Abstract
This paper attempts to track key performance indicators (KPIs) in order to figure out the performance of the Supply Chain in the retail sector. It also focuses on inventory replenishment strategies and capacity utilization in the retail sector. In recent years, this sector has spent considerable amount of time and money trying to improve its operations in such a way so as to respond efficiently to customers’ needs. This has led to several developments like the introduction of automated store ordering, usage of RFID and etc.
The KPIs helps in directly analyzing the performance of every specific activity and operation and hence also helps in zeroing down to the exact root of the problem, if any, and thus helps the managers to rectify them. The Improvement Opportunities are further explained in detail for achieving a better performance.
The Key Performance Indicators (KPIs)
The KPIs are segregated into different categories accordingly as follows:
Supply Chain and Logistics: The network of retailers, distributors, transporters, storage facilities and suppliers that participate in the sale, delivery and production of a particular product.
• % of time spent picking back orders: Number of hours spent on picking back orders as a percentage of working hours.
• Sales order by FTE : This indicator measures the number of customer orders that are processed by full time employees per day. This helps evaluate the workforce cost per order.
• Scrap (or leftover) value %: Scrap (or leftover) value as a percentage of production value.
• Inventory Accuracy: Most Advanced Planning Systems calculate net inventory requirements. If the book inventory used as the basis for these calculations has a high error, the net inventory requirements generated will not reflect the true inventory needs. The inventory error should be factored into the safety stock calculation to protect service levels from variance in inventory due to inventory count accuracy.
Assertive continuous improvement programs should be in place to support a decrease in inventory count errors.
Inventory Accuracy = (|book inventory - counted inventory|)/book inventory
• Inventory Carrying Costs: Inventory Carrying Cost = Inventory Carrying Rate x Average Inventory Value
• Inventory Carrying Rate: This can best be explained by the example below
1. Add up annual Inventory Costs: Example: Storage =Rs800k, Handling= Rs400k, Obsolescence =Rs600k, Damage= Rs800k, Administrative= Rs600k, Loss (pilferage etc)= Rs200k. Hence Total=Rs3,400k
2. Divide the Inventory Costs by the Average Inventory Value: Example: Rs3,400k / Rs34,000k = 10%
3. Add: Opportunity Cost of Capital (the return you could reasonably expect if you used the money elsewhere) = 9%, Insurance =4%, Taxes= 6%. Hence, total= 19%
4. Add the percentages: 10% + 19% = 29%. The Inventory Carrying Rate = 29%
• Missed Deliveries per Million (MPM): Measures supplier on time delivery by part reference ordered using the same logic as the quality measure PPM.
Several missed categories are defined such as ; Missing part reference, undershipped, overshipped, delivery window missed etc.
MPM = (Total number of missed deliveries / Total number of part references ordered) x 1,000,000
• Delivery Schedule Adherence (DSA): Delivery Schedule adherence (DSA) is a business metric used to calculate the timeliness of deliveries from suppliers. Delivery schedule adherence is calculated by dividing the number of on time deliveries in a period by the total number of deliveries made. The result is then multiplied by 100 and expressed as a percentage.
• Customer order promised cycle time: The anticipated or agreed upon cycle time of a Purchase Order. It is gap between the Purchase Order Creation Date and the Requested Delivery Date. This tells you the cycle time that you should expect (NOT the actual).
• Inventory replenishment cycle time: Measure of the Manufacturing Cycle Time plus the time included to deploy the product to the appropriate distribution center.
• Material value add : Sell price minus material cost divided by material cost.
• Supply chain cycle time: The total time it would take to satisfy a customer order if all inventory levels were 0.
• Fill Rate: The number of items ordered compared with items shipped. Fill rate can be calculated on a line item, SKU, case or value basis.
• On time ship rate: What percent of orders where shipped on or before the requested ship date. On time ship rate can be calculated on a line item, SKU, case or value basis.
• Perfect Order Measure / Fulfillment: The error-free rate of each stage of an order. Error rates are captured at each stage (order entry, picking, delivery, shipped without damage, invoiced correctly) and multiplied together.
• Customer order cycle time: The average time it takes to fill a customer order.
• % of backorders: The number (or percentage) of unfulfilled orders.
Inventory: Inventory is a list for goods and materials, or those goods and materials themselves, held available in stock by a business. Inventory are held in order to manage and hide from the customer the fact that supply delay is longer than delivery delay, and also to ease the effect of imperfections in the manufacturing process that lower production efficiencies if production capacity stands idle for lack of materials.
• Independent demand ratio: For manufacturers that also supply replacement parts and consumables this metric helps to define the % mix of demand for an item from independent (outside sources) vs dependent (inside sources). The ratio is calculated by dividing the unit usage for customer orders by the total unit usage of the item from all sources (work orders, sales samples, destructive testing, inventory adjustments, etc.)
• Early receipts to MRP date (required date): Early receipts to MRP date - This is a measure on your Planning efficiencies. Some planners or warehouse personnel may request that the material be brought in long before the plant/operators need the parts. Reasons for doing so may be quality, lead time variance, buffer stock etc. Early receipts to MRP produce higher levels of inventory that are not required yet. In a way, this is at the other end of the scale than JIT. Measure: MRP due date vs Receive to Dock (stores) date.
• Early PO Receipts to PO due date: Early receipts to PO date - This is a measure on your suppliers and their diligence to supply per the contract date. Early receipts to PO produce unexpected deliveries turning up, congested goods inwards and of course higher that projected inventory levels. Measure: PO due date vs Receive to Dock (stores) date.
• Sell through %: A percentage of units sold during a period and is equal to Units sold divided by (units sold + on hand inventory). This can also be described as Units sold divided by Beginning Inventory Quantity.
• Inactive Stock: Products with Stock (in units or Rs), and without movement-sales in a given period of time (depending on movement of the market). Useful to define continuity of a specific product-size (SKU), or promotion campaigns. Most useful in companies with a big number of SKUs.
• Average age of inventory: The (average) age of each product in stock. For example, product received in Jan, but remains until Aug.
• Unit Cost per batch: Unit Cost per batch = (Cost/Quantity) for each batch Primarily used in FIFO (First In First Out) Method Assumes an inventory of non-unique goods (that is, every one is similar to every other one) Generally preferred inventory valuation method. Assumes inventory is sold in the order that it is stocked, with the oldest goods sold first and the newest goods sold last. Uses the unit cost per batch of acquired/produced goods, and counts the inventory backwards from the newest batch.
• Inventory Value: Inventory Value = (Average Unit Cost) x (Units of current Inventory)
• Stock cover: Stock cover is the length of time that inventory will last if current usage continues.
