Slow-moving Items Research Articles

Prediction of Intermittent Demand Occurrence using Machine Learning

Demand forecasting plays a pivotal role in modern Supply Chain Management (SCM). It is an essential part of inventory planning and management and can be challenging at times. One of the major issues being faced in demand forecasting is insufficient forecast accuracy to predict the expected demand and fluctuation in actual vs. the predicted demand results in fore-casting errors. This problem is further exaggerated with slow-moving and intermittent demand items. Every organization encounters large proportions of items that have small ir-regular demand with long periods of zero demand, which are known as intermittent demand Items. Demand for such items occur sporadically and with considerable fluctuation in the size of the demand. Forecasting of the intermittent demand entails the prediction of demand series that is characterized by the time interval between demand being significantly greater than the unit forecast period. Because of this there are multiple periods of no demand in the intermittent demand time series. The challenge with these products with low irregular demand is that these items need to be stocked and replenished at regular interval irrespective of the demand cycle, thus adding to the cost of holding the inventory. Since the demand is not continuous, Traditional Forecasting models are unable to provide reliable estimate of required inventory level and replenishment point. Forecast errors would resulting in obsolescent stock or unfulfilled demand. The current paper presents a simple yet powerful approach for generating a demand forecasting and replenishment process for such low volume intermittent demand items to come up with a recommendation for dynamic re-order point, thus, improving the inventory performance of these items. Currently, the demand forecast is generally based on past usage patterns. The rise of Artificial Intelligence/Machine Learning (AI/ML) has provided a strong alternative to solve the problem of forecasting Intermittent Demand. The intention is to highlight that machine learning algorithm is more efficient and accurate than traditional forecasting method. As we move forward to industry 4.0, the digital supply chain is considered as the most essential com-ponent of the value chain wherein the inventory size is controlled, and the demand predicted.

OPPORTUNITIES AND CHALLENGES FOR INDIAN RETAIL STORES AND THEIR MARKETING STRATEGIES

Purpose: Recognize consumer needs, seek out new business opportunities, create the best marketing plan, cut costs, and keep an eye on the competition. Because of retailers, consumers have access to a vast array of goods and services from around the globe, and the retail sector boosts employment and the country’s economy. As the studys title suggests, the goal is to look into the Opportunities and Challenges for Indian retail stores and their Marketing strategy. Design/Methodology/Approach: The secondary data for this paper was gathered from a range of sources about the opportunities and challenges of retail stores and their marketing strategies, including research papers, journal articles, and literature reviews. These sources were found by searching for relevant keywords in the Google Scholar search engine, finding relevant websites, and gathering additional information from reports on the opportunities and challenges of retail stores in India. Findings: Price wars among competitors can erode profit margins and negatively impact the industry. Retail stores need to find ways to differentiate themselves beyond just price, such as by offering superior customer service or unique product selections. Retailers can analyse sales data to identify trends, such as which products are selling well and at what times of the year. Understanding the age, gender, and preferences of customers can help tailor marketing and product offerings. Finding out if there is excess stock, understocking, or slow-moving items is crucial for efficient inventory management. Collecting and analysing customer feedback can reveal areas for improvement in customer service or product quality. Originality/Value: A research-based case study on analysis of the opportunities and challenges for Indian retail store and their marketing strategy. Paper Type: Research Case Study KEYWORDS: Opportunities and challenges, Retail store, Profit Margins, Marketing Strategy, Preferences, SWOT Analysis.

Analysis of inventory management of slow-moving spare parts by using ABC techniques and EOQ model-a case study

Computer spare parts (CSPs) inventory management (IM) is very important for many companies. Excess inventory leads to high storage costs. On the other hand, the lack of CSP has a strong impact on the quality of service. This study was conducted by Power-One Jordan Computer Hardware-Software company (POJCHSC), Amman, Jordan. The focus area was the IM department and the target sample was employees working in the management department. The results showed that factors influencing the management of slow moving CSPs include production costs, obsolescence and CSPs dependence availability and transportation costs. By forecasting during the study, the results showed that the demand for adapters and chargers would increase by 20%. This demand forecast was performed using the economical order quantity (EOQ) model. The percentage of profits made by this company is 48%, and this requires some intervention to prevent losses. The results of this study are useful to the company, as well as to other similar industries that deal with slow-moving items. These results will help to simplify IM of slow-moving items. When we focused on POJCHSC manufacturers, the disadvantages of using the traditional ABC classification model were identified. Therefore, there is a need to have an ABC classification that is improved and which takes into consideration the criticality of the slow-moving CSP.

