Unsold Goods Research Articles

Pemanfaatan Aplikasi Berbasis Android Untuk Meminimasi Produk Return/”Retur” di Industri Retail

The main process carried out by the retail industry is distribution. The distribution flow starts from receiving goods from suppliers by the receiving division, the picking process carried out by the warehouse division, sending goods to retailers carried out by the distribution and delivery division, as well as receiving returned/returned goods (unsold goods) from retailers by the returns division. The problem faced by the retail industry is the high value of returned products identified as inventory. From the results of problem identification using the six sigma method, it was found that the cause of the high inventory of returned products was the late arrival of returned goods from retailers. In general, returned goods can be returned to the supplier/manufacturer if the maximum expiration date is 3 months. If the expiration date is less than 3 months, the item cannot be returned/replaced with a new product. Delays are caused by the system for checking returned goods that are nearing their expiry date, which is still done manually, so there is no system that can warn retail employees to immediately return the returned goods. This problem can be avoided if there is a product identification system that will expire. This identification system is called an early warning system. Early signal system design uses the Structured Analyzed and Design Technique (SADT) design method. The operating system chosen is Android base. The use of the Android base was chosen because it is more effective and efficient for use in retail branches or retail industry outlets. The process of creating this early signal system uses a MySQL database. The application creation process uses PHP and Android Studio. With an early signal system, delays in returning goods by retailers can be minimized.

A new gray optimization model for disposing or re-commercializing unsold goods in reverse logistics networks with a cross-docking center

PurposeThis paper investigates a problem in a reverse logistics (RLs) network to decide whether to dispose of unsold goods in primary stores or re-commercialize them in outlet centers. By deducting the costs associated with each policy from its revenue, this study aims to maximize the profit from managing unsold goods.Design/methodology/approachA new mixed-integer linear programming model has been developed to address the problem, which considers the selling prices of products in primary and secondary stores and the costs of transportation, cross-docking and returning unwanted items. As a result of uncertain nature of the cost and time parameters, gray numbers are used to deal with it. In addition, an innovative uncertain solution approach for gray programming problems is presented that considers objective function satisfaction level as an indicator of optimism.FindingsAccording to the results, higher costs, including transportation, cross-docking and return costs, make sending goods to outlet centers unprofitable and more goods are disposed of in primary stores. Prices in primary and secondary stores heavily influence the number of discarded goods. Higher prices in primary stores result in more disposed of goods, while higher prices in secondary stores result in fewer. As a result of the proposed method, the objective function satisfaction level can be viewed as a measure of optimism.Originality/valueAn integral contribution of this study is developing a new mixed-integer linear programming model for selecting the appropriate goods for re-commercialization and choosing the best outlet center based on the products' price and total profit. Another novelty of the proposed model is considering the matching percentage of boxes with secondary stores’ desired product lists and the probability of returning goods due to non-compliance with delivery dates. Moreover, a new uncertain solution approach is developed to solve mathematical programming problems with gray parameters.

INVENTORY PREDICTION SYSTEM USING THE LEAST SQUARE METHOD AT THE SERBA SERBI ONLINE SHOP TANJUNGPINANG

In this era of Industry 4.0, technology is advancing rapidly, and various applications are emerging to help traders optimize their product procurement. One such application is the forecasting or prediction system. Serba Serbi Online Shop Tanjungpinang is a shop that requires this system to manage its inventory successfully. As the shop offers a wide variety of goods, managing inventory has become challenging. The capital spent on inventory has increased, and unsold goods are piling up in the warehouse. To overcome this issue, the shop needs to calculate the amount of inventory required to reduce the stock build-up in the warehouse. The prediction method used for this purpose is the least square method. This method uses periodic or time-series data, which requires past sales data to predict future sales. Historical sales data is necessary to develop an inventory prediction system. The sales data for a single item from July 2022 to May 2023 was analyzed using the Least Square method. Based on this analysis, the predicted sales for June is 144.64, rounded to 145. The accuracy of the prediction results were tested, and the best accuracy was obtained from the prediction using 3 months of data, with an error rate of 5.47%. The prediction with the highest error rate was obtained from using 9 months of data, with an error rate of 10.65%. On average, the prediction accuracy is 0.02154%, which is considered very good.