• Stockouts in period: Stockouts indicate where a demand cannot be met due to the absence of the required inventory.
• Inventory lead time: Lead time is the length of time it takes to obtain inventory from suppliers.
• Inventory Turnover: The number of times that a company’s inventory cycles or turns over per measurement period (month, quarter, year).
• Inventory months of supply: Inventory On Hand / Avg Monthly Usage
SCOR: The Supply-Chain Operations Reference-model (SCOR) is a process reference model that has been developed and endorsed by the Supply-Chain Council as the cross-industry standard diagnostic tool for supply-chain management. SCOR enables users to address, improve, and communicate supply-chain management practices within and between all interested parties.
• Order fulfillment cycle time: Order Fulfillment Cycle Time is a continuous measurement defined as the amount of time from customer authorization of a sales order to the customer receipt of product.
• Total supply chain management cost: Total Supply Chain Management Cost is a discrete measurement defined as the fixed and operational costs associated with the Plan, Source, Make, and Deliver supply chain processes.
• Upside supply chain flexibility: Upside Supply Chain Flexibility is a discrete measurement defined as the amount of time it takes a supply chain to respond to an unplanned 20% increase in demand without service or cost penalty.
• Direct Product Cost: Sum of costs associated with manufacturing a specific product.
• Direct Labor Cost: Sum of costs associated with payment of the employee insurances, taxes etc.
• Direct Material Cost: Sum of costs associated with acquisition of support material.
• Time needed to recruit/hire/train additional labor: Amount of time required to achieve a certain substantial improvement concerning the number of employees.
• Time needed to obtain additional capital: Amount of time required to achieve a certain substantial improvement concerning capital.
• Time needed to obtain additional equipment: Amount of time required to achieve a certain substantial improvement concerning equipment acquisition.
• Finished product cycle time: Average time associated with finalizing activities, such as: package, stock, etc.
• Test cycle time: Average time associated with testing and trying out activities.
• Cost of managing processes: Periodic costs of managing processes, usually based on the number of FTEs involved in management functions for processes.
• Cost of goods sold (COGS): Cost of Goods Sold includes all expenses directly associated with the production of goods or services the company sells (such as material, labor, overhead, and depreciation). It does not include SG&A.
• Perfect Order Measure / Fulfillment: The error-free rate of each stage of an order. Error rates are captured at each stage (order entry, picking, delivery, shipped without damage, invoiced correctly) and multiplied together.
Cash Conversion Cycle (CCC): A metric that expresses the length of time, in days, that it takes for a company to convert resource inputs into cash flows. The cash conversion cycle attempts to measure the amount of time each net input dollar is tied up in the production and sales process before it is converted into cash through sales to customers. This metric looks at the amount of time needed to sell inventory, the amount of time needed to collect receivables and the length of time the company is afforded to pay its bills without incurring penalties. Also known as “cash cycle”. Calculated as: CCC = DIO + DSO - DPO Where: DIO represents days inventory outstanding, DSO represents days sales outstanding, DPO represents days payable outstanding. Usually a company acquires inventory on credit, which results in accounts payable. A company can also sell products on credit, which results in accounts receivable. Cash, therefore, is not involved until the company pays the accounts payable and collects accounts receivable. So the cash conversion cycle measures the time between outlay of cash and cash recovery. This cycle is extremely important for retailers and similar businesses. This measure illustrates how quickly a company can convert its products into cash through sales. The shorter the cycle, the less time capital is tied up in the business process, and thus the better for the company’s bottom line.
Improvement Opportunities in Retail Logistics
In general, the logistic decisions taken by the retailer can be improved by increasing:
• the level of differentiation when controlling the operations;
• the level of sophistication in the Decision Support Systems;
• the level of integration of multiple decisions (made by the retailer company and/or its supply chain partners).
Below, several examples are given to illustrate how each of these general guidelines can be translated into specific solutions, taking into account the fact that different retailers and/or different products need different logistic solutions.
The Level of Differentiation when Controlling the Operations
Different types of items need different ways of replenishment. For example ABC-classification, based on the perception that items with large turnover (A-items) need to be treated differently compared to items with low turnover (C-items). While there is some value in this approach, we propose a different classification for retail-items. We distinguish the following five main product categories:
1. Phasing-in/out items (including items with a short Product Life Cycle)
2. Promotion items
3. Purchasing driven items
4. Capacity driven items
5. Regular items
Below, each of these five product categories is discussed in more detail.
The phasing-in/out items (including items with a short product life cycle) are different from other items since there is either very little demand history available, or it becomes very risky to carry inventory due to obsolescence. Thus, for these items, special attention is given to issues like demand forecasting and inventory management in an environment with high risk of obsolescence and/or markdown policies. Improvement opportunities reported in the literature are:
• Using similarity in forecasts made by different individual people as an indicator of forecast accuracy when no sales data are available yet;
• Using early sales data to improve demand forecasts in the case of style goods;
• Using repeat rate information from customer cards to improve demand forecasts when new products are introduced;
• Using optimal markdown policies to reduce the risk of obsolescence.
The demand forecasts for items with a short product life cycle (like style goods) can be improved substantially in two ways. The first improvement applies when an initial production or buy decision has to be made and no sales data are available yet for the new assortment. When each member of a buying committee makes an independent demand forecast for every product, the variance in these individual forecasts is an almost perfect predictor of the overall demand forecast accuracy. This allows the manufacturer and/or retailer to select the items with a high demand forecast accuracy, which can be manufactured at the beginning of the production season. The production of items with low demand forecast accuracy is postponed until a group of large retailers placed their first orders (called the Early Write program). These first orders typically make up approximately 20% of the total orders. While this procedure was first applied at a manufacturer, a similar procedure may also be used at a retailer, when he/she receives his/her first actual sales data in the new season. Fisher et al. (2001) report how the inventory replenishment of products with a short product lifecycle can be optimized for a retailer, when the retailer has two buying opportunities: an initial buy and a reorder opportunity.
Another tool to quickly evaluate the performance of items that are phasing in is applied by Dunnhumby at Tesco (Hill and Dowle, 2003). The strength of their approach is that they use detailed information on the buying behavior of individual customers. This is possible thanks to the retailer’s customer card, which is providing them with information for more than millions of customers. When a new product is introduced, they measure not only the sales rate, but also the repeat rate, which is defined as the proportion of customers who come back to the store for the new product. This information enables them to tell within weeks of the launch whether a product is successful or not. To forecast demand, they identify the 10 most similar product launches (in terms of how the repeat rate evolves over time) that have taken place in the same product category in the last 2.5 years.