Taming the Long Tail of Inventory: Gambler's Fallacy in Intermittent Demand Management

Items with intermittent or demand often tie up valuable warehouse space and capital investment of manufacturers and retailers. Traditional inventory solutions were not designed for items with these demand characteristics. In the case of long tail products, the paucity of demand data poses additional challenges for model estimation and performance evaluation. Furthermore, while most inventory policies in the OR literature were conceived to maximize long run average cost per time period or expected total cost over a finite horizon, in reality, practitioners are more concerned with KPIs such as inventory turnover ratio or gross margin return on investment, metrics that often normalize the profit or cost performance with the number of sales generated in a finite horizon. The nature of intermittent demand product further accentuates the impact of these effects on the performance of inventory policies under these KPIs. We analyze the average cost per unit of goods sold for inventory policies over a finite horizon with intermittent demand product. Our analysis is motivated by the recent surprising observation that the gambler's fallacy phenomenon holds actually in a random sequence of independent events, due to the finite horizon effect. We use this to analyze the performance of stationary policies in finite horizon, and provide justification for the use of replenishment-delay policies for very slow-moving parts, even if the demand distribution is stationary and independent across time. This is surprising as the constant base stock policy is known to be optimal for items with these demand characteristics in the classical settings. More generally, we propose a class of stationary staggered base stock policy to minimize the average inventory cost per unit of goods sold for slow-moving items over a finite horizon. We call this the HBR (histogram-based robust) policy, obtained using a distributionally robust inventory control model with the Wasserstein uncertainty set on the nominal interarrival-demand distribution. Interestingly, to deal with the finite horizon effect, we need to bias the true interarrival and demand distribution to account for the gambler's fallacy phenomenon. We implement our policy and other benchmark methods on real data and find that our robust policy can reduce inventory cost compared to the benchmarks in all scenarios with varying cost parameters.

Comparison of statistical and machine learning methods for daily SKU demand forecasting

Daily SKU demand forecasting is a challenging task as it usually involves predicting irregular series that are characterized by intermittency and erraticness. This is particularly true when forecasting at low cross-sectional levels, such as at a store or warehouse level, or dealing with slow-moving items. Yet, accurate forecasts are necessary for supporting inventory holding and replenishment decisions. This task is typically addressed by utilizing well-established statistical methods, such as the Croston’s method and its variants. More recently, Machine Learning (ML) methods have been proposed as an alternative to statistical ones, but their superiority remains under question. This paper sheds some light in that direction by comparing the forecasting performance of various ML methods, trained both in a series-by-series and a cross-learning fashion, to that of statistical methods using a large set of real daily SKU demand data. Our results indicate that some ML methods do provide better forecasts, both in terms of accuracy and bias. Cross-learning across multiple SKUs has also proven to be beneficial for some of the ML methods.

An integrated fuzzy approach for classifying slow-moving items

PurposeThe purpose of this paper is to propose a new integrated method for evaluating inventory of slow-moving items by introducing the application of fuzzy AHP method with interval Type-2 fuzzy sets (IT2FSs) and ABC analysis.Design/methodology/approachIn the study, fuzzy analytic hierarchy process (AHP) method with IT2FSs is employed to set the importance of criteria. The weights obtained from IT2 fuzzy AHP are used to classify slow-moving items in ABC analysis. In the application part, a real-life case study is presented.FindingsThe result of this study indicates that an integrated approach utilizing IT2 fuzzy AHP and ABC analysis can be used as a supportive tool for classification of slow-moving items. The problem is solved under fuzzy environment to handle uncertainties and lack of information about slow-moving items.Practical implicationsActual data are provided from an automotive company for prioritizing a various criteria to evaluate and classify stocks and a hypothetical model integrated with IT2 fuzzy AHP and ABC analysis is demonstrated.Originality/valueApart from inventory classification literature, the study integrates fuzzy AHP method by employing interval IT2FSs and ABC analysis to solve the real-life inventory classification problem.

