast. However, in many real-life situations, retailers adapt slowly rather than quickly to changes in customer demand as they cannot be sure that any change is structural. In this paper, we show that the adaption speed and underlying (unknown) demand process crucially effect the value of information sharing. For the situation with a single upstream supply-chain member (manufacturer) and a single retailer, we consider two demand processes: stationary or random walk. These represent two extremes where a change in customer demand is never or always structural, respectively. The retailer and manufacturer both forecast demand using a moving average, where the manufacturer bases its forecast on retailer demand without information sharing, but on end-customer demand with information sharing. In line with existing results, the value of information turns out to be positive under stationary demand. One contribution, though, is showing that some of the existing papers have overestimated this value by making an unfair comparison. Our most striking and insightful finding is that the value of information is negative when demand follows a random walk and the retailer is slow to react. Slow adaptation is the norm in real-life situations and deserves more attention in future research - exploring when information sharing indeed pays off