About Kurt Hatlevik

I appreciate that I can be a part of this worldwide blog community—as a consultant working from Norway, the blog lets me share more than 20 years of experience with Microsoft Dynamics 365. Along the way, I participated in developing retail, PDA/RF, barcoding, master data, kitting and WMS-solutions for Dynamics. My blog focuses on my deepest interests and expertise: along with a 360 degree view of digital transformation topics, I welcome opportunities to dive into retail and intercompany supply chain automation, logistics, and production—everything that is moving around in a truly connected enterprise. As Enterprise Architect on Dynamics 365, I specialize in strategic development and planning for corporate vertical solutions and works to build international networks that increase knowledge and understanding for Dynamics 365. As an advocate for both providers and customers, I'm committed to ensure that customers constantly changing needs are meet, and I see community as key for increasing expertise. I welcome you to connect with me.

Types of warehouses

Warehouses may be categorized by type, which is primarily defined by the customers they serve. Here are some of the more important distinctions:

A retail distribution center
typically supplies product to retail stores, such as Wal-Mart or Target. The immediate customer of the distribution center is a retail store, which is likely to be a regular or even captive customer, receiving shipments on regularly scheduled days. A typical order might comprise hundreds or thousands of items; and because the distribution center might serve hundreds of stores, the flow of product is huge. The suite of products changes with customer tastes and marketing plans; but because the orders are typically known a day or more in advance, it is possible to plan ahead. Some product may be pushed from the distribution center to the stores, especially in support of marketing campaigns.

A service parts distribution center
is among the most challenging of facilities to manage. They hold spare parts for expensive capital equipment, such as automobiles, airplanes, computer systems, or medical equipment. Consequently, one facility may represent a huge investment in inventory: tens or even hundreds of thousands of parts, some very expensive. (A typical automobile contains almost 10,000 parts.) Because of the large number of parts, total activity in the DC may be statistically predictable, but the demand for any particular part is relatively small and therefore hard to predict. This means that the variance of demand can be large and so relatively large quantities of safety stock must be held, especially since there can be usually long lead times to replenish parts to the warehouse. Indeed, sometimes there is as much safety stock as cycle stock, and so, in aggregate, these skus require much space. This in turn increases travel distances and makes order-picking less efficient.

A typical service parts warehouse manages two distinct order streams: stock orders, by which dealers replenish their shelves; and emergency orders, in which an equipment owner or independent repair shop urgently requires a few special parts to repair a broken piece of capital equipment. Stock orders tend to be large and fairly predictable replenishments of popular consumables, while emergency orders are typically small (two to three pick-lines), unpredictable, and urgent, because expensive capital equipment is likely waiting for repair. Emergency orders are typically for items that are ordered infrequently (otherwise they could have been provided by the dealer from stock inventory). Such orders—a few, slow-moving items that must be picked immediately—are relatively expensive to handle. Worse, customers ordering for repair might order before they are absolutely sure which parts need replacement; and so there can be a significant percentage of returns to be handled at the warehouse.

For most product in a service parts warehouse there are not sufficiently reliable patterns of movement to justify special processes, but one can layout stock to be more space efficient by storing similar sizes together, thereby reducing travel. Furthermore, one can hedge chances of having to travel long distances. For example, it can be advantageous, especially for emergency orders, to store products together that are likely to be ordered together.

Another complication is that the life cycle of a service part is unusual, with three stages of product life, as shown in the next Figure.

Early failures are generally due to manufacturing imperfections; mid-life failures are generally due to random events that stress the part beyond its tolerance; and end-of-life failures are due to expected wearing out of the product. Demand for product generally reflects this pattern, and creates challenges in the warehouse. For example, there is little time to ramp up availability of new product at the start of its life cycle. Also, parts are more frequently requested at the end of the product life cycle, and so it is easy for the warehouse to be stuck with obsolete merchandise. Finally, it may be necessary to relocate product as its popularity changes.

A catalog fulfillment or e-commerce distribution center
typically receives small orders from individuals by phone, fax, or the Internet. Orders are typically small, for only 1–3 items, but there may be many such orders, and they are to be filled and shipped immediately after receipt. Because customer orders require instant response, such distributors typically try to shape demand by offering special prices for ordering at certain times or in certain quantities or for accepting more variable delivery dates.