Shortage of psychotropic medications in community pharmacies in Saudi Arabia: Causes and solutions

Background: Patients with mental disorders, such as depression and anxiety, who seek medical care in private psychiatric clinics in Riyadh, Saudi Arabia, have recently expressed concerns to doctors about difficulty in filling psychotropic medications, such as Amitriptyline and Aripiprazole, at retail community pharmacies. Objectives: The aim of this study was to investigate whether there is a shortage of some commonly prescribed psychotropic medications in retail community pharmacies in Saudi Arabia, and if so, to explore the possible reasons behind the shortage of these medications. Methods: The availability of 28 commonly prescribed psychotropic medications was checked in multiple retail community pharmacies in 4 different regions of Saudi Arabia. Further, potential reasons behind the shortage of some psychotropic medications in retail community pharmacies were also explored. Results: Amitriptyline, Amoxapine, Aripiprazole, Bupropion, Buspirone, Duloxetine, Haloperidol, Hydroxyzine, Lithium, Prochlorperazine, Procyclidine, Promethazine, Thioridazine, Trazodone, and Trifluoperazine were unavailable in over half of the 248 community pharmacies surveyed. Four possible reasons behind the shortage of these medications were reported by 31 pharmacists working in different retail community pharmacies’ purchasing departments, with a majority (58.06%) reporting the primary reason for a shortage of these medications that they are slow-moving items with low profit margins. Conclusions: The findings of this study should expedite the reform process in both the Ministry of Health and the Saudi Food and Drug Authority (SFDA) to publish and enforce an essential list of medications for retail community pharmacies, which should include the most commonly prescribed psychotropic medications.

Critical level rationing in inventory systems with continuously distributed demand

This paper analyzes the use of a constant critical level policy for fast-moving items, where rationing is used to provide differentiated service levels to two demand classes (high priority and low priority). The previous work on critical level models, with either a continuous or periodic review policy, has only considered slow-moving items with Poisson demand. In this work, we consider a continuous review (Q, r, C) policy with two demand classes that are modeled through continuous distributions, and the service levels are measured by the probability of satisfying the entire demand of each class during the lead time. We formulate a service level problem as an non-linear problem with chance constraints for which we optimally solve a relaxation obtaining a closed-form solution that can be computed easily. For instances, we tested, computational results show that our solution approach provides good-quality solutions that are on average $$0.3~\%$$0.3% from the optimal solution.

Managing inventory systems of slow-moving items

Slow-moving demand patterns frequently occur with spare parts as well as items in decentralized retail supply chains with large assortments. These patterns are commonly called lumpy since they exhibit comparably high demand variation and a high fraction of zero-demand events. In this paper, we examine two distribution-based approaches to model lumpy demand processes for inventory control: (i) a generalized hurdle negative binomial model, and (ii) a worst-case non-parametric model that is derived using a test-based approach. Considering a base stock inventory policy, we examine a set of lumpy time series from the industry to exemplify the suitability and benefit of the proposed approaches for managing inventory systems of slow-moving items.

Mail-in-rebate coupled with revenue sharing and downward direct discount for supply chain coordination

Use of sales promotion in the short-term to increase sales by revenues and market share for slow-moving items is common in the supply chain, though in such cases performances of chain members are suboptimal. On the contrary, a growing body of literature suggests that a high degree of integration is needed to optimise performances of all supply chain partners simultaneously. To address supply chain integration, when sales promotion is used, we consider a three-echelon supply chain. Demand depends on the selling price by the retailer and individual promotional mechanisms are chosen by channel members. We study game theoretical models to examine impacts of two promotional policies, e.g., mail-in-rebate (MIR) and downward direct discount (DDD) on supply chain coordination. Effectiveness and flexibility of MIR, MIR coupled with revenue sharing (RS) and DDD as coordination contracts are discussed analytically. We show that in decentralised decision making all channel members prefer manufacturers MIR and discard the possibility of distributers' MIR. Although to cut out channel conflict all chain members cooperate actively by complementing one another through contribution to invariant discount that is provided to customers, still, distributors and manufacturers prefer MIR and RS to DDD, whereas retailers prefers the reverse.