Possibilities of Sale Forecasting Textile Products with a Short Life Cycle

Almost 115 million tons of fibers comprising almost 90 million tons of chemical fibers were produced in the world in 2021, which are mainly used for the production of clothing and footwear. A total of 30% of textile and apparel products are never sold, which means extreme waste production. This article points out possibilities of forecasting the sales of clothing in the case of one relatively large online store. Inadequate stocks of textile products in the company lead to losses and overstock leads to the need to sell products at a discount, which is undesirable and not sustainable for the company. Therefore, the aim of this research is to design a forecasting system based on classical methods (with emphasis on seasonality) and its verification in practice. The results were verified directly with the real sale or with results from a model based on a neural network. The problem with textile products is that they have a short life cycle, i.e., the length of the life cycle is approximately half a year, and a high seasonality is also presented. Therefore, the seasonal indices and Holt–Winters methods (multiplication and additional approaches) were used for forecasting products. Ultimately, this model could contribute to reducing the loss of unsold goods and thus reduce the waste of resources and increase the use of goods in other similar companies.

Streamlining multi-commodity inventory allocation and redistribution

To prevent excess unsold goods caused by market fluctuations, retailers can redistribute surplus commodities among their stores to maximise profits. This paper introduces a novel approach to model the common real-world problem of multi-commodity inventory allocation and redistribution. Our unified approach integrates the two problems and seeks to optimise maximum profit. The study encompasses various factors such as inventory capacity, reallocation constraints, vehicle capacity, time windows for pickup and delivery, and a homogeneous fleet of vehicles. We propose two mixed-integer programming paradigms, the integrated and sequential formulations, along with an improved variable neighborhood search (IVNS) algorithm to solve the problem. Computational results demonstrate the effectiveness of the IVNS algorithm, while further analysis highlights the pros and cons of the two formulation paradigms. Notably, the integrated formulation yields superior solutions at the expense of increased computational time.

Grouping Building Material Sales for Management Optimization with the K-Means Algorithm

Sales transactions at the Pedi Building Store are recorded in the daily logbook provided by the store, so that it cannot be immediately known which types of products are selling well or which are not selling well. To overcome this problem, an analysis of sales data is carried out using the K-Means Algorithm. The K-Means algorithm is an algorithm that requires as many input parameters and divides a set of objects into clusters so that the level of similarity between members in one cluster is high, while the level of similarity with members in other clusters is very low. This study aims to determine the types of products that sell well and don't sell well. The data used in this analysis are 100 data samples with 2 clusters formed, namely unsold goods (Cluster 1) and salable goods (Cluster 2). With the analysis of the K-Means Algorithm, it produces 4 data items that are not selling well (Cluster 1), while 96 data items that sell well (Cluster 2). With the results of the analysis of the K-Means Algorithm, a sales application was developed for the Pedi Building Store which is managed by the analysis of the K-Means Algorithm. The application makes it easy for sellers to manage data to form sales processes of salable and less salable goods

Forecasting Brown Sugar Production Using k-NN Minkowski Distance and Z-Score Normalization

The demand for brown sugar products often falls below the level of production, resulting in unsold goods when market demand surpasses the production capacity. This paper addresses the challenge faced by many brown sugar businesses in estimating production yields. Another issue, apart from production uncertainty, is the presence of a dataset with a significant nominal range. The study focuses on a specific brown sugar producing company in Indonesia. To address the production estimation problem, this research proposes the use of k-NN supervised learning as a forecasting method. However, instead of relying solely on k-NN, the study suggests employing z-score normalization to handle the dataset's large nominal range. The production data used for analysis spans from March 2019 to February 2022, comprising 144 weekly records. The dataset is divided into training and testing data, employing an 8:2 split validation ratio. The proposed method consists of several steps, including data normalization using z-score, processing k-NN based on the Minkowski distance, and concluding with the de-normalization process. The results demonstrate the successful implementation of the proposed method in predicting production levels. The evaluation indicates an average error margin of 3.34%, which is below the 5% threshold. The evaluation of predictive data for k-NN with z-score normalization proves effective in forecasting brown sugar production uncertainty and addressing the challenge of a large nominal range.