A 3PL warehouse
is one to which a company might outsource its warehousing operations. The 3PL provider might service multiple customers from one facility, thereby gaining economies of scale or complementary seasons that the customers would be unable to achieve on their own. 3PL facilities may also be contracted as overflow facilities to handle surges in product flow.

A perishables warehouse
may handle food, fresh flowers, vaccines, or other product requiring refrigeration to protect its very short shelf life. They are typically one link in an extended cold chain, along which perishable product is rushed to the consumer. Such DCs are distinctive in that product dwells within for very short times, frequently only hours. Also, there is a great emphasis on using space effectively because, with refrigeration, it is so expensive. They face many challenges in inventory management, including requirements to ship product according to FIFO (First-In-First-Out) or FEFO (First-Expired-First-Out). Also, there are typically many restrictions on how product is handled. For example, chicken cannot be stacked on top of anything else, to protect against juices dripping onto product below and contaminating it. Finally, appropriate temperatures must be maintained and this can be different for different kind of products. A typical food DC operates separate areas for ambient temperatures, chilled (around 2 degrees C, 35 degrees F), and frozen product (-18 degrees C, around 0 degrees F). To protect stored product, it is important to avoid bringing in anything warmer. This type of warehouse will become more common as China, India, Brazil, and other rapidly industrializing countries build a middle class, which will increasingly want fresh fruit, vegetables, meat, and dairy.

While there are many types of warehouses in the supply chain, one of the main reminders is that there is a systematic way to think about a warehouse system regardless of the industry in which it operates.

What we need to realize is that the selection of equipment and the organization of material flow are largely determined by :

  • Inventory characteristics, such as the number of products, their sizes, and turn rates;
  • Throughput and service requirements, including the number of lines and orders shipped per day;
  • The footprint of the building and capital cost of equipment;
  • The cost of labor.

We also see that there is a regional difference in how to organize warehouse operations. Here is a brief survey:

North America

North America is driven by mass consumption. Think Wal-Mart. This enables huge economies of scale and, indeed, the trend has been for ever larger distribution centers and ever accelerating rates of product flow. As telecommunications enables better coordination along the supply chain, the uniformity of market and of distribution infrastructure allows fewer, more centralized and therefore larger distribution centers.

The Amazon.com distribution center shown in the following figure is typical:

One level, with conveyors and sortation equipment but little other significant automation. Such warehouses are generally built in the countryside surrounding major metropolitan areas, so that land is cheap but there is still ready access to large markets.

The fairly high costs of labor are held down somewhat by constant immigration into the US and Canada.

Warehouses in North America are coordinated by increasingly sophisticated warehouse management systems and so very rich data sets are available with which to evaluate and refine performance.

East Asia

Business in Asia has traditionally been based on personal relationships and less on computational models. Because of this tradition, data is not robust and not widely available; consequently the opportunities to improve operations by science are not fully developed at present.

In general, the most active economic areas are separated by lots of water, which means lots of product conveyed by air (for high-value or time-sensitive products) or ship (for bulky items or commodities). For both air and sea cargo, the large fixed costs increase incentives to consolidate freight. Consequently one expects to see the emergence of strong regional hubs, such as Singapore and Hong Kong, to support this consolidation.


India, like many developing countries, has both inexpensive land and low labor costs. Capital costs are relatively high in relation to the cost of labor and so there is less pressure to install specialized storage, even pallet rack. And because labor costs are low there is less incentive to increase efficiency. For example, it is not an attractive proposition to reduce labor costs by picking from flow rack: The labor savings cannot justify the cost of the rack or the forklift trucks.

In addition, warehouses in India distribute mainly to the local economy and so supply a market that is not wealthy. Consequently, the SKU’s are not likely to be high cost items and so there is not much savings available from reducing inventories by precise timing. Consequently information technologies cannot generate much savings.

Finally, inefficiencies in transport make India in effect a collection of smaller markets. These inefficiencies include the physical, such as roads in less-than-ideal condition, as well as the administrative. For example, each state within India levies customs duties on freight transported across the border. This slows interstate commerce and increases the expense. Such factors increase the costs of transportation and so favor a strategy of having more, smaller distribution centers rather than fewer, larger ones, where the volume of activity could better justify capital investment. The national government is attempting to revise its tax structure fix these inefficiencies.