The promotion items are items that are part of the regular assortment, but are either offered temporarily at a reduced price or offered at the regular price but with additional visibility (e.g. via advertisements or via a special location in the store).
Some of the possible Improvement opportunities are:
• Using marketing intelligence and/or econometric models to forecast demand for the promotion items and their substitutes
• Using a push-strategy with two waves
• Coordinating the promotion with the supplier
For these items, demand should no longer be forecasted based on extrapolation of time series (e.g. via methods like exponential smoothing or moving average, which are typically used when the item is not promoted), but based on marketing intelligence taking into account price-elasticities and/or the impact of promotions and advertising on consumer buying behavior. Since the sales during promotions may well be a (large) multiple of regular sales, promotions should be typically coordinated with external suppliers to make sure enough products are available in time in the retailers’ DC. For items in the same product category as the promoted item, substitution effects may occur, which have to be taken into account when forecasting their demand.
While regular items are typically pulled by the retail stores, promotion items are typically pushed by a central decision maker. For example, the shipments from the DC may typically be based on a so-called “alpha-policy”: the items are distributed in two waves, and, in the first wave, alpha % is pushed to the stores. Often, the optimal value for alpha is somewhere between 70 and 80%. A few days after the promotion started, the remaining 20 to 30% is distributed based on the early sales data.
The purchasing driven items are one-time-items that are not part of the regular assortment, but are bought by the Purchasing department. The reason might be that they spotted a special buying or selling opportunity. The purchasers buy a certain quantity of the product, and when this lot is sold-out, no replenishment from the supplier takes place.
The amount purchased is often determined by purchasing considerations (e.g. based on discount-opportunities) rather than by demand forecasting. For the distribution of the purchasing driven items to the stores, a push-strategy with two waves, like the alpha-policy, may be adopted.
The capacity driven items are items used by the Operations department to smooth handling and/or transportation capacities. If, for example, the demand for these capacities varies within the week, smoothing may lead to a reduction in the total assets needed.
To smooth handling-capacities in the DC and the stores, the review period for items with sufficient excess shelf space1 may be increased by decreasing the delivery frequency. For example, a store may order part of its assortment on a weekly basis, while another part of its assortment is ordered on a daily basis. The items ordered on a weekly basis can be ordered in the quiet part of the week, in order to smooth the handling capacity in the retail supply chain. Ordering with a lower frequency often leads to higher lot-sizes per item, implying also higher handling efficiency. Another way to benefit from reduced ordering frequencies is to redesign the retailer’s DC. If all items ordered on a weekly basis are stored in a separate part of the DC, the total walking distance for the order pickers per week can be reduced substantially. A pre-requisite for this is that all items in this part of the DC have excess shelf space in all stores.
To smooth transportation capacities, large volume/large sales items may be used. In groceries, these are typically items like soft drinks. On Tuesday and Wednesday, the regular replenishment quantities ordered by the stores may be low, while on Thursday and Friday these quantities may be high. By ordering these items in advance on Tuesday and Wednesday instead of on Thursday and Friday, the capacity load is smoothed. If the retail store has little storage space available, this option may not be feasible.
The regular items are all items that are not phasing in or out, are not on promotion, and are not purchasing or capacity driven. Before discussing the operational control of the regular items in the store, a few notes should be made on the trade-off between inventory holding costs and customer service, and its impact on the control of the entire supply chain. In several projects with retailers, it has been noted that at the operational level (where the size of the store and the assortments are given), the space in the retail store should be considered as a constraint rather than a cost factor. Moreover, handling costs at the retailer’s DC and particularly at the store level usually outweigh the relevant
1 The items with sufficient excess shelf space are often slow-moving items and/or (physically) small items.
inventory holding costs for regular items by far. In addition, the inventory contributes most to the service level of the final customer, if this inventory is stored mainly downstream in the supply chain. Therefore, the supply chain should often aim to handle goods as long as possible in the most efficient handling units (trucks or pallets (or even layers) when distributed from the manufacturer to the retailer’s DC), and accept the higher inventory levels in the retailer’s DC. The goods can be shipped as soon as they are produced. This concept is called Supply Driven Coordination or Chain Synchronization. Moreover, from the retailer’s DC one might ship inventory as soon as possible to the store, when it fits on the shelves (given the number of facings, determined at the tactical level in the planograms).
In current ASO systems, the regular items often follow a traditional (R,s,nQ)-policy2. This means that every review period (R), the inventory position is checked to see whether it is below the reorder level (s). If so, n times Q items are being ordered with Q being the case pack size and n the minimal integer number of case packs needed to make sure that, after reordering, the inventory position is equal to or higher than s. These parameters still leave a number of options open to further differentiate the inventory replenishment strategies within the regular items.
For example, the review period R may be different for different items. In a supermarket environment, we noted that perishables and non-perishables have clear distinct sales and logistic characteristics. By definition, perishables have a smaller Shelf Life than non-perishables. As a result, when controlling perishables’ inventories, the focus is more on reducing waste. For perishables with a very low Shelf Life, this reduction of waste may be achieved by decreasing the review period (i.e. by increasing the delivery frequency).
Not only the review period may be different for different items, but also the reorder level may be determined in a different way for different items. If we consider again the perishables with a very low Shelf Life, we note that apart from decreasing the review period, other options to reduce the waste are: reduction of the lead-time (e.g. by using cross-docking or direct delivery), keeping average sales per item relatively high (by keeping assortments limited) and/or using the customers’ willingness to substitute demand within a product category.
2 Note that a full-service concept (fill the shelves as soon as a new case pack fits in) is a special case of (R,s,nQ) with s equal to the maximum shelf capacity minus the case pack size plus one consumer unit.
Apparently, for these items, the reorder level should not only be based on small lead-times and high average demand, but also take into account the product substitution. Most ASO systems are primarily designed for non-perishables and do not take into account these substitution effects. For items with very high substitution rate (e.g. bread) this would lead to unrealistic reorder levels.
For vegetables and fruit, reduction of waste is also important, and this can be improved by increasing the quality of the demand forecast. Note that the demand forecast is a major factor in the reorder level. The demand forecast might be improved by taking into account price-elasticity, the quality of the inventory on hand and seasonal effects.
Finally, for perishables with multiple lots on the shelf, each lot having a different age, more complex models may be needed to determine the reorder level. There are numerous models in the literature dealing with perishable items.
