A large convergent telco, originated from a recent merger of fixed and mobile providers, faced the need to redesign and unify two separate multi-echelon supply chains. The interdependency between various planning levels (procurement, marketing and production) made process integration critical. A robust decision support system was required to tackle this complex environment, characterized by demand uncertainty, fluctuating marketing strategies, powerful suppliers and intricate direct and reverse logistics, with over 200 simultaneous flow types.
The goal was to capture operational synergies, reduce inventory costs and increase the reuse of refurbished equipment, while maintaining superior customer service.
An holistic framework was conceived to connect the multiple decision levels and distinct business areas into a single workflow. This unified planning model was materialized with a decision support system for demand planning, purchasing, production and reverse logistics.
The model leverages both predictive and prescriptive analytics. Upstream, a new forecasting methodology combines historical data with marketing sensibilities. Demand estimations are then a key input for a state-of-the-art stock management approach, designed to meet desired service levels while dealing with multiple time horizons: production and reverse logistics plans are created on a weekly basis, while procurement lead times require a long-term vision.
The project has ultimately generated a fully customized decision support system (DSS). With a user-friendly interface and comprehensive alerts that ensure data integrity, the DSS is a robust reliable tool.
Short processing times allow for exploring multiple scenarios, while decreasing planning expenses by ~50%. Robust plans induce a reduction of ~10% in stock coverage and an increase of ~10% in equipment re-usage rates.
Warehouses are a critical part of a seamless supply chain. The current focus on assigning value-added activities to warehouses, in order to reduce downstream labour costs, asserts that warehouses need to be designed in detail and carefully thought. The significant amount of processes happening simultaneously and the interdependencies between them imply that it is crucial to make accurate decisions when implementing any changes. When studying a process improvement, a layout change or the introduction of a modern technology in the warehouse operation, the critical question that arises is the following: Will this change affect negatively the warehouse’s performance? What are the benefits of having a more balanced operation? A warehouse can only operate at peak efficiency if all its processes are balanced as a whole and are communicating without any major disruptions. The existing discontinuances can either be due to suppliers that are delivering too early or too late, to a bottleneck in the warehouse operation or to the difficulty in finding something specific in the warehouse. Considering the example of an unbalanced reception, if the warehouse’s suppliers are not allocated to the best periods of time, it might lead to the need of having more operators and more buffer space in the reception area. Also, an unbalanced reception could imply that downstream processes, such as sorting or shipping, would need more operators to work at its maximum capacity. Simulating warehouse flow can help determining bottlenecks in the operation and the hidden cost of an unbalanced warehouse operation. Where should products be to improve sorting efficiency? The current demand for more products with shorter lifecycles and for increased differentiation is a challenge to warehouse operations. This evolution leads to more investigations on the placement of the different products in the warehouse, to ensure the reduction of the amount of time spent in picking activities, the most labor-intensive warehouse operation. Simulation is a proven methodology to test different location assignment positions and to verify the impact of changes in the amount of time and resources needed in a warehouse. Can an innovative technology or layout improve warehouse performance? A crucial part of managing a warehouse is the need to improve its performance and to make complex decisions among diverse options on how to achieve the proposed improvements. Could the change of a warehouse’s layout lead to an increase in the congestion between operators? Could the adoption of a new automation process, which would double the overall processing rate of the warehouse, be worthy, without an increase in the reception rate? Would that lead to any savings or would it just make the warehouse more unbalanced? All these different scenarios can be properly simulated in detail, evaluating the trade-off between each measure and weighing the distinct opportunities holistically. Minimising the risk of warehouse changes Usually, investing in warehouse changes requires big investments that can either derive from new equipment, from outsourcing or moving the operation while the change is occurring, from overtime compensation to the workers, among others. In worst case scenarios, these changes can have consequences which may affect the performance of the whole supply chain and put it at risk. It is highly uneconomical and extremely risky to test different scenarios in a real warehouse, enhancing the importance of virtual reality. Simulation presents itself as a new world of dynamism towards decision making and demonstrates an endless growth potential in companies.