Stochastic inventory model with finite production rate and partial backorders

One of the most widespread stochastic inventory models is the classical stochastic continuous review inventory control (Q, R) model. However, there are some restrictive assumptions in deriving this model. One of these assumptions is that the production rate is infinite which has motivated many researchers to develop (Q, R) models with finite production rate. In these models, the randomness of demand is indirectly introduced in the models by assuming that the safety stock is equal to a safety factor multiplied by the standard deviation of lead time demand. In this paper, the classical stochastic continuous review inventory control (Q, R) model is generalised for the case when the production rate is finite and unmet demand is partially backordered. Unlike the models presented in the literature, the probabilistic nature of the demand is reflected directly in the formulation of the proposed model. Furthermore, the optimal lot size and reorder point are determined for this model. Two types of items are investigated; fast-moving and slow-moving items. Also, the effect of the production rate on the optimal solution is studied through numerical examples. The results show that the production rate impacts the inventory and production decisions significantly.

Reducing expiry of slow-moving critical medical items with transshipment

Healthcare providers have to keep high availability for critical medical items that are important to save lives, even if these items have low demand rates. However, keeping high availability can result in high expiry of slow-moving medical items because of their finite lifetimes. Lateral transshipment between inventory locations has been reported to reduce expiry of blood products, which are fast-moving. This paper extends lateral transshipment to slow-moving critical items in a medical system to reduce expiry and proposes a decision rule. Transshipment may take place when demand happens at a location or when a unit expires. The proposed decision rule takes the myopic-best action at each decision epoch by assuming no transshipments in the future. The rule is iteratively applied at all decision epochs to realize long-term savings. Simulation results demonstrate significant cost savings by the lateral transshipment based on the proposed decision rule. The decision rule works well compared to the upper-bound percentage of total savings. The savings are more considerable when the difference of demand rates between locations is large and the lifetime of the medical item is not too long or too short. The savings could be more significant when the number of locations increases but is bounded. Furthermore, simulation results on an example of Fomepizole demonstrate about 9.5% cost savings realized by lateral transshipment based on the proposed decision rule.

A note on lateral transshipment under point-process driven demand

We analyze lateral transshipment for items whose demand is driven by an arbitrary point process. We find conditions under which lateral transshipment stochastically lowers the cost of a system of demand centers. Our main structural result is that transshipment works best for slow-moving items, such as spare-parts, when the demands are driven by non-homogeneous or homogeneous Poisson processes. We also obtain general results that apply when demand is driven by an arbitrary point process.

Optimal production and inventory decisions for supply chains with one producer and multiple newsvendors

This paper considers a supply chain with a single manufacturer supplying a newsvendor-type item to multiple retailers (newsvendors). The manufacturer produces the item in batches at a finite rate and ships the output to the retailers. The retailers then have to satisfy a stochastic demand which follows a general probability distribution. The objective is to view the system as an integrated whole and determine the production and shipment schedule which maximises the expected total profit in a selling period. The concavity of the expected total profit function is established, the global optimal solution is derived, and a simple but efficient algorithm is set out. Two special cases are considered, the first of which is for fast-moving items and the other for slow-moving items.

Service-oriented decisions on inventory levels in the case of incomplete demand information

New products without historical demand information or slow-moving items with little such information cause difficulties in defining inventory management policies facing demand uncertainty. The classical approach using the Normal distribution for describing the random demand during lead time might lead to a degraded level of customer service. But the choice for other types of distributions is also no option, so it is realistic that the full functional form of the distribution is unknown, but the decision-maker has some but not incomplete information on the demand distribution during lead time. As the distribution is only partially specified, several distributions satisfy the known information. Customer service measures therefore also take values in an interval between a lower and an upper bound. In this paper, upper and lower bounds are determined for two performance measures: the number of stock-out units and the stock-out probability per replenishment cycle, given incomplete information about the demand distribution, that is only the first two moments and the range, are known. Based on these results, the optimal inventory level given the desired maximum number of stock-out units or the desired maximum stock-out probability is calculated for the case where only the first two moments are known. The results of our approach are compared to the more traditional approach where a Normal distribution of demand during lead time is assumed. Comparisons with the Gamma, Uniform and symmetric triangular distribution are made. Furthermore, the robustness of our bounds to uncertainty in the parameters is tested.

