Warehouse activity profiling

What you do not measure, you cannot control Tom Peters

Provenance gap. This quotation and attribution are preserved verbatim from the previous deck. No authoritative primary source was recovered during this migration.

🧠 Learning objectives

• Analyze warehouse inventory, order, and storage activity from different perspectives.
• Recognize the importance of profiling to improve warehouse design, planning, and operation.
• Identify relevant input data necessary for profiling.

Profiling foundations

01

Why profiling?

Warehouse activity profiling and data mining is the systematic analysis of item and order activity, seeking to identify and take advantage of patterns of opportunity. Frazelle (2016)

Profiling can help to:

• Highlight the root cause of material and information flow problems.
• Pinpoint major opportunities for process improvements.
• Give managers and designers a presentation of the warehouse activity in their terminology.
• Provide an objective basis for project-team decision making and warehouse reengineering (e.g., storage system design).

Imagine that you were sick and went to a doctor for a diagnosis and prescription. When you arrived at the doctor’s office, he already had a prescription waiting for you, without even talking to you, let alone looking at you, examining you, or doing blood work. In effect, he wrote your prescription with his eyes closed and a random prescription generator. Needless to say, you would not be going back to that doctor for treatment.

Unfortunately, the prescriptions for many sick warehouses are written and implemented without much examination either.

For lack of knowledge, lack of tools, and/or lack of time, many warehouse projects launch without any understanding of the root cause of the problems and without exploration of the real opportunities for improvement. Warehouse activity profiling and data mining is the systematic analysis of item and order activity, seeking to identify and take advantage of patterns of opportunity. Our pattern recognition algorithms highlight the root cause of material and information flow problems, pinpoint major opportunities for process improvements, and provide an objective basis for project-team decision making. I will start with some of the major motivations and potential roadblocks to successful profiling. Then I will review a full set of example profiles and their interpretations. The examples will serve to teach the principles of profiling and as an outline for the full set of profiles required for warehouse reengineering.

This is another motivation for the profiling exercise—to give managers and designers of the warehouse operations a presentation of the activity of the warehouse in their terminology.

— World-Class Warehousing and Material Handling (Frazelle 2016, 36, 87).

Profiling principles

The P’s and P’s of Profiling (Frazelle 2016):

• Preparation
• Power
• Participation
• Probity
• Purpose
• Principle
• Pictures
• Perspectives
• Pitfall

The P’s and P’s of Profiling

I have been leading, facilitating, researching, and teaching activity profiling and data mining for more than 20 years. I recently summarized our lessons learned as the P’s and P’s of profiling.

— World-Class Warehousing and Material Handling (Frazelle 2016, 37).

Preparation of profiling

Profiling stimulates thinking and promotes problem-solving.

• Analyzing customer/purchase orders, item activity, inventory levels, etc. improves understanding of the underlying activities.

The Preparation of Profiling

The first thing I do before writing a new article is to read what others have written about the topic. If I am preparing to teach a class or a seminar, I do the same thing: I review what others have prepared on the topic to stimulate my thinking. Reviewing activity profiles works the same way; it is like reading an illustrated journal of the warehouse activity. As you study the profiles of customer orders, purchase orders, item activity, inventory levels, and so on, the creative juices should begin to flow as your understanding of underlying activity improves.

— World-Class Warehousing and Material Handling (Frazelle 2016, 37).

Power of profiling

Based on warehousing activity, profiling helps to reveal smaller sets of:

• Candidate process flows (e.g., which order-picking methods based on product characteristics?).
• Candidate material handling systems.

In warehouse reengineering projects, profiling quickly eliminates process and equipment options that do not warrant consideration.

The Power of Profiling

Done properly, the patterns in profiling quickly reveal a small set of candidate process flows and material handling equipment types for each warehouse activity. Conversely, profiling quickly eliminates process and equipment options that really do not warrant consideration.

Many warehouse reengineering projects go awry because we waste valuable resources analyzing concepts that were doomed from the start.

the patterns in profiling quickly reveal a small set of candidate process flows and material handling equipment types for each warehouse activity.

— World-Class Warehousing and Material Handling (Frazelle 2016, 37).

Participation of profiling

Profiling involves key stakeholders in the design process. People from many affected groups will:

• provide data,
• verify/rationalize data,
• and help interpret results.

The Participation of Profiling

Profiling involves key stakeholders. During the profiling process, it is natural to ask people from many affected groups to provide data, to verify and rationalize data, and to help interpret results. To the extent those stakeholders have been involved, they have helped with the design process.

— World-Class Warehousing and Material Handling (Frazelle 2016, 38).