With the increasing diversity of the products commercialised by the various industries, such as food retail or manufacturing industry, inventory management has taken a core role for an organization’s success. Only an efficient and accurate inventory management allows a company to reduce its amount of stock – thus minimising the necessary investment and the risk of obsolescence and/or deterioration (spoilage) – without jeopardizing the customer service level. To better clarify the impact of inventory management in these dimensions, it is necessary to explain the diverse relations of cause-consequence in this area, using food retail as an example to illustrate them. It is also fundamental to describe the most present concepts in inventory management, namely, the time intervals used in this area (lead time and review time) and the safety stock. Stock-out and overstocking consequences In general, inventory management tries to find the optimal quantity of stock for an efficient process. If the stock in a store diminishes, there is a greater risk of generating a stock-out, which means the store will have no stock, which leads to a loss of sales. Besides, even if a stock-out doesn’t occur, the product may have a stock level low enough so that there’s lack of visibility in the presentation space. This lack of notoriety can equally induce a loss of sales. On the other hand, if the stock in store increases, for the inverse motives, there’s potential for a sales growth. However, there’s also a greater risk of spoilage, since the higher level of stock in the store creates a higher probability of the products expiring before being bought. These products cause a financial loss to the company since they will no longer be sold. Review and lead time in inventory management Although there are several inventory management methods, all of them have in common the definition of the moments in which stock is ordered, as well as the quantity to be ordered. Related to the moments in which the stock review and order (if one is necessary) are executed, the time interval that measures two consecutive stock reviews is called review time. After placing an order with a supplier, he takes a certain amount of time to deliver the order. This time interval is called lead time. To ensure that the store doesn’t incur in a stock-out but also doesn’t have stock excess, the ideal would be to guarantee that the quantity received in a certain order is equal to the one to be consumed in the period until the arrival of the next order. To achieve it, the store should, in the ordering moment, compare its stock level with the one corresponding to the protection period (review time + lead time), and then order the difference between the two stock levels. The stock level corresponding to the demand in the protection period is called cycle stock. As one can deduce, to know the stock level which will be consumed during a review time and a lead time, it would be necessary to know the demand in this period. Given that this quantity is not known a priori, it is necessary to use demand forecasts to better estimate this value. These forecasts are always associated with a forecast error, this is, the real demand may be lower or higher than the one predicted. If the demand is higher than the forecast, the risk of stock-out is aggravated. To preclude this hypothesis, an extra stock quantity is used in the store to reduce the probability of stock-out due to forecasting errors. This stock is called safety stock. The size of the safety stock depends, naturally, on the forecasting error associated with a certain product. The larger the error, the greater the safety stock necessary to assure a certain customer service level. Other constraints of the replenishment process may lead to higher safety stocks (ex: low supplier service level). So it can be said that the inventory management theme, on the one hand, is structured in a company’s management, since it can have a huge impact on its success potential, and, on the other hand, is covered with a vast complexity, either by the necessity of different data (lead time of upstream processes, review time, presentation stocks, …), as well as by the necessary integration with demand forecasting. Given the clear difficulty of implementing an inventory management method in a company, the big challenge in this area is to be able to improve simultaneously the three key performance indicators: stock level, spoilage and customer service level.
Everyone likes a good deal especially when they could be part of it. In the past years, promotions have been continually growing in sales and consumer behavior influence. However, the demand variability imposed promotes stress in the whole supply chain, especially concerning the expected demand of each product – promotional forecast. Being able to accurately predict promotional demand involves many decisions where typically analytical models delivered superior results and unbiased insights. Yet, there are several challenges placed every time that deserves to be considered regarding this process. Shrinkage vs service level Although promotions face an increasing impact on the consumer, one pivotal fact is the need to have products available on the shelf to the end customer. If too much product is placed for a promotion it could result in shrinkage or negative margins due to product natural life-cycle. Subsequently, this might induce an over-dimensional operation with higher risk and capital invested. As opposite, an out-of-stock situation will impact customer satisfaction and loyalty. For manufacturers, out-of-stock could be even more damaging and lead to a competitive disadvantage with a loss of brand equity and loyalty. As promotional impact increases the more imperative will be to have a balanced situation and by using a more rigorous forecast less stock will be needed to fulfill possible forecast-demand deviation and, consequently, decreasing risk. Promotional factors Predicting promotions is considerably more complex than predicting non-promoted products. The number of factors influencing demand change within promotions and, for similar promotional conditions, the data available is considerably less or non-existent. Marketing campaigns, price, store display, geographic location, brand, gifts, and many other features consistently impact how clients react to promotions. Nowadays, there are several models and methodologies to tackle this problem, however, it is necessary to have the analytical expertise to parametrize and understand the black box that not always deliver the expected outcome. The probability of ending up at an overfitted situation with meaningless results is higher, but when correctly applied is possible to take advantages increasing forecast accuracy and, especially, promotional knowledge. With an advanced understanding of each feature impact, promotional plans have more information to meet the expected results and be in line with organization strategy. Products interaction New products introduction and promotional assortment add even more complexity into this process. With an increasing product diversification, the differences with similar products are narrower and, in many cases, irrelevant for client requirements and satisfaction. This creates an intense network of product interactions where one promotion has an impact on others promoted and non-promoted products. The introduction of new products enhances the density of the problem by requiring a forecast without historical data to support the analysis. On this subject, product attributes and similar products have an important role in order to prepare and get a sustainable forecast. By looking for attributes instead of individual products it is possible to get close to future demand and products interactions - leveraging the available information. Promotional forecasting is a process ready for improvement and there are plenty of options to achieve it. With good processes and technology, organizations can continue to exploit the benefits of promotion without tarnishing it by using inaccurate forecasts. This consequently leads to better promotions plans with better information, which helps further forecast to be more accurate. What follows is a significant cost reduction and a synchronized supply chain. Besides, having a good methodology could be the core point to leverage the business and take advantage over the competition.