Figure : The relatively low cost of labor, high cost of capital, and artificially small market mean that this warehouse in India may be economically efficient. (Photo courtesy of Rohan Reddy)

India is increasingly becoming a global sourcing hub and so suitable distribution centers are being built in around large ports, such as Mumbai (Bombay). However, land can be expensive there. Caleb Tan of Menlo Worldwide Logistics has observed prices comparable to those of Singapore or Hong Kong. Apparently this is due to a lack of land because of encroachment of slums as more and more people migrate from the countryside to economically vibrant areas.


A distinctive feature of the logistics scene in China is the seemingly boundless supply of very low cost of labor together with relatively inexpensive land. Consequently warehouses tend to be large, low buildings as in North America; but with some striking differences. For example, it is not unusual as of this writing to find a warehouse of 250,000 square feet with a single fork lift truck. The reason is that equipment is expensive but labor is cheap.

Figure : In the US warehouse on the left, cartons of beer have been palletized because labor is expensive compared to capital. The reduction in labor is worth the expense of a forklift plus the additional storage space. In the Chinese warehouse on the right, cartons have been stacked by hand and must be unstacked by hand; but labor is cheap and capital is expensive.

Despite cheap labor, China does have some capital-intensive warehouses, with the latest information technology and storage equipment. Such warehouses are most likely devoted to the distribution of high-value goods for export. Because such goods, such as consumer electronics, have high-value and short life-cycle, the warehouses can justify their equipment by substantial reductions in inventory costs.

The very different costs in the US and China sometimes leads to behavior that makes sense locally but may make the supply chain inefficient. For example, The Home Depot receives some Chinese-built product at its Import Distribution Center in Savannah, Georgia, USA. The shipping department in the warehouse in China de-palletizes freight in order to pack each trailer as tightly as possible for the drive to the sea port. Thus an expenditure of relatively cheap labor will reduce the relatively significant costs of equipment and transportation. But this means the product arrives in the US as loose cartons in containers and so The Home Depot must re-palletize the cartons before storage in deep, drive-in pallet rack. And most of it will, shortly after, be de-palletized once more when it is picked as cartons for shipment to stores.

Singapore, Hong Kong, Japan

Some economic powers such as Singapore, Japan, and Hong Kong suffer from limited space so land is much more expensive than elsewhere. Consequently, many of the warehouses are high-rise, such as shown here:

Figure :Multi-story warehouses are common in Singapore, Hong Kong, and Japan where land is expensive.

In addition, as first-world economies, labor in these places is expensive and so warehouses here are more likely to be automated. Freight elevators are likely to be bottlenecks to material flow in these facilities.

Space constraints have led to an interesting type of warehouse in Hong Kong and in Singapore: A multi-floor facility with no automation or elevators, but, instead, a spiral truck ramp so that trailers may be docked at any floor

Figure : Multi-story warehouse with no automation but accessibility provided by a spiral truck ramp.

In effect, each floor becomes a ground floor—but the cost is that significant land area, determined in part by the turning radius of a truck, is lost to the ramp and unavailable for storage. That storage space must be reclaimed up above.

This design is a clever and efficient way of using space if each floor is occupied by an independent tenant. But one must be careful if some tenant occupies multiple floors for then it may become necessary to shuttle trucks among floors. For example, a multi-story warehouse with spiral ramp would be unsuitable as a local distribution center: Trailers departing with small shipments for many customers must be loaded in reverse sequence of delivery to avoid double-handling. But since the load may not match the layout of product amongst the floors, this could require much shuttling of the trailer among floors to load.

Similarly, this might be an inefficient warehouse in which to receive shipments of diverse items that may be stored on different floors: Because rear-entry trailers restrict the sequence in which freight can be accessed, a trailer may have to shuttle among the floors to deliver all the freight to the appropriate places. Alternatively, the trailer would have to be loaded to match the allocation of product among floors at the warehouse. In either case, extra work is required.