Probity of profiling

Profiling supports and promotes objective decisions.

The Probity of Profiling

Profiling supports and promotes objective decisions. I worked with one client whose team leader we somewhat affectionately called “Captain Carousels.” No matter what the data said, no matter what the order and profiles looked like, no matter what the company could afford, he was going to have carousels in the new design.

Example: Captain AGV

— World-Class Warehousing and Material Handling (Frazelle 2016, 38).

Purpose of profiling

Profiling helps to better plan and design warehouses based on distributions (not averages!).

Figure 1: N. of items/order distribution completed for a small mail-order company. The average #items/order is 2 — but it has never happened! (Frazelle 2016)

The Purpose of Profiling

You will see myriad complex statistical distributions in our journey through warehouse activity profiling. Why go to all the trouble? Suppose that we are trying to determine the average number of items on an order. Figure 2.1 shows that 50 orders are for one item, 0 are for two items, and 50 are for three items. What is the average number of items per order? It’s two. How often does that happen? It never happens! If we are not careful to plan and design based on distributions as opposed to averages, our plans and designs will be flawed.

— World-Class Warehousing and Material Handling (Frazelle 2016, 38, 39).

Principle of profiling

Activity profiling and data mining are the repeated and logical application of Pareto’s law¹ in warehousing:

• Few SKUs generate most of the activity.
• Few SKUs represent most of the inventory.
• Few customers generate most picking and shipping activity.

The Principal and Principle of Profiling

Vilfredo Pareto was an Italian economist and gardener. Pareto’s law stems from his observation that 80 percent of the land was owned by 20 percent of the population and that 20 percent of the peapods in his garden yielded 80 percent of the peas. Joseph Juran called this principle the “separation of the vital few from the trivial many.”

Activity profiling and data mining are essentially the repeated and logical application of Pareto’s law, sometimes referred to as the 80/20 principle or ABC analysis. Examples of Pareto’s law working in warehousing include ■ A minority of the items generate a majority of the activity. ■ A minority of the items are responsible for a majority of the inventory. ■ A minority of the customers generate a majority of the picking and shipping activity.

— World-Class Warehousing and Material Handling (Frazelle 2016, 38).

1. Pareto’s law: 80/20 principle or ABC analysis, “separation of the vital few from the trivial many”.

Pictures of profiling

Wise, data-driven, consensus decisions are facilitated with pictures.

The Pictures of Profiling When you see a picture you experience a thousand simultaneous thoughts. We are aiming for the same effect in warehouse activity profiling as we paint a representative picture of warehouse activity. In profiling, we capture the activity of the warehouse in pictorial form so that we can quickly develop wise, data driven, consensus decisions.

— World-Class Warehousing and Material Handling (Frazelle 2016, 39).

Perspectives of profiling

• Interactive data visualization (dynamic charting controlled by the user).
• Simulation (2D representation of the physical warehouse with the activity profiles moving warehouse objectives according to the profiles).
• Animation (3D simulation).

The Perspectives of Profiling If a picture is worth a thousand words, then a video is worth a thousand pictures. Interactive data visualization, simulation, and animation are the closest media we have to video for activity profiling. Interactive data visualization (Figure 2.2) is simply dynamic charting controlled by the user. Simulation (Figure 2.3) is typically a two-dimensional representation of the physical warehouse with the activity profiles moving warehouse objects according to the profiles. Animation (Figure 2.4) is three-dimensional simulation.

— World-Class Warehousing and Material Handling (Frazelle 2016, 40).

Pitfall of profiling

Paralysis of analysis: drowning in profiling and forgetting to solve the problem.

The Pitfalls of Profiling

One warning before we begin to profile the warehouse (as an engineer and logistics nerd, I fall into this trap a lot): We can drown in our own profiles. Some people call this paralysis of analysis! If we are not careful, we can get so caught up in profiling itself that we forget to solve the problem.

Good example: “Productivity porn”

— World-Class Warehousing and Material Handling (Frazelle 2016, 40).

Basic statistics

The key facts to learn in activity profiling (Bartholdi and Hackman 2019):

• What is the business? Who are the customers? What are the service requirements? What special handling is required?
• Area of warehouse, types of storage and material handling equipment.
• N. of SKUs.
• Avg. n. of pick-lines shipped per day.
• Avg. n. of units (pieces, cases, pallets) per pick-line.
• Avg. n. of customer orders shipped in a day.
• N. of order-pickers.
• N. of shifts devoted to pallet movement, case-, and broken-case picking.
• Avg. n. of shipments received in a day.
• Average rate of introduction of new SKUs.
• Seasonalities.