Tinjauan Atas Prosedur Pengelolaan Persediaan Pada Proshop Klub Golf Bogor Raya

ABSTRACTInventory is one of the important current assets and has great value for the company's operations. This company does not have a lot of inventory, so there can be a shortage of inventory (stock out) and cannot meet customer requests. But if there is too much inventory (overstock) it will cause unsold goods and pile up in storage.The purpose of writing this research is to evaluate the management of merchandise inventory and to find out what constraints exist when managing inventory. Evaluation is carried out to find out whether inventory management has been carried out properly, also to find out whether obstacles in managing inventory can be overcome properly. The compiler made observations at the Bogor Raya Golf Club Proshop located in Bogor.The observation results show that the Bogor Raya Golf Club Proshop has managed its inventory quite well. Inventory management procedures have been carried out properly starting from submitting requests for goods, ordering goods, goods arriving, until the goods are sold. Even though inventory management at the Bogor Raya Golf Club Proshop has been going well, there are still a number of obstacles, such as late arrival of goods, unexpected requests and incompatibility of ordered goods.The results of the evaluation in this observation show that the Bogor Raya Golf Club Proshop must maintain efforts to manage its inventory, so that unwanted things do not occur which will harm the company. By maintaining inventory, the company does not need to incur costs for reordering or storage costs. Then if you see that the problem came from a supplier/vendor error, that way the company must be smart in choosing suppliers.Keywords: Management, Inventory of goods

Product destruction: Exploring unsustainable production-consumption systems and appropriate policy responses

The practice of product destruction, whereby retailers or manufacturers dispose of viable consumer products such as unsold goods or customer returns, is an extreme expression of the linearity of our current production-consumption system. This qualitative exploratory study aims to uncover why companies engage in this highly unsustainable and resource-inefficient behaviour and to explore the potential policy interventions required to effectively address the issue. In-depth interviews were conducted with eleven experts from the textiles and electronics sectors to understand the driving forces behind this practice and provide a bottom-up perspective on appropriate policy interventions. The research identified two distinct sets of factors that contribute to product destruction. Upstream factors influence overall levels of customer returns and unsold stock and primarily include aspects of the retailer's business model, consumer expectations and product design. Downstream factors drive companies to dispose of these products rather than to pursue product life-extension strategies such as repair and reuse. Key downstream factors include economic incentives, profit-margin considerations, liability and brand integrity concerns, the availability of reuse networks and management issues. Consequently, to meaningfully address product destruction, a policy mix is required to simultaneously target upstream and downstream factors and change the behaviour of different actors, from manufacturers and retailers to consumers and reuse organisations.

SISTEM INFORMASI STOK BARANG MENGGUNAKAN METODE CLUSTERING KMEANS (STUDI KASUS RMD STORE)

This inventory information system was created to obtain information on goods that were sold in large quantities and those that were not sold (less desirable) in order to reduce the buildup of unsold goods and minimize losses caused by goods that were not in demand, therefore we made an application of the inventory information system using the method clustering KMeans case study RMD Store using PHP Laravel and MySqli.

Application of system dynamics to reveal the suitable CPFR mode and unsold goods return policy for deteriorating items

With the intensification of global competition and rapid changes in customer needs, most companies focus on rapid customer response. This has caused problems such as shortened product lifecycles, diversified and small volume demands, and shortened lead time order. As a result, suppliers at all levels in a supply chain produce too many finished products, parts, or components, and increase inventory stock. This is an undesirable situation, particularly for deteriorating items. Increased inventory of deteriorating items does not only increase inventory cost, but also the product cost when the items lose function due to long storage time. In order to solve this problem, many scholars have proposed tools to improve supply chain performance, for example, collaborative planning, forecasting and replenishment (CPFR). Therefore, this research applied system dynamics to reveal the suitable CPFR mode for deteriorating items under two types of unsold goods return policies. The first model prohibits buyer to return unsold goods to seller, while the second model allows buyer to return unsold goods to seller. By using CPFR, buyer and seller can share, change and instantly update forecast information, both on the supply and demand sides. The inventory levels can be minimized and the bullwhip effect in supply chain can be resolved by CPFR to create a win-win situation for both seller and buyer. The results show that for deteriorating items, seller receives more profit than buyer in all four CPFR scenarios under the policy that buyer cannot return goods to seller. However, if buyer can return goods to seller, the seller’s profit is less than the buyer’s but the profit gap is not as wide as the previous model. Therefore, to maintain long-term collaborative relationship, seller should allow buyer to return goods. In both models, total profit is the highest when buyer is responsible for order generation in CPFR. This study applied system dynamics to illustrate the impact of different business models on supply chain performance and then to reveal effective strategy for supply chain partners.