Central and South America

This is a region of developing markets that are separated by geography such as the Amazonian rain forest and the Andes mountains. Consequently, markets tend to be relatively small and inappropriate for significant capital investment in warehouses.

Data may not be available or may not be transmitted to supply chain partners. In part this is because there has not been a strong, reliable stock market and so wealth has generally been invested in family businesses, which are less inclined to share data.

Labor costs are relatively low, but some segments of the workforce enjoy strong political protections, which results in labor inflexibility. Consequently, while labor can be cheap, it can be hard to shrink or re-deploy a workforce. Most of labor is devoted to handling small quantities, because retail stores are small and customers purchase tiny amounts daily.

Space is the main concern. Infrastructure is not well-developed and so, to reduce travel on bad roads, warehouses must locate close to the customer. But customers are concentrated in a few, very large cities such as Mexico City, Sao Paolo, and Lima, which are congested and where space is expensive.

Most warehouses in this region hold goods for domestic distribution. Because markets are not highly developed, inventory tends to be of less expensive goods and so the costs of moving inventory slowly are relatively low. (In contrast, China has many distribution centers supporting export of high-value goods, such as consumer electronics. The high cost of holding such inventory, justifies investment in facilities that enable rapid movement of product. This also holds for Mexico, which can get product quickly to market in the US and so able to justify advanced distribution centers.)

The following photograph reflects the low labor costs relative to the costs of capital in this region.

Figure: A ladder is much cheaper than a person-aboard truck, though much slower. (Note the product stored as loose cartons.)


Warehouses in Europe, especially in Germany and France, are shaped by the relatively high labor costs and inflexibility of the work force. These facts push designers to find engineering solutions rather than social solutions to logistics challenges. For example, there is a greater inclination to use automation than in comparable facilities in North America.

In the past, the economies of Europe were separate. More recently the economies are integrating into a common market, which will create economies of scale, which will likely lead to larger warehouses. However, urban areas, many of which have grown out of ancient towns, will still present challenges to the efficient flow of product. All this is reflected in Figure,

Figure: A highly automated distribution center in Germany. (Photo courtesy of Kai Wittek)

which shows a distribution center of a major drugstore chain in Germany. The multi-story portion of the building, visible in the background, houses a high-rise automated storage-and-retrieval system. This is not so much to conserve space as to reduce labor costs. (Unlike the Singapore warehouses, this facility is tall only where the AS/RS is installed.)

According to the Rossman web page, the AS/RS is 30 meters high, the aisles are 127 meters long and the facility provides 14,000 pallet positions. The delivery trucks in the foreground are relatively small, at least compared to the 48-foot trailers common in North America. The small trucks are necessary to deliver to stores in the centers of cities, the ground plans of which may have been laid out centuries ago and cannot accommodate large trucks. But the warehouse is large, reflecting the extent of the market it serves.


References : Copyright 1998–2011 John J. BARTHOLDI, III and Steven T. HACKMAN. All rights reserved. This material may be freely copied for educational purposes—but not for resale—as long as the authors’ names and the copyright notice appear clearly on the copies. Corresponding author: john.bartholdi@gatech.edu

Review of inventory II


The company behind the inventory II

Inventory II is an add-on module to Microsoft Dynamics AX, that focuses on improving and extending inventory management. Before I look deep into the product, I wanted to take a look at the developers of the module. The company behind the Inventory II is ‘FSB‘. FSB is the first letters of Flemming, Søren and Benny. For us, that have been with the Dynamics AX from the beginning, will recognize these developers, because they are the founding fathers of most of the costing, supply chain and production code in Dynamics AX. They worked closely together with Erik Damgaard to create to Axapta(That later became Dynamics AX). Much of the current source code of Dynamics AX originates from ‘FSB‘-team. This tells me that the Inventory II is made by developers with probably the best knowledge of how the inventory architecture in Dynamics AX is build up.

So what is it all about ?

First we need to understand how standard Microsoft Dynamics AX is maintaining on-hand and how the inventory values are tracked. In AX all transactions are stored in a table called inventTrans. Each time records are inserted, updated or deleted, these changes will update another table called InventSum. As the name suggests; InventSum contains a summary of the onhand per dimension(like color, size, location, warehouse, pallet etc). The data model can be visualized like this :

Normally this model works very fine, but there are some drawbacks in the model.