Orders and warehouse data

02

Parts of an order

An order is made up of order lines. Example:

A Buyer creates an order for three computer monitors, three keyboards, and three printers. When looking at the entire order you are at the order header level. The monitors, keyboards, and printers are the order lines with a quantity of 3 each specified.

Example of order header attributes IBM - Order Management System:

• Bill to information (e.g., street address where the invoice is sent)
• Payment information (e.g., credit, debit)
• Additional attributes (e.g., order identification, order creation, shipping, and financial information)
• Exchange type, if the order is an exchange order. It can be a regular exchange, advanced exchange, or a pre-paid exchange.

Attributes of order header and line:

• Shipping address
• Personalization instructions (e.g., gift wrapping)
• Shipping and handling costs
• Price Promotions

— Warehouse & Distribution Science (Bartholdi and Hackman 2019, 237–40).

Stock keeping unit (SKU)

• Item type with unique code.
• Give idea of inventory diversity, for example:
  • Many SKUs, and few items
  • Few SKUs and many items
• Differs from product numbers (e.g., serial numbers) assigned by a third-party (e.g., manufacturer).

Example of SKU code and universal product code (UPC) (RedstagFulfillment)

• Why Inventory Should be Digital - AMS (amsc-usa.com)

Provenance note. This historical supplementary link is preserved for context and is not used as academic evidence.

How to create SKU codes?

A SKU code could include the product’s broad category, variation, and specific details.

Figure 2: Example of SKU code. Broad category=TS, variation=BLM (“BL” = color blue, “M” = size M), specific detail=A101 (“A” = brand, “101” = series) (Amazon)

Examples:

• CF228356 = Champagne Flute size 2 (medium), from manufacturer 283, style 56 (RedstagFulfillment).
• UDA-BL-10 = UnDer Armour shoe, color BLue in size 10 (Symbia Logistics).

[[Pasted image 20230501134643.png]]

SKU: Definition and How To Set Up Codes [+ Tips and Examples] (amazon.com.sg) How to Name Your SKUs (With Examples) | Symbia Logistics What Is A SKU Number And How Is It Used? (redstagfulfillment.com)

Provenance gap. The original source contains an unresolved pasted-image placeholder. No corresponding image or source metadata was recoverable from commit 177b897.

Order line

An order line is a line on the order containing information relative to a specific item (SKU) being ordered.

Example of order line attributes from IBM - Order Management System:

• Item identifier (SKU)
• Unit of measure, such as a single unit or a dozen
• Quantity of the line item ordered
• Order line status (e.g., created, shipped)
• Inventory attributes (e.g., lot number, revision number)
• Serial number
• Pricing information (e.g., unit price for the line item)
• Mark for address
• Gift attributes, if the order line is a gift

Data sources

• SKUs
• Storage conventions
• Order history
• Layout
• Location addresses
• Storage unit data

SKUs

Useful information include:

• Unique ID (allows connecting with other data sources)
• Short description (useful in validation and error checking)
• Product family (relevant for storage and/or handling).
  • An SKU might be in more than one product family.
  • Industry dependent, for example:
  • Grocery distributor: dry goods, dairy, produce, refrigerated, frozen, etc.
  • Candy distributor: chocolate (sensitive to heat), mint-flavored candies (odoriferous), and marshmallow (light and tends to absorb the smells of its neighbors), etc.
  • Apparel distributor: garment type, mill, style, color, or size.

The SKU master links product identity to descriptions and product-family attributes used for validation, storage, and handling decisions.

— Warehouse & Distribution Science (Bartholdi and Hackman 2019, 237–40).

SKU data for validation and capacity

Useful SKU information also includes:

• Addresses of storage locations within the warehouse (e.g., zone, aisle, section, shelf and bay/ position on the shelf).
• Date introduced (SKUs may be underrepresented in activity because newly introduced)
• Max. inventory levels by month or week (helps determine how much space must be provided for this SKU)
• Scale of the selling unit, such as cases or pieces (useful for validation and error-checking)

This continuation preserves the capacity- and validation-related SKU fields from the previous slide. These fields connect product identity to storage space, lifecycle effects, and the warehouse’s selling-unit conventions.

— Warehouse & Distribution Science (Bartholdi and Hackman 2019, 237–40).

Warehouse conventions on packing data

How to distinguish between different types of storage units and selling units?

For example, Walmart’s hierarchy of packaging has four levels:

• EACH: Unit intended for individual sale.
• PACK: Group of individual units, together in one package.
• CASE: Standard shipping unit.
• PALLET: Load of several CASES.

The four labels provide a familiar example, but their operational meanings must be checked against the facility’s item master and pick-ticket conventions.