물류센터의 팔렛합적 문제

In most distribution centers, products are received from the suppliers in units of pallet, stored in a rack, moved to the picking area for replenishment, picked according to customer orders, and shipped to customers. In some distribution centers, however, replenishment is made in not a whole pallet but only a portion of a pallet load, mainly due to the limited space in the order-picking area; as a result, partially loaded pallets occupy cells in the rack. As the number of slow-moving items increases, more cells are occupied by partially loaded pallets so that fewer empty cells are available for storing full, incoming pallets. This will necessitate the construction or leasing of additional storage space, which will entail significant cost. As an alternative, we propose pallet consolidation, which involves moving a partial load to another partially-loaded pallet in order to create one empty cell in the rack. In this paper, we define three pallet consolidation problems; formulate each problem as a binary integer programming model; and present heuristic algorithms for the problems. The average performance of each of the proposed heuristics is evaluated using simulation.

Item dwell time in project inventories: A field experiment

This paper describes the use of item dwell-time measurement in the context of capital investment projects. It contributes to the research on product-centric control and intelligent products by empirically demonstrating the value of item dwell-time alerts in the context of project delivery. The paper shows how item dwell-time measurement can be implemented in the field, and compares item dwell-time alerts to conventional measures of inventory turnover and inventory value in four telecommunications infrastructure projects. We find that when individual items cannot be used interchangeably at project sites, conventional inventory measures do not provide sufficiently timely and accurate information about emerging problems in project inventories. These findings are especially important in situations in which supply chain managers can find alternative uses for materials at other sites or in other projects. Timely reallocation of materials prevents the accumulation of slow-moving items that weigh on project performance by increasing obsolescence and inventory holding costs.

Forecasting demand for slow-moving items in case of reporting errors

The paper considers the problem of demand forecasting for slow-moving items in case of reporting errors. A general- ization of the beta-binomial demand model is proposed that takes into account possible distortions in the learning sample. Prop- erties of the underlying probability distribution are derived. For this new model, algorithms that provide consistent estimators of the model parameters as well as mean square error optimal forecasts when used for historical demand data with reporting errors are developed. An example for slow-moving car parts is given to illustrate the proposed demand forecasting approach.

Pricing Promotional Products Under Upselling

Upselling is offering an additional product to a customer who just made a purchase. Most catalogers and online sellers, in addition to some traditional retailers, use upselling often to clear inventories of slow-moving items. We investigate the pricing and discounting questions for such an item, which we call the promotional product. In our model, an arriving customer may purchase this promotional product or one of the other products that the firm sells. If the customer purchases one of the other products, the promotional product is offered to the customer, possibly with a discount. While deciding whether to offer a discount and, if so, how big a discount to offer, the firm uses the information that the customer has just bought a certain product with a certain price. We investigate how discounting decisions depend on the inventory levels, time, type of pricing policy in use, and the relationship between the customers' reservation prices for the promotional product and the other products (negatively or positively correlated). In particular, we find that if the firm sets prices and discounts dynamically and the customers' reservation prices for the promotional product are negatively correlated with their reservation prices for the product they purchased, then customers are always offered a discount regardless of the inventory levels and time. On the other hand, if the customers' reservation prices for the promotional product are positively correlated with their reservation prices for the product they purchased, then the customer may or may not be offered a discount, depending on the inventory levels and time. Our numerical study shows that the benefit to the firm from using customer purchase information is high when the firm uses a static price, but chooses discounts dynamically. We also find that although dynamic discounting decisions bring modest improvements, setting the price dynamically seems to have a more significant effect on the firm's profits.

Extended beta-binomial model for demand forecasting of multiple slow-moving inventory items

This article considers the problem of modelling the lead-time demand for multiple slow-moving inventory items in the case when the available demand history is very short, and a large percentage of items has only zero records. The Bayesian approach is used to overcome these problems with the past demand data: it is proposed to use the beta-binomial model to predict the lead-time demand probability distribution for each item. Further, an extension of this model is developed which incorporates the prior information regarding the maximum expected probability of demand per period. Parameter estimation and Bayesian forecasting routines are derived for the new model. The performance of the proposed techniques is evaluated by a simulation study.