Very large joins

Since the table InventSum and InventDim table can be VERY large, I have often seen performance problems when these two tables are joined in queries. Often from a financial perspective you may want to get the inventory value in total per warehouse. At a customer site where they have approx. 13000 SKU’s and generate approx. 50.000 inventory transactions a day, they have around 60 million inventory transactions in total. What trying to get a total inventory value, the reports often needed to run for many hours. It seems, when you need to join the two very large tables, the performance of SQL drops drastically.

Reporting on historical values

Another issue is how to report inventory sum and values on a specific date. Like “1/1/2010”. I have looked deep into this, and see that the way std. AX is working, is to take the current InventSum, and “sum-reverse” inventory transactions posted between now and the wanted reporting date. If you have many transactions, you will see that this quickly will take a lot of time to make an overall reporting.

Database locking

At a customer site where we implemented Axapta 3.0, we had 18 packing stations, that packingslip and invoice updated sales orders all day long. The average size of the sales orders was 8 lines. What we very often experienced, was “blocking locks” at the packing stations when they did the postings. The reason was that when the packingstations invoice updated the orders, Axapta also did a update on the InventSum to reflect this posting. If this also happened on another packingstations, it very often could lead to a blocking lock, where the two packingstation was waiting for each other to complete. At that time we concluded that the main reason for this was the “UPDATE INVENTSUM” that std Axapta made. This process has been improved in AX 40, and AX 2009, but the update processes still there, that could lead to blocking locks.

Inventory value

With std. AX there is a need to run a periodic process called “inventory closing”. The inventory closing is the process of settling receipt cost values with the issue cost values. To say it simple; If the financial on-hand quantity is Zero, the financial value should also be zero. This is actually a great feature, because it allows you to sell the items before you receive the vendor invoice, and the real cost from the vendor invoice can be propagated to the respective sales orders/ issue transactions.
But what we see in practice is that the inventory closing is ran one a month, and then it could take several hours to complete. Also a lot of issues related to this has also been seen, which could result that the inventory values suddenly gets bigger than the US military budgetJ.

Level based reservations / intelligent reservation

For large distributors, reservations are a “must-have”. The ability to promise the customers that the order has been reserved almost guarantees that the customer will get the goods in time, and that no other sales order will “steel” the items. But many distribution centers differentiate between buffer and picking locations. In std. AX you could physical reserve the items, but this prevented the users from moving the goods from the buffer area to the pick area, where the items could be picked. The reason for this is that the reservation is “exact”. In Axapta 3.0 this resulted in that we needed to make a system called “virtual on-hand”, to maintain reservations, but still have the opportunity to move the items around. Another important feature I missed was the ability to control the allocation. So that some on hands dimensions are used before others. A scenario for this is if you have an automated warehouse area, and you want to control this area in relation to other area’s.

Inventory II

When we should upgrade the customer to AX 2009, we got to know the FSB, and got trained on the Inventory II product. WOW, did this solve most of our biggest problems. The solution was elegantly small, and easy deploy into the system. The Inventory II was also built into the To-Increase module “Warehouse Management & Distribution”. The architecture is a bit different, and can be visualized like this :

The complexity of on-hand has been replaced by a single table, called ImTrans. This system uses a ‘insert-approach’, meaning that there do not happen any SQL-“update” statements on any of the records. This way it removes any “blocking locks” and maximizes concurrency in the database when updating the on-hand values. FSB also introduced a system called “Watermark” to keep track of what records are valid for the current on-hand. If we take the process of a sales order going from “On order” to invoiced, the ImTrans would have transactions like this :

Step 1 : First transaction – Sales order line is created with 10 pcs of item V1. Inserted with an issue status of ‘on order’, and the watermark level is ‘W1’

Step 2 : Line is reserved; This results in a “reverse transaction” where the first transaction is eliminated (If you do a select sum() you would only get that 10 pcs is reserved physically)

Step 5 : Then we “jump” forward to the invoice step, and each time the previous statuses are reversed.