The Level of Sophistication in Reorder Systems
Thanks to economies of scale and cheaper and better information technology, large retail chains are trying to distinguish themselves from other retailers by increasing the level of sophistication of their reorder systems. At this moment, the quality of reorder systems varies greatly between retail chains, and, even within retail chains, it may differ substantially per retailer. The level of sophistication of their reorder system may differ with respect to:
1. the level of automation;
2. the quality of the input data;
3. the intelligence in setting the logistic parameters in the reorder system;
4. the ability to visualize economic trade-offs;
5. the ability of the personnel to make decisions or to evaluate proposed decisions.
In some stores, the reorder decisions are still made manually, without any support from a computer. In other stores, the computer may give advice on the timing and the quantity to be ordered for most items. But even in those stores, part of the assortment may still be ordered without the help of a computer. At a grocer’s for example, we noted that the majority of non-perishables were ordered via an ASO system, while certain perishables were ordered manually, since they either required additional intelligence (like a judgment on the quality of the inventory for vegetables), or they had to be ordered via a separate ordering system (belonging to a particular supplier).
Even when automated store ordering is implemented, the data quality has a large impact on the success of the system. It is known from empirical research that inventory data are highly inaccurate. To increase the sales data accuracy retailers may either apply more strict rules on how to register sales, or they may attach an electronic identification device to each individual product, which is scanned automatically at the cash register.
The intelligence in setting the logistic parameters in the system (i.e. the reorder level and the order quantity) also differs a great deal. Sometimes, a fixed reorder level is applied, and sometimes the reorder level varies over time, taking into account weekly sales patterns, seasonality and/or trends in sales. In some cases, the determination of the reorder level depends on many different variables like the weather, substitution effects, the review period, the price, etc. These more complex situations are often not dealt with by the ASO systems, but are often handled by store clerks who have considerable experience in their product category.
Also the order quantity is determined in many different ways. The simplest case is when the supplier determines the order quantity by fixing the case pack size (typically for most items in the supermarket). If, however, the item is made for one particular retailer only, the retailer can optimize the case pack size. This optimization should not only include the minimization of the inventory holding costs and the fixed ordering costs, but also take into account operational constraints like the maximum shelf capacity. Ideally, the computer should not only calculate the optimal solution, but also offer insight to the decision maker on the economic trade-offs between important performances indicators. In the example of the case pack size, we can think of the following performance indicators: the number of orders per year, the total handling time needed, the expected total number of refills needed (if the case pack size is too big to put on the shelf), the total inventory and the resulting service level to the customers
To be able to make this trade-offs the personnel needs good training. Purchasers for example, who are often responsible for setting the case pack size in cooperation with the supplier, may be more focused on and trained in getting the lowest price than in making an overall evaluation of the impact of the case pack size on all performance indicators. In addition, at the store level, where store managers or store clerks are responsible for the determination of the order quantities, the level of education may differ greatly.
The Level of Integration of Multiple Decisions (Made by the Retail Company and/or Its Supply Chain Partners).
The decisions with respect to inventory and capacity management are often affecting many different performance indicators, organizational units and hierarchical levels within these organizational units at the same time. Often, in practice only partial effects are taken into account when decisions are being made. As a result, the quality of the decision-making can be improved by increasing the level of integration. We distinguish three types of integration:
1. Integration of all relevant performance indicators in the supply chain;
2. Integration of decisions made at different organizational units;
3. Integration or coordination of decisions made at different hierarchical levels.
Below some examples are given, which are related to inventory and capacity management and which were encountered in retail supply chains. Each example includes one or more types of integration.
Example 1
When deciding on the case pack size, a non-food-retailer used the classical Economic Order Quantity formula. This formula is almost a hundred years old and applied successfully at many companies in multiple industries. The formula is derived from a model, which only considers the inventory holding costs and the fixed ordering costs. Cost analysis in several retail supply chains (including this one) showed that, in fact, handling costs are often far more important than inventory holding costs, and should, therefore, be included in the decision-making. As a matter of fact, not only handling costs in the store, but also handling costs at the retailers’ DC and/or the supplier may be significant and affected by the decision on the case pack size. In this case, the handling at the retailer’s DC had to be taken into account as well, whereas the implications for the supplier were only minor. Finally, note that even a focus on total relevant costs in the entire supply chain may be too narrow-minded. The customer service level for example may also be affected by the case pack size.
Example 2
Within retail chains, Marketing and Operations are often separate departments. Marketing typically decides on issues like the marketing strategy, target customer service level, the store layout, depth and breadth of the assortment, pricing, promotions and shelf space allocation (via planograms). Operations typically decide on issues like (in) direct delivery, delivery frequencies, replenishment strategies (pull/push), reorder levels, minimum lot-sizes, etc.
Sometimes the decisions from both departments are interdependent, but this is not always taken into account when the actual decisions are being made. For example, planograms and reorder levels should be matched. If the space allocated to a product is less than the space required for operations (which is mainly based on the reorder level and the case pack size), inefficient handling may be the result: if an order arrives at the store, it may not fit on the shelves, leading to leftovers, which are sent to the backroom and have to be taken back to the shelves again later on.
Example 3
A retailer typically aims for a particular market segment and designs his logistics strategy to meet the requirements of this market segment. For example, some retailers aim for high customer service, while others primarily aim for low costs. To make their strategies work, the retail companies have to ensure that their long-term marketing and logistics strategies are in line with the replenishment strategies applied at the store level every day. If, e.g. at the shop floor, the replenishment strategy is to fill the shelves completely as soon as a new case pack fits in, this would be in line with a high customer service objective, but not with a low cost strategy. In case the inventory replenishment strategy is determined locally (at the store level) by individual people, there is a serious risk that either these people have different objectives, or they are simply not aware of the link between their decisions and the strategy of the retail chain.
Concluding Remarks
In this paper, we have shown that with the knowledge of the KPIs both customer service and the capacity utilization in retail chains can be increased by improving the logistic decisions taken by the retailer. New technologies allow the retailers to improve their logistic decisions by increasing either the level of differentiation, the level of sophistication and/or the level of integration in their decision-making.
We have described the KPIs by dividing it into different categories of its respective field: Supply Chain and Logistics, Inventory, SCOR (Supply-Chain Operations Reference-model), Cash Conversion Cycle (CCC). All these metrics aids in the supply chain management of the retail sector. In this paper, we described the meaning, formula and significance of each KPI.
In many retail chains, different items need different logistic solutions. In this paper, we distinguished five product categories: items that are phasing-in/out, items that are on promotion, items that are driving the utilization of capacities, and regular items. All these categories require a different way of controlling the operations. Most ASO systems currently applied are primarily developed for regular items. In this paper, we describe how these ASO systems can be improved to also support other products. All these findings are based on observations at the retailers in Kolkata.
A final remark can be made on the importance of the analysis of the KPIs in retail chains, and its impact on the focus of Retail Logistics and its decisions.