MATHEMATICAL MODELING OF THE MARKET OF THREE GOODS IN TERMS OF SUPPLY LAG

The mathematical model of the free market of three goods in the conditions of supply lag in the interval of discrete time with market parameters, demand vector and initial conditions is considered. The results of simulation modeling of the market of 3 goods with the values of parameters and initial conditions are obtained. Graphs of the optimal dynamics of prices of three competing goods, graphs of the dynamics of demand for the three competing goods at the optimal dynamics of prices are analyzed. Graphically presents the dynamics of additional supplies of three competing products, the order of which was made steps earlier in forecasting at time t optimal prices. It was found that during the initial time interval the balances of unsold goods accumulate, the market is overstocked, so at the end of the interval orders for goods are terminated. The dynamics of the optimal allowable total deliveries of goods to the market and the graphs of the dynamics of sales of goods are presented and analyzed. The dynamics of the maximum allowable profit of the seller is obtained.

A Financing Newsvendor Inventory Problem with Supplier Buyback Contract Based on Biased Game

In this paper, we assume that the supplier will buy-back the retailer’s unsold goods when the retailer order more than the market demand in the capital-constrained newsvendor problem, so the retailer and supplier can take on the hazard together. This system forms the biased game through the retailer and supplier making the order quantity, the supply price and buy-back price for their max-profits respectively. Furthermore, we studied the current satiation of the supply chain integration between the suppliers and the retailers. At last, a numerical example was included to illustrate the effect of the supply chain integration.

Il valore della moda fuori moda La gestione e valutazione dell'invenduto in base al ciclo della moda e all'economia circolare

The fashion system has very often worked without taking very much attention to unsold goods and not used raw materials. The product life cycle is usually very short and products lose their value very fast as every season fashion is forward-looking. Discount shops and outlets are the final step where the past seasonal collections are on sale but sometimes it can happen that goods are destroyed in order not to damage the brand value and exclusivity. This behaviour is not very sustainable anymore. Nowadays this situation is changing and a new approach to this issue is arising. Brands have understood how important brand heritage is and they have started to collect and preserve their past. For many companies recycling and upcycling strategies are becoming more and more common and they offer this new product proposal to the market as a new trend to preserve our planet. On the other side, consumers are changing their shopping and consumption habits. Second-hand shops and websites like Vestiare Collective seem to be the evidence that a new culture and way of thinking is arriving.

Assessment of Carbon Footprint for the Textile Sector in France

Global warming represents a major subject on all society levels including governments, economic actors and citizens. The textile industry is often considered a polluting activity. In this project, French textile manufacturers sought to quantify the carbon footprint (CF) of sold clothes and household linen using Life Cycle Assessment in France for the purpose of reducing it to meet the constraints of Paris Agreement by 2050. First, manufacturers calculated the carbon footprint of 17 clothes and household linen products and established alternative scenarios for four production routes. Secondly, they modeled the supply of the upstream sector through different countries. Based on imports of textile products, their calculated CF for one French person reaches 442 kg of CO2eq/year. Means of action to reduce this carbon footprint by a factor of 6 (74 kg of CO2eq/person/year for textiles) are calculated and are the following: installing the most energy-intensive production processes in a country with a low carbon electricity mix, avoiding unsold goods, implementing eco-design approaches and enhancing the value of end-of-life products with reuse or recycling. Therefore, CF for textiles per capita is reduced to 43 kg CO2eq/year which goes beyond the objectives of Paris Agreement and facilitates carbon neutrality in the textile sector. The first priority for reducing the French carbon footprint of clothes and household linen would be to locate textile production in countries with (i) low carbon electricity, (ii) to reduce unsold items, and (iii) to elaborate ecodesign of product including circular economy.

