CHALLENGE

     Our client, a food retailer company, deals every day with several SKUs (Stock Keeping Units) throughout its warehouses, trucks and stores. Considerable efforts were spent on optimizing inventory levels in the whole distribution system. However, one important factor was often ignored: the choice of the Case Pack.

     The definition of the Case Pack – transportation unit from supplier to distribution center and later to stores – is an underestimated process that may have a large impact across the entire supply chain. An ill-defined quantity leads to additional efforts in the chain and, consequently, it impacts the bottom-line.

SOLUTION

     Several cost components needed to be modeled, in order to properly define the impact in the whole supply chain. Afterwards, the optimal quantity arose from the trade-off of the different costs.

     At a warehouse level, the processing and picking costs were modeled according to the handled boxes specifications as well as the safety stock needed in the warehouses. In addition, at a store level, the inventory and shrinkage costs were further modeled in agreement with the Case Pack quantity.

     Alongside, several product attributes such as seasonality and packaging attributes were also integrated in the developed model.

RESULTS

     In a first moment, we focused on conceiving an appropriate and robust methodology according to the several distinctive SKUs' attributes.

     This methodology translated the developed cost model into an online optimization service enabling a more reliable and effortless process to optimize the Case Pack.

     Today, the main recommendations constitute guidance in the negotiation with suppliers to steer the Case Pack towards the optimum. The robustness and modularity of the developed methodology has enabled the deployment of a service to periodically revise the Case Pack quantities.

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Insights

  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.  

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  Increasing loyalty by executing customer retention and acquisition promotional campaigns has been one of the main marketing objectives for the last decades. While marketing and commercial teams work hard to deliver the best value to customers, the brand-customer relationship is becoming more dynamic and multi-layered with customers having increased competitor awareness and preferring to use proximity stores to cherry-pick shopping baskets.         Whether on retail, banking, or any service companies, the strategical planning of loyalty targeting and promotional activity is becoming a very complex task which, if not built upon solid profitability evidence, quickly turns into a resource-usage and margin-diminisher managerial problem. While the promotional strategy is typically enforced on a top-down matter, the customer response complexity and dynamicity urge a bottom-up approach that manages to ensure one-to-one ROI focused on real customer needs and basket growth opportunities. A broad analytical vision of the customer behavior history and future response predictions are the most valuable assets towards ensuring the promotional value proposition is suited to customers’ needs and ready to drive long-term profitability. Embracing the dynamic customer’s lifecycle       A typical promotional strategy is usually defined over some sort of low-dimension customer lifecycle segmentation, more often than not based on the core RFM variables (Recency, Frequency, and Monetary value). However, both customers and companies are engaging in a multitude of ways, in which different products and channels are tailored to target specific customer needs, such as convenience, large-basket destination stores and online retailing. In this era, the increasing customer price awareness along with the emerging promotional make loyalty a much more dynamic and hard to acquire concept. While RFM models have very valuable predictive power and provide a simple but effective way of segmenting customers, they fail to pick up on the necessary consumer behavior detail to fully optimize the marketing relationship.       One could dive even deeper and find such different behaviors within the same customer, perhaps across different product categories. Multi-product companies typically face very different competitors across their assortment range, and customer retention and churn measurements should also include this diversity dimension. Otherwise, the company may never extract the full potential from its customers and may be losing more money than they even realize. Broad lifecycle segmentation is no longer enough to respond to the evolution of customer loyalty, as companies need to figure out the driving needs for customer shopping and understand their true potential value and, ultimately, profitability. The pursuit of one-to-one ROI      Figuring out the customer level ROI is all about the understanding potential for customer improvement and the relationship between customer investment and expected probability of growth activation. Analyzing customer potential is two-fold. It involves computing detailed customer-need specific lifecycle segmentations and also mining association rules to estimate them for customers with insufficient transactional data. Customer level information is obviously extremely rich on providing information for this value. The next step is to get answers on how to acquire that value potential. Past promotional campaigns and extremely frequent A/B testing should be deployed to accurately analyze the efficiency of each promotional vehicle, communication channel and promoted products on converting customers’ potential value into loyal recurrent transactions. Developing these predictive models enables companies to avoid the most common mistakes of promotional planning: Focusing on quantity instead of quality: Having too many promotions will, at most, maintain customer’s cost perception while eroding most chances of sustainable profitability; Ignoring customer data on the promotional definition: Suppliers are the main source of promotional funding, but the value of such mass promotions are limited regarding the impact of customer growth and acquisition.      When strategic guidelines are spread down from general segmentations, customers may end up getting targeted for promotions they don’t need, on channels they don’t respond to, and with a disproportionate value regarding their potential brand engagement. The only way to avoid this issue is to strategize not on the promotion and channel usage, but on how different customers should be targeted. Before deciding on what to offer to each customer, companies should strategically define: Which segments to invest and disinvest in? What is the target ROI to decide on whether customers should be contacted? What impact will the available campaigns have on customers’ shopping patterns? How much will each customer segment, if targeted, increase market share or profitability?      Then, by compiling all the type of discounts and promotions that may be included in campaigns, companies should be able to look at the expected response for each pair of customer-promotion and figure out the expected customer response in shopping behavior. Overcoming organizational challenges  Effectively planning promotional activity across the company, making sure both customer needs and supplier funding opportunities are being considered, poses three main challenges to large companies: Managing and navigation huge amounts of data Building a robust promotional planning process that brings all stakeholders’ needs and capabilities on board. Ensure the customer-level promotional portfolio is correctly planned instead of having promotional silos that may have cross-promotional effects and thus erode profitability      There is no point in devising the ultimate promotional campaign for the customers if the commercial teams can’t fund it and there is also no point in engaging in non-profitable promotional activities that contribute little towards achieving the targeted customer value proposition. Overcoming these difficulties requires developing all the processes and frameworks that enable all stakeholders to work together during promotional planning stages. Having the ability to plan, control and evaluate mass and customer-specific promotional activities is the only way to ensure marketing investments have sufficient returns and to avoid them turning into a margin-consuming spiral activity. Only then can companies pursue purposeful customer marketing strategies and develop promotional proposals that leverage customer knowledge into sustainable growth.

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  With the increasing diversity of the products commercialised by the various industries. For example, food retail or the 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).            As a conclusion, 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.  

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