This paper attempts to track key performance indicators (KPIs) in order to figure out the performance of the Supply Chain in the retail sector. It also focuses on inventory replenishment strategies and capacity utilization in the retail sector. In recent years, this sector has spent considerable amount of time and money trying to improve its operations in such a way so as to respond efficiently to customers’ needs. This has led to several developments like the introduction of automated store ordering, usage of RFID and etc.
The KPIs helps in directly analyzing the performance of every specific activity and operation and hence also helps in zeroing down to the exact root of the problem, if any, and thus helps the managers to rectify them. The Improvement Opportunities are further explained in detail for achieving a better performance.
The Key Performance Indicators (KPIs)
The KPIs are segregated into different categories accordingly as follows:
Supply Chain and Logistics: The network of retailers, distributors, transporters, storage facilities and suppliers that participate in the sale, delivery and production of a particular product.
• % of time spent picking back orders: Number of hours spent on picking back orders as a percentage of working hours.
• Sales order by FTE : This indicator measures the number of customer orders that are processed by full time employees per day. This helps evaluate the workforce cost per order.
• Scrap (or leftover) value %: Scrap (or leftover) value as a percentage of production value.
• Inventory Accuracy: Most Advanced Planning Systems calculate net inventory requirements. If the book inventory used as the basis for these calculations has a high error, the net inventory requirements generated will not reflect the true inventory needs. The inventory error should be factored into the safety stock calculation to protect service levels from variance in inventory due to inventory count accuracy.
Assertive continuous improvement programs should be in place to support a decrease in inventory count errors.
Inventory Accuracy = (|book inventory - counted inventory|)/book inventory
• Inventory Carrying Costs: Inventory Carrying Cost = Inventory Carrying Rate x Average Inventory Value
• Inventory Carrying Rate: This can best be explained by the example below
1. Add up annual Inventory Costs: Example: Storage =Rs800k, Handling= Rs400k, Obsolescence =Rs600k, Damage= Rs800k, Administrative= Rs600k, Loss (pilferage etc)= Rs200k. Hence Total=Rs3,400k
2. Divide the Inventory Costs by the Average Inventory Value: Example: Rs3,400k / Rs34,000k = 10%
3. Add: Opportunity Cost of Capital (the return you could reasonably expect if you used the money elsewhere) = 9%, Insurance =4%, Taxes= 6%. Hence, total= 19%
4. Add the percentages: 10% + 19% = 29%. The Inventory Carrying Rate = 29%
• Missed Deliveries per Million (MPM): Measures supplier on time delivery by part reference ordered using the same logic as the quality measure PPM.
Several missed categories are defined such as ; Missing part reference, undershipped, overshipped, delivery window missed etc.
MPM = (Total number of missed deliveries / Total number of part references ordered) x 1,000,000
• Delivery Schedule Adherence (DSA): Delivery Schedule adherence (DSA) is a business metric used to calculate the timeliness of deliveries from suppliers. Delivery schedule adherence is calculated by dividing the number of on time deliveries in a period by the total number of deliveries made. The result is then multiplied by 100 and expressed as a percentage.
• Customer order promised cycle time: The anticipated or agreed upon cycle time of a Purchase Order. It is gap between the Purchase Order Creation Date and the Requested Delivery Date. This tells you the cycle time that you should expect (NOT the actual).
• Inventory replenishment cycle time: Measure of the Manufacturing Cycle Time plus the time included to deploy the product to the appropriate distribution center.
• Material value add : Sell price minus material cost divided by material cost.
• Supply chain cycle time: The total time it would take to satisfy a customer order if all inventory levels were 0.
• Fill Rate: The number of items ordered compared with items shipped. Fill rate can be calculated on a line item, SKU, case or value basis.
• On time ship rate: What percent of orders where shipped on or before the requested ship date. On time ship rate can be calculated on a line item, SKU, case or value basis.
• Perfect Order Measure / Fulfillment: The error-free rate of each stage of an order. Error rates are captured at each stage (order entry, picking, delivery, shipped without damage, invoiced correctly) and multiplied together.
• Customer order cycle time: The average time it takes to fill a customer order.
• % of backorders: The number (or percentage) of unfulfilled orders.
Inventory: Inventory is a list for goods and materials, or those goods and materials themselves, held available in stock by a business. Inventory are held in order to manage and hide from the customer the fact that supply delay is longer than delivery delay, and also to ease the effect of imperfections in the manufacturing process that lower production efficiencies if production capacity stands idle for lack of materials.
• Independent demand ratio: For manufacturers that also supply replacement parts and consumables this metric helps to define the % mix of demand for an item from independent (outside sources) vs dependent (inside sources). The ratio is calculated by dividing the unit usage for customer orders by the total unit usage of the item from all sources (work orders, sales samples, destructive testing, inventory adjustments, etc.)
• Early receipts to MRP date (required date): Early receipts to MRP date - This is a measure on your Planning efficiencies. Some planners or warehouse personnel may request that the material be brought in long before the plant/operators need the parts. Reasons for doing so may be quality, lead time variance, buffer stock etc. Early receipts to MRP produce higher levels of inventory that are not required yet. In a way, this is at the other end of the scale than JIT. Measure: MRP due date vs Receive to Dock (stores) date.
• Early PO Receipts to PO due date: Early receipts to PO date - This is a measure on your suppliers and their diligence to supply per the contract date. Early receipts to PO produce unexpected deliveries turning up, congested goods inwards and of course higher that projected inventory levels. Measure: PO due date vs Receive to Dock (stores) date.
• Sell through %: A percentage of units sold during a period and is equal to Units sold divided by (units sold + on hand inventory). This can also be described as Units sold divided by Beginning Inventory Quantity.
• Inactive Stock: Products with Stock (in units or Rs), and without movement-sales in a given period of time (depending on movement of the market). Useful to define continuity of a specific product-size (SKU), or promotion campaigns. Most useful in companies with a big number of SKUs.
• Average age of inventory: The (average) age of each product in stock. For example, product received in Jan, but remains until Aug.
• Unit Cost per batch: Unit Cost per batch = (Cost/Quantity) for each batch Primarily used in FIFO (First In First Out) Method Assumes an inventory of non-unique goods (that is, every one is similar to every other one) Generally preferred inventory valuation method. Assumes inventory is sold in the order that it is stocked, with the oldest goods sold first and the newest goods sold last. Uses the unit cost per batch of acquired/produced goods, and counts the inventory backwards from the newest batch.
• Inventory Value: Inventory Value = (Average Unit Cost) x (Units of current Inventory)
• Stock cover: Stock cover is the length of time that inventory will last if current usage continues.