Capacity choice, monetary trade, and the cost of inflation

Both buyers and sellers in decentralized markets make investments ex ante that constrain trading possibilities ex post: buyers decide in advance how much money to carry, while sellers decide how much inventory to stock. We show that the interaction between these choices naturally leads to indeterminacy of equilibrium. When neither inflation nor the scrap value of unsold goods is high, there is a continuum of equilibria in which buyers and sellers coordinate on their choices of money balances and capacity. Furthermore, at low inflation rates, traded quantities are pinned down by the seller’s capacity choice, and inflation therefore has no effect on output and welfare. We also contrast our results with other environments with strategic complementarities and discuss how they depend on the pricing mechanism.

ABC ANALYSIS IN REVERSE LOGISTICS IN THE COMMERCIAL ENTERPRISE

The reverse logistics related to unsold goods in B2B contracts is a current problem for both practitioners and scientists. This work addresses the problem of using ABC analysis in reverse logistics. The analyzes used weekly data from 2014 to 2017 regarding sales and returns of selected assortment groups. The conducted analyzes showed that the dual ABC categorization due to the volume of sales and returns allows the selection of key groups of assortments for a potential enterprise.

Management of e-waste

The sky rocketing growth in various industries were being witnessed due to the rapid advancement in technologies and innovation in a very short duration of time. A large percentage of the population had the capacity to change or replace appliances and gadgets with the new ones launching into the society. This resulted in discarding of the former equipments before reaching it’s end of life duration. Since industries designed and manufactured goods on a large scale, as a result, a lot of manufacturing industries especially those that manufactured electrical and electronics were expelling a lot of waste to the environment. These were the unsold goods and whose market values had dropped due to the newer products taking their place. This not only harmed the disposal grounds but also posed a serious risk to its components like flora, fauna and human beings as well. The paper discussed in brief, about the various steps and procedures that were undertaken to tackle the problem of e-waste management. Countries like Australia had policies implemented to be followed for the sake of waste management. Lastly, the real-life examples of few countries reflecting on how they shed light on issues when it comes to managing their respective wastes along with future predictions and estimations of e-waste.

GAME MODEL FOR ONLINE AND OFFLINE RETAILERS UNDER BUY-ONLINE AND PICK-UP-IN-STORE MODE WITH DELIVERY COST AND RANDOM DEMAND

Online retailers are increasingly adding buy-online and pick-up-in-store (BOPS) modes to order fulfilment. In this paper, we study a system of BOPS by developing a stochastic Nash equilibrium model with incentive compatibility constraints, where the online retailer seeks optimal online sale prices and an optimal delivery schedule in an order cycle, and the offline retailer pursues a maximal rate of sharing the profit owing to the consignment from the online retailer. By an expectation method and optimality conditions, the equilibrium model is first transformed into a system of constrained nonlinear equations. Then, by a case study and sensitivity analysis, the model is validated and the following practical insights are revealed. (I) Our method can reliably provide an equilibrium strategy for the online and offline retailers under BOPS mode, including the optimal online selling price, the optimal delivery schedule, the optimal inventory and the optimal allocation of profits. (II) Different model parameters, such as operational cost, price sensitivity coefficient, cross-sale factor, opportunity loss ratio and loss ratio of unsold goods, generate distinct impacts on the equilibrium solution and the profits of the BOPS system. (III) Optimization of the delivery schedule can generate greater consumer surplus, and makes the offline retailer share less sale profit from the online retailer, even if the total profit of the BOPS system becomes higher. (IV) Inventory subsidy is an indispensable factor to improve the applicability of the game model in BOPS mode.

Modeling variations in price inertia under demand uncertainty

This study employed a model of two-period in which the manufacturer determines a price floor and drafts output of production precedently to the attainment of certainty by demand. In addition, the closer the distance between the minimum price and the price during the high phase of demand, the higher is the degree of price inertia. This model first assumes that the manufacturer sells products to consumers directly and then introduces competing retailers who have the right to return unsold goods at the minimum resale price specified by the manufacturer. By solving for the minimum resale price and production output, the model’s results indicate that vertical market linkages influence the market power–price inertia relation and that asymmetric price transmission could be symbolic of competitive markets. Further, study reveals that the retail price in a highly concentrated retail market might be lower than that in a retail market with fierce competition. In addition, high product durability, low demand uncertainty, low return credit, and long contract duration between upstream and downstream members lead to price inertia rising during economic recessions compared with booms. The relationship between price adjustments and market competition, therefore, suggests that the reasons underlying price inertia should be considered when formulating antitrust and monetary policy.