So the on-hand transactions are not a single record, but a SUM of the records. The watermark will make sure that you only fetch the latest values when you do reporting. So this solved the issue of the blocking lock when doing postings.

But will this not generate a gigantic table ? The answer is yes, it would, and the heart of the inventory II is therefore a background engine, that will eventually remove ImTrans records that no longer represents any valid data. In inventory II this engine can be visualized like this :

Here there is a parameter for how long the inventory II should keep the records in the intermediate status postings. FSB has also included a monitor for what the inventory II engine is doing :

But the engine does much more. It also do the cost settling, background reservations, compression, issue alerting and posting cost differenced to general ledger.

But for me feature I like the most is the level based reservation(Also called intelligent reservation), where sales order can be reserved on a warehouse at order intake, but not specify location, batch on the reservation. Later in the process when picking route has been created, the reservation can be tighten down to location etc. And at the end when the transaction is picked,(or posted) the final dimensions like pallet ID, serial ID etc is specified.

The nice thing is that the set up for this is dynamic, and is specified on the dimension group an item is related to:


With the experience I have had with Inventory II, I can truly recommend this solution. I would never go ahead to implement a high volume/transaction customer without inventory II, because I have truly felt the pain without it. The support is very good, and help is always near if you need it. The question is how will this change in AX 2012. As far as I have seen and heard, the inventory II features are still needed in AX 2012, and I know that the FSB guy’s are working on an AX 2012 build. I look forward test it out.

Bad bosses do, “tight lines”

Bad managers are often weak on communication – with poor performance on employee relations. This must often be compensated with unnatural hierarchies and “tight lines”. One does not become a better leader by turning a deaf ear.

Leadership is about to listen, analyze signals and to make decisions – whether they are popular or not. But the definitive answer for leadership does not exist. Management is also situation-oriented. The leader must deal with a story, an environment, a culture, individuals, its place in the “life cycle”, the market and much more. Being a good leader, requires expertise in a wide variety of disciplines. Not least, psychology and sociology.

We see many thoughts, theories and concepts related to management. Dear child apparently has many names. No matter what we call field of management, the management is a balancing act that involves many audiences. One should relate to a board, to other management and employees. Yes, let’s call it for internal communications. But one should also relate to external audiences. Be could be customers, potential customers, parters, owners and shareholders, regulators, journalists, and more.

Being a leader is so that one must master many disciplines, one must be both broad and deep. And you have to continuous speeding up and braking down. It is a great achievement to be a good leader.

Modern management is not only about involvement. One must care about people to get them to perform. And the staff must be very much feel they are listened to. The leader must be real – those who only play a role is in fact quickly discovered – and then threw out the door.

I work in IT-industry, and we work in a tremendous pace. Then the leader must also show willingness to change with an equally high speed. If not – it’s over and out.

Among the requirements is that the leader must be involved and receptive – and make their decisions based on a totality of ideas, arguments and values. Then it is important to listen to his subordinates, involving them in decisions and the courage to put the hierarchy aside for the best ideas.

It is also true that the strongest communities often employ the brightest minds. These are people with a commitment and that largely wants to have influence on their everyday life and its development. Modern leaders also attracts the best resources. They both courage and ability to hire the best people. The receptive and dynamic leader will both develop the company, employees and themselves. I believe it is this that gives companies and organizations progress and success.

The smartest leaders treat the employees as enthusiastic, intelligent and independent people. They are involved and they are strong enough to take decisions based on good processes.

Good management requires insight into the people and the courage to get involved, and that is what I feel I’m experiencing as Columbus IT Norway consultant. If you have a bright mind on ERP and Dynamics AX and to want to experience true leadership I encourage you to contact us.

Slotting ROI: Tangibles and intangibles

This is a brief rundown of possible benefits of slotting and its impact on several operational factors, from different sources and our own experience.
Some warehouses with few SKUs and trivial order lists may not need slotting at all. The larger and more complex a warehouse becomes, the more slotting will have an impact. Ranges of % in benefits are for typical warehouses, but can vary from 0% – to even more than what is stated. Values are extremely dependent on several interacting aspects of warehousing, most significantly: order profiles, diversity of SKUs, geometry of warehouse, the existing layout and slotting procedure.