• Stockouts in period: Stockouts indicate where a demand cannot be met due to the absence of the required inventory.
• Inventory lead time: Lead time is the length of time it takes to obtain inventory from suppliers.
• Inventory Turnover: The number of times that a company’s inventory cycles or turns over per measurement period (month, quarter, year).
• Inventory months of supply: Inventory On Hand / Avg Monthly Usage
SCOR: The Supply-Chain Operations Reference-model (SCOR) is a process reference model that has been developed and endorsed by the Supply-Chain Council as the cross-industry standard diagnostic tool for supply-chain management. SCOR enables users to address, improve, and communicate supply-chain management practices within and between all interested parties.
• Order fulfillment cycle time: Order Fulfillment Cycle Time is a continuous measurement defined as the amount of time from customer authorization of a sales order to the customer receipt of product.
• Total supply chain management cost: Total Supply Chain Management Cost is a discrete measurement defined as the fixed and operational costs associated with the Plan, Source, Make, and Deliver supply chain processes.
• Upside supply chain flexibility: Upside Supply Chain Flexibility is a discrete measurement defined as the amount of time it takes a supply chain to respond to an unplanned 20% increase in demand without service or cost penalty.
• Direct Product Cost: Sum of costs associated with manufacturing a specific product.
• Direct Labor Cost: Sum of costs associated with payment of the employee insurances, taxes etc.
• Direct Material Cost: Sum of costs associated with acquisition of support material.
• Time needed to recruit/hire/train additional labor: Amount of time required to achieve a certain substantial improvement concerning the number of employees.
• Time needed to obtain additional capital: Amount of time required to achieve a certain substantial improvement concerning capital.
• Time needed to obtain additional equipment: Amount of time required to achieve a certain substantial improvement concerning equipment acquisition.
• Finished product cycle time: Average time associated with finalizing activities, such as: package, stock, etc.
• Test cycle time: Average time associated with testing and trying out activities.
• Cost of managing processes: Periodic costs of managing processes, usually based on the number of FTEs involved in management functions for processes.
• Cost of goods sold (COGS): Cost of Goods Sold includes all expenses directly associated with the production of goods or services the company sells (such as material, labor, overhead, and depreciation). It does not include SG&A.
• Perfect Order Measure / Fulfillment: The error-free rate of each stage of an order. Error rates are captured at each stage (order entry, picking, delivery, shipped without damage, invoiced correctly) and multiplied together.
Cash Conversion Cycle (CCC): A metric that expresses the length of time, in days, that it takes for a company to convert resource inputs into cash flows. The cash conversion cycle attempts to measure the amount of time each net input dollar is tied up in the production and sales process before it is converted into cash through sales to customers. This metric looks at the amount of time needed to sell inventory, the amount of time needed to collect receivables and the length of time the company is afforded to pay its bills without incurring penalties. Also known as “cash cycle”. Calculated as: CCC = DIO + DSO - DPO Where: DIO represents days inventory outstanding, DSO represents days sales outstanding, DPO represents days payable outstanding. Usually a company acquires inventory on credit, which results in accounts payable. A company can also sell products on credit, which results in accounts receivable. Cash, therefore, is not involved until the company pays the accounts payable and collects accounts receivable. So the cash conversion cycle measures the time between outlay of cash and cash recovery. This cycle is extremely important for retailers and similar businesses. This measure illustrates how quickly a company can convert its products into cash through sales. The shorter the cycle, the less time capital is tied up in the business process, and thus the better for the company’s bottom line.
Improvement Opportunities in Retail Logistics
In general, the logistic decisions taken by the retailer can be improved by increasing:
• the level of differentiation when controlling the operations;
• the level of sophistication in the Decision Support Systems;
• the level of integration of multiple decisions (made by the retailer company and/or its supply chain partners).
Below, several examples are given to illustrate how each of these general guidelines can be translated into specific solutions, taking into account the fact that different retailers and/or different products need different logistic solutions.
The Level of Differentiation when Controlling the Operations
Different types of items need different ways of replenishment. For example ABC-classification, based on the perception that items with large turnover (A-items) need to be treated differently compared to items with low turnover (C-items). While there is some value in this approach, we propose a different classification for retail-items. We distinguish the following five main product categories:
1. Phasing-in/out items (including items with a short Product Life Cycle)
2. Promotion items
3. Purchasing driven items
4. Capacity driven items
5. Regular items
Below, each of these five product categories is discussed in more detail.
The phasing-in/out items (including items with a short product life cycle) are different from other items since there is either very little demand history available, or it becomes very risky to carry inventory due to obsolescence. Thus, for these items, special attention is given to issues like demand forecasting and inventory management in an environment with high risk of obsolescence and/or markdown policies. Improvement opportunities reported in the literature are:
• Using similarity in forecasts made by different individual people as an indicator of forecast accuracy when no sales data are available yet;
• Using early sales data to improve demand forecasts in the case of style goods;
• Using repeat rate information from customer cards to improve demand forecasts when new products are introduced;
• Using optimal markdown policies to reduce the risk of obsolescence.
The demand forecasts for items with a short product life cycle (like style goods) can be improved substantially in two ways. The first improvement applies when an initial production or buy decision has to be made and no sales data are available yet for the new assortment. When each member of a buying committee makes an independent demand forecast for every product, the variance in these individual forecasts is an almost perfect predictor of the overall demand forecast accuracy. This allows the manufacturer and/or retailer to select the items with a high demand forecast accuracy, which can be manufactured at the beginning of the production season. The production of items with low demand forecast accuracy is postponed until a group of large retailers placed their first orders (called the Early Write program). These first orders typically make up approximately 20% of the total orders. While this procedure was first applied at a manufacturer, a similar procedure may also be used at a retailer, when he/she receives his/her first actual sales data in the new season. Fisher et al. (2001) report how the inventory replenishment of products with a short product lifecycle can be optimized for a retailer, when the retailer has two buying opportunities: an initial buy and a reorder opportunity.
Another tool to quickly evaluate the performance of items that are phasing in is applied by Dunnhumby at Tesco (Hill and Dowle, 2003). The strength of their approach is that they use detailed information on the buying behavior of individual customers. This is possible thanks to the retailer’s customer card, which is providing them with information for more than millions of customers. When a new product is introduced, they measure not only the sales rate, but also the repeat rate, which is defined as the proportion of customers who come back to the store for the new product. This information enables them to tell within weeks of the launch whether a product is successful or not. To forecast demand, they identify the 10 most similar product launches (in terms of how the repeat rate evolves over time) that have taken place in the same product category in the last 2.5 years.