For warehouses using many different products, mixing several storage systems becomes more cost-effective than using only one type of storage. However, rule-of-thumb systems are difficult to size and results can be far from the optimized solution, with productivity impacts up to 30-40% on replenishments and picking.
The optimal balance between types of racking can be determined using specialized software, evaluating the relative needs for replenishment, picking rates, space, flow-rack cost, etc.

Space Maximization:
Pre-slotting determines the optimal quantities and storage types for products based on their order profiles. This maximizes the warehouse cube, thereby cutting the square footage requirements with possible space savings in the 35% – 43% range.

– Bartholdi and Hackman, Warehouse & Distribution Science 2010
– Avery, Operations and Fulfillment, July 1999

Dynamic Slotting

Typically, static slotting for distance and velocity alone can reduce labor cost by 10%. Further savings of 5% can be achieved by using batch picking and product family grouping. Dynamic slotting uses floating-pick storage spaces, and uses specific-period strategies instead of product life-cycle strategies. Receiving and put-away are directed moves to specific locations. This results in sustained productivity, with measurable savings in operational costs from fixed slotting.

Space Utilization:  minimize allocated free space because of adjustable assigned volumes.

Put-Away: Directed put-away reduces guesswork and eliminate errors, and removes the need to search the warehouse for locations.

Replenishment: Synchronizing stock replenishment and space allocation can significantly reduce the number of stockouts, a time-consuming problem for high-throughput distribution centers. A case study showed results of up to 77% less stockouts.

Picking Efficiency:
Results indicate that order fulfillment time can be reduced by 20%. Using an optimal combination of picking policy (good slotting may optimize batch picking strategy); up to 50% in labor savings from traditional strategies can be reached.

Order completion and shipping: reduce management and order completion time, as picking is optimized.

Some Intangibles:
Many aspects of warehouse operations will experience reduced pressure because of smart slotting:
– put-away management
– order picking management
– shipping management
– improvements in key metrics (KPIs): picks per hour, cycle times, inventory turns, order accuracy, order checking.
– enhanced work environment and safety (ex.: high velocity items in safe locations)
– fewer material damages (less distance traveled, less material relocation and handling)
– equipment wear

[Petersen CG, Aase G, Int. J. of Production Economics, 2004]
[Launders, IIE Transactions, August 1996]  37% improvement cited
[Frazelle, 1990] 20-50% improvement in picking time.
[X.He , SP Sethi , J Optim. Appl. 2008ç
[Gagliardi, Ruiz, Renaud, 2008] 77% stockouts


Slotting has an impact on all of warehouse operations and KPIs: productivity, shipping, inventory, stocking, order cycle, storage. The typical distribution of cost in warehouse operations is [Frazelle, 2002]:
–    Order Picking: 50%
–    Shipping: 15%
–    Receiving: 15%
–    Storage: 20%
Adding the savings from all operations, it is a reasonable assumption that dynamic slotting will provide 10 to 30% cost reduction from a baseline operation.

Studies suggest that in a typical warehouse, less than 15% of SKUs are properly slotted. Once fully slotted, most warehouses would save 10 to 30% on operations. For a medium size distribution center with several thousands of SKUs, simulations also show 20% savings in total labor cost. Examples of 100% improvement in both productivity and response time have been reported. Given that planning cost and establishing a slotting strategy is minimal in capital investment and risk, this is a most profitable ROI.

Possible improvement on Warehouse KPIs:
–    Picking rate: 20-50%
–    Order completion time: 25%
–    Storage efficiency: 0-30%
–    Equipment usage cost: 25%
–    Material damage: 25%
–    Space utilization: 5-40%

Tips on improving Warehouse Operations

Some of the most costly processes in the warehouse are picking and pack out process. They also account for the majority of the warehouse operational time- Lets discuss some of the key aspects that should be considered to reduce cost and increase warehouse operations efficiency.

Location planning

    Deciding the location for placing the item plays a key role in increasing the handling efficiency. Following factors should be considered while assigning a location for a particular item, number of counts of the item, Item’s volume and weight, sales volume during normal and peak periods of warehouse operations, special handling requirements and if any value added services like kitting etc is required to be done.
    Typically products are slotted in the storage mediums like pallet racks, drive in or drive through racks and bins. Once location is designated the next thing would be the numbering and grouping of the locations. Numbering should be done based on the building, zone, level bay facing and aisle. This activity will help in increasing the picking efficiency and reduce the time spent by the pickers searching for products.