The promotion items are items that are part of the regular assortment, but are either offered temporarily at a reduced price or offered at the regular price but with additional visibility (e.g. via advertisements or via a special location in the store).
Some of the possible Improvement opportunities are:
• Using marketing intelligence and/or econometric models to forecast demand for the promotion items and their substitutes
• Using a push-strategy with two waves
• Coordinating the promotion with the supplier
For these items, demand should no longer be forecasted based on extrapolation of time series (e.g. via methods like exponential smoothing or moving average, which are typically used when the item is not promoted), but based on marketing intelligence taking into account price-elasticities and/or the impact of promotions and advertising on consumer buying behavior. Since the sales during promotions may well be a (large) multiple of regular sales, promotions should be typically coordinated with external suppliers to make sure enough products are available in time in the retailers’ DC. For items in the same product category as the promoted item, substitution effects may occur, which have to be taken into account when forecasting their demand.
While regular items are typically pulled by the retail stores, promotion items are typically pushed by a central decision maker. For example, the shipments from the DC may typically be based on a so-called “alpha-policy”: the items are distributed in two waves, and, in the first wave, alpha % is pushed to the stores. Often, the optimal value for alpha is somewhere between 70 and 80%. A few days after the promotion started, the remaining 20 to 30% is distributed based on the early sales data.
The purchasing driven items are one-time-items that are not part of the regular assortment, but are bought by the Purchasing department. The reason might be that they spotted a special buying or selling opportunity. The purchasers buy a certain quantity of the product, and when this lot is sold-out, no replenishment from the supplier takes place.
The amount purchased is often determined by purchasing considerations (e.g. based on discount-opportunities) rather than by demand forecasting. For the distribution of the purchasing driven items to the stores, a push-strategy with two waves, like the alpha-policy, may be adopted.
The capacity driven items are items used by the Operations department to smooth handling and/or transportation capacities. If, for example, the demand for these capacities varies within the week, smoothing may lead to a reduction in the total assets needed.
To smooth handling-capacities in the DC and the stores, the review period for items with sufficient excess shelf space1 may be increased by decreasing the delivery frequency. For example, a store may order part of its assortment on a weekly basis, while another part of its assortment is ordered on a daily basis. The items ordered on a weekly basis can be ordered in the quiet part of the week, in order to smooth the handling capacity in the retail supply chain. Ordering with a lower frequency often leads to higher lot-sizes per item, implying also higher handling efficiency. Another way to benefit from reduced ordering frequencies is to redesign the retailer’s DC. If all items ordered on a weekly basis are stored in a separate part of the DC, the total walking distance for the order pickers per week can be reduced substantially. A pre-requisite for this is that all items in this part of the DC have excess shelf space in all stores.
To smooth transportation capacities, large volume/large sales items may be used. In groceries, these are typically items like soft drinks. On Tuesday and Wednesday, the regular replenishment quantities ordered by the stores may be low, while on Thursday and Friday these quantities may be high. By ordering these items in advance on Tuesday and Wednesday instead of on Thursday and Friday, the capacity load is smoothed. If the retail store has little storage space available, this option may not be feasible.
The regular items are all items that are not phasing in or out, are not on promotion, and are not purchasing or capacity driven. Before discussing the operational control of the regular items in the store, a few notes should be made on the trade-off between inventory holding costs and customer service, and its impact on the control of the entire supply chain. In several projects with retailers, it has been noted that at the operational level (where the size of the store and the assortments are given), the space in the retail store should be considered as a constraint rather than a cost factor. Moreover, handling costs at the retailer’s DC and particularly at the store level usually outweigh the relevant
1 The items with sufficient excess shelf space are often slow-moving items and/or (physically) small items.
inventory holding costs for regular items by far. In addition, the inventory contributes most to the service level of the final customer, if this inventory is stored mainly downstream in the supply chain. Therefore, the supply chain should often aim to handle goods as long as possible in the most efficient handling units (trucks or pallets (or even layers) when distributed from the manufacturer to the retailer’s DC), and accept the higher inventory levels in the retailer’s DC. The goods can be shipped as soon as they are produced. This concept is called Supply Driven Coordination or Chain Synchronization. Moreover, from the retailer’s DC one might ship inventory as soon as possible to the store, when it fits on the shelves (given the number of facings, determined at the tactical level in the planograms).
In current ASO systems, the regular items often follow a traditional (R,s,nQ)-policy2. This means that every review period (R), the inventory position is checked to see whether it is below the reorder level (s). If so, n times Q items are being ordered with Q being the case pack size and n the minimal integer number of case packs needed to make sure that, after reordering, the inventory position is equal to or higher than s. These parameters still leave a number of options open to further differentiate the inventory replenishment strategies within the regular items.
For example, the review period R may be different for different items. In a supermarket environment, we noted that perishables and non-perishables have clear distinct sales and logistic characteristics. By definition, perishables have a smaller Shelf Life than non-perishables. As a result, when controlling perishables’ inventories, the focus is more on reducing waste. For perishables with a very low Shelf Life, this reduction of waste may be achieved by decreasing the review period (i.e. by increasing the delivery frequency).
Not only the review period may be different for different items, but also the reorder level may be determined in a different way for different items. If we consider again the perishables with a very low Shelf Life, we note that apart from decreasing the review period, other options to reduce the waste are: reduction of the lead-time (e.g. by using cross-docking or direct delivery), keeping average sales per item relatively high (by keeping assortments limited) and/or using the customers’ willingness to substitute demand within a product category.
2 Note that a full-service concept (fill the shelves as soon as a new case pack fits in) is a special case of (R,s,nQ) with s equal to the maximum shelf capacity minus the case pack size plus one consumer unit.
Apparently, for these items, the reorder level should not only be based on small lead-times and high average demand, but also take into account the product substitution. Most ASO systems are primarily designed for non-perishables and do not take into account these substitution effects. For items with very high substitution rate (e.g. bread) this would lead to unrealistic reorder levels.
For vegetables and fruit, reduction of waste is also important, and this can be improved by increasing the quality of the demand forecast. Note that the demand forecast is a major factor in the reorder level. The demand forecast might be improved by taking into account price-elasticity, the quality of the inventory on hand and seasonal effects.
Finally, for perishables with multiple lots on the shelf, each lot having a different age, more complex models may be needed to determine the reorder level. There are numerous models in the literature dealing with perishable items.
The Level of Sophistication in Reorder Systems
Thanks to economies of scale and cheaper and better information technology, large retail chains are trying to distinguish themselves from other retailers by increasing the level of sophistication of their reorder systems. At this moment, the quality of reorder systems varies greatly between retail chains, and, even within retail chains, it may differ substantially per retailer. The level of sophistication of their reorder system may differ with respect to:
1. the level of automation;
2. the quality of the input data;
3. the intelligence in setting the logistic parameters in the reorder system;
4. the ability to visualize economic trade-offs;
5. the ability of the personnel to make decisions or to evaluate proposed decisions.
In some stores, the reorder decisions are still made manually, without any support from a computer. In other stores, the computer may give advice on the timing and the quantity to be ordered for most items. But even in those stores, part of the assortment may still be ordered without the help of a computer. At a grocer’s for example, we noted that the majority of non-perishables were ordered via an ASO system, while certain perishables were ordered manually, since they either required additional intelligence (like a judgment on the quality of the inventory for vegetables), or they had to be ordered via a separate ordering system (belonging to a particular supplier).
Even when automated store ordering is implemented, the data quality has a large impact on the success of the system. It is known from empirical research that inventory data are highly inaccurate. To increase the sales data accuracy retailers may either apply more strict rules on how to register sales, or they may attach an electronic identification device to each individual product, which is scanned automatically at the cash register.
The intelligence in setting the logistic parameters in the system (i.e. the reorder level and the order quantity) also differs a great deal. Sometimes, a fixed reorder level is applied, and sometimes the reorder level varies over time, taking into account weekly sales patterns, seasonality and/or trends in sales. In some cases, the determination of the reorder level depends on many different variables like the weather, substitution effects, the review period, the price, etc. These more complex situations are often not dealt with by the ASO systems, but are often handled by store clerks who have considerable experience in their product category.
Also the order quantity is determined in many different ways. The simplest case is when the supplier determines the order quantity by fixing the case pack size (typically for most items in the supermarket). If, however, the item is made for one particular retailer only, the retailer can optimize the case pack size. This optimization should not only include the minimization of the inventory holding costs and the fixed ordering costs, but also take into account operational constraints like the maximum shelf capacity. Ideally, the computer should not only calculate the optimal solution, but also offer insight to the decision maker on the economic trade-offs between important performances indicators. In the example of the case pack size, we can think of the following performance indicators: the number of orders per year, the total handling time needed, the expected total number of refills needed (if the case pack size is too big to put on the shelf), the total inventory and the resulting service level to the customers
To be able to make this trade-offs the personnel needs good training. Purchasers for example, who are often responsible for setting the case pack size in cooperation with the supplier, may be more focused on and trained in getting the lowest price than in making an overall evaluation of the impact of the case pack size on all performance indicators. In addition, at the store level, where store managers or store clerks are responsible for the determination of the order quantities, the level of education may differ greatly.
The Level of Integration of Multiple Decisions (Made by the Retail Company and/or Its Supply Chain Partners).
The decisions with respect to inventory and capacity management are often affecting many different performance indicators, organizational units and hierarchical levels within these organizational units at the same time. Often, in practice only partial effects are taken into account when decisions are being made. As a result, the quality of the decision-making can be improved by increasing the level of integration. We distinguish three types of integration:
1. Integration of all relevant performance indicators in the supply chain;
2. Integration of decisions made at different organizational units;
3. Integration or coordination of decisions made at different hierarchical levels.
Below some examples are given, which are related to inventory and capacity management and which were encountered in retail supply chains. Each example includes one or more types of integration.
Example 1
When deciding on the case pack size, a non-food-retailer used the classical Economic Order Quantity formula. This formula is almost a hundred years old and applied successfully at many companies in multiple industries. The formula is derived from a model, which only considers the inventory holding costs and the fixed ordering costs. Cost analysis in several retail supply chains (including this one) showed that, in fact, handling costs are often far more important than inventory holding costs, and should, therefore, be included in the decision-making. As a matter of fact, not only handling costs in the store, but also handling costs at the retailers’ DC and/or the supplier may be significant and affected by the decision on the case pack size. In this case, the handling at the retailer’s DC had to be taken into account as well, whereas the implications for the supplier were only minor. Finally, note that even a focus on total relevant costs in the entire supply chain may be too narrow-minded. The customer service level for example may also be affected by the case pack size.
Example 2
Within retail chains, Marketing and Operations are often separate departments. Marketing typically decides on issues like the marketing strategy, target customer service level, the store layout, depth and breadth of the assortment, pricing, promotions and shelf space allocation (via planograms). Operations typically decide on issues like (in) direct delivery, delivery frequencies, replenishment strategies (pull/push), reorder levels, minimum lot-sizes, etc.
Sometimes the decisions from both departments are interdependent, but this is not always taken into account when the actual decisions are being made. For example, planograms and reorder levels should be matched. If the space allocated to a product is less than the space required for operations (which is mainly based on the reorder level and the case pack size), inefficient handling may be the result: if an order arrives at the store, it may not fit on the shelves, leading to leftovers, which are sent to the backroom and have to be taken back to the shelves again later on.
Example 3
A retailer typically aims for a particular market segment and designs his logistics strategy to meet the requirements of this market segment. For example, some retailers aim for high customer service, while others primarily aim for low costs. To make their strategies work, the retail companies have to ensure that their long-term marketing and logistics strategies are in line with the replenishment strategies applied at the store level every day. If, e.g. at the shop floor, the replenishment strategy is to fill the shelves completely as soon as a new case pack fits in, this would be in line with a high customer service objective, but not with a low cost strategy. In case the inventory replenishment strategy is determined locally (at the store level) by individual people, there is a serious risk that either these people have different objectives, or they are simply not aware of the link between their decisions and the strategy of the retail chain.
Concluding Remarks
In this paper, we have shown that with the knowledge of the KPIs both customer service and the capacity utilization in retail chains can be increased by improving the logistic decisions taken by the retailer. New technologies allow the retailers to improve their logistic decisions by increasing either the level of differentiation, the level of sophistication and/or the level of integration in their decision-making.
We have described the KPIs by dividing it into different categories of its respective field: Supply Chain and Logistics, Inventory, SCOR (Supply-Chain Operations Reference-model), Cash Conversion Cycle (CCC). All these metrics aids in the supply chain management of the retail sector. In this paper, we described the meaning, formula and significance of each KPI.
In many retail chains, different items need different logistic solutions. In this paper, we distinguished five product categories: items that are phasing-in/out, items that are on promotion, items that are driving the utilization of capacities, and regular items. All these categories require a different way of controlling the operations. Most ASO systems currently applied are primarily developed for regular items. In this paper, we describe how these ASO systems can be improved to also support other products. All these findings are based on observations at the retailers in Kolkata.
A final remark can be made on the importance of the analysis of the KPIs in retail chains, and its impact on the focus of Retail Logistics and its decisions.
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