Item’s should be grouped intelligently like fast movers, slow movers and medium movers. One should place the fast moving item’s in the locations which are closest and easier to reach and in a place close to the order fulfillment stations, this will improve the time taken to fulfill an order. Similarly slow movers can be placed in remote slots inside the warehouse as it will not be affecting the average picking time since these are sparingly ordered.


Internal routines must be developed to replenish the fast moving items in the line locations this will help in reducing the time taken to pick as the pickers will not be going back with zero inventories in the designated locations. Warehouse operations can decide the basis for refill like need based or minimum stock level refill’s which will reduce the labor cost in doing refills.

Storage types

Couple of broader classifications available for storage of products is flow storage and static storage. This contributes a lot in improving the bottom line and reducing the time and labor for doing picking and replenishment’s. The main advantage of flow storage is that the products will be coming to the pickers and they don’t have to go in search of item’s. With the help of modern control systems any warehouses using the conveyors and carousels to bring the products to the place of order fulfillment stations will be able to show a much higher productivity. The capital expenditure done to accomplish this will make a lot of difference in the bottom line of warehouses. This one time expense can be justified if the warehouse experiences a lot of activity and involves a lot of labor for picking and packing.

Source : http://www.infosysblogs.com

KPI’s for a warehouse customer.

At a customer we have implemented a role center, so that all resources can see company the KPI’s for maximizing the throughput.  I wanted to share with you the KPI’s and the results achieved.
We decided to focus on 4 roles, with up to 6 KPI’s per role.  We also decided to have 6 KPI’s for role.  Here is what we implemented :

Customer service :
– Open sales orders : Total number of open sales orders
– Open orders where customer is stopped :  Worklist to get in payments from past due customers.
– Delivered not invoiced sales orders : Worklist to make sure as much as possible is invoiced as soon as possible.
– Reserved ordered sales orders : sales orders that depend on a purchase or a production.
– Delayed sales orders in relation to confirmed date: Worklist for setting focus on delivery time

Purchase :
– Not sent purchase orders : Worklist of purchases that has been created, but not sent to the vendor
– Open purchase orders : Just a total
– Not confirmed purchase orders – Worklist to get confirmation dates from the vendors
– Received but not invoiced – Worklist to pay vendors
– Received today
– Planned purchases : How many purchase order lines have the master planning recommended that is firmed today.

Order processing
– Invoiced today (# and sum) : A KPI to make awareness of that invoicing is the final measurement of the company, and critical important that this goes smoothly.
– Invoiced yesterday (# and sum) : KPI to measure if we are better than yesterday .
– Packingslips today : How many packing slip updates have been done sofar today
– Ready for picking : How many picking routes are complete reserved and can be printed and picked
– Picking Now : How many picking routes are currently being picked ?
– Picked sales orders : Hos many picking routes are completed, and awaiting packing ?

– Productions ready for picking : Number of productions, where all BOM-items have been physically reserved, and just waiting for picking the BOM-items.
– Ended productions today : How many productions has been completed so far today
– Started productions : How many productions is currently started ?
– Delayed productions – How many productions are delayed according to the production finishdate(Delivery date)
– Planned productions : How many productions should be created from master planning today.

Since much of the order processing is automated, and sales picking routes are released automatically, the workforce can focus on the KPI that leads to that the system can generate as many picking routes as possible.  Then the workforce is focused on the physical work, and focus is always to maximize the productivity.
The result is a reduction of internal lead time with 2 days, No more add-hoc overtime, and a much more focused workforce.  Visibility is the key to success.

Recommended blog on Dynamics AX traning

Hi.  There is a blog that I would recommend a free blog that gives a very good insight to the main supplychain processes in standard Dynamics AX.
Take a look at the blog http://www.dynamicsaxtraining.com/ that Volodymyr Myronenko is maintaining.   I recommend all new Dynamics AX consultants and developers to look into this blog.  It sure give a overall starting point.
Happy blogging.