— Warehouse & Distribution Science (Bartholdi and Hackman 2019, 259).

Packing terms need local definitions

Figure 3: Walmart’s hierarchy packaging conventions (source: 8th&Walton)

Caution: While each of the terms (i.e., each, box, inner pack, case, shipping unit) are commonly used, there is no convention as to which level of packing they apply.

It is particularly important to understand the conventions the warehouse uses to distinguish among different types of storage units and selling units. For example, the word “case” is often called by other names (“carton”, “box”) and, depending on its use, can have substantially different meanings for a warehouse. For example, a vendor may ship a case that contains several inner packs each of which contains several boxes each of which contains pieces (Figure 2.2). A standard example is an office products distributor supplying a standard type of ball point pen. The manufacturer may supply the product 12 pieces to a box (as you find it in the store), 12 boxes to an inner pack (stored in a thin carton container), and 4 inner packs to a case for a grand total of 576 pens in the vendor’s case or shipping unit. While each of the terms each, box, inner pack, case, shipping unit are commonly used, there is no convention as to which level of packing they apply. It is important to understand how this packing data is stored in the database. Often, the retail customer (of the distributor) is required to purchase an integer multiple of a selling unit. For the pen the selling unit may be an each, which means that a customer can actually order less than a full box of 12 pens. In the database this information may be stored as either the number “1” or by the symbol “EA” for each. If the customer is required to purchase boxes, then the selling unit may be listed as “12” or “box”. Now suppose the database records a customer purchase of 12, which appears on the order picker’s pick ticket? What exactly does this mean: 12 pens (1 box) or 12 boxes? If you think it means 12 pens when in fact it means 12 boxes, you would be underestimating demand by a factor of 12. If the manufacturer sells the pens as pieces, accounting will record transactions in units of a single pen, notwithstanding the restriction on the outbound side on how it is to be resold. To ensure consistency, one could always record demand in the smallest physical unit, pens, but this would require an order picker to know that 156 = 13 boxes, for example. To facilitate picking accuracy, the demand may be recorded as 13, and the order picker would know that this means 13 boxes. (Of course, if another pen is resold as pieces, this would lead to confusion.) To avoid this confusion, many facilities separate how demand is recorded for accounting purposes from how it is presented on a pick ticket, e.g., the pick ticket should say something like demand = 156 = 13 boxes of 12 each. There is another reason why such packing data is important. It is always more efficient to store and handle product in some kind of easily-held container as opposed to loose pieces. In the case of the pen this handling unit may be the box of 12 or the inner pack of 12 boxes. For purposes of restocking a shelf of product it would be much easier to restock in units of the inner pack. For purposes of space efficiency on the shelf and order picking efficiency, it would be better to store the product as boxes neatly stacked. The sku data may reside in different databases within a company and so it can present a challenge to collect it all. As a general rule, if you think there is the smallest chance that some data may be relevant, collect it!

— Warehouse & Distribution Science (Bartholdi and Hackman 2019, 259).

Order history

Concatenated shopping lists submitted by all customer. Information contains the following:

• SKU id
• Customer
• Special handling
• Date/time order picked
• Quantity shipped

The order history is primarily financial (not operational) information. It can be validated using data on the lines shipped each day.

Order history The order history is simply a concatenation of all the shopping lists submitted by all the customers during the preceding year. It contains the following information. - Unique ID of this order, to distinguish it from the shopping lists of other customers and from the shopping list of the same customer on another day or later on the same day - Unique ID of sku, which allows us to look up the sku to see where it is stored - Customer - Special handling - Date/time order picked - Quantity shipped

For analyzing warehouse operations you have to be careful where you obtain this data. Often this data is from a sales transaction database, which tracks financial rather than warehouse events. Consequently, the date recorded may represent the date the order was placed or when it was printed, not when it was processed in the facility. Similarly, the order that appears in the sales transaction database may actually have been processed at another facility. Generally, though, the information is available somewhere because each day the order pickers had to know what to pick. Keep in mind that an order history is primarily financial information. This is good in that it is likely to be very accurate; but it can also be misleading because the transactions it represents are financial and not necessarily operational. For example, it can happen that a sku is shown to have been requested in a negative amount; but this generally means something like the item was returned and restocked to the shelf. There is a simple check as to whether the order data received is approximately correct. Most companies keep track of the lines shipped each day. As a very first validation check, count the number of lines in the database by time period. These numbers should closely match what is recorded. (If you are obtaining only what personnel believe has been shipped, do not be surprised if the numbers obtained through careful processing of a database are substantially different.) The order data will be the largest file you must manage. As a rough estimate expect about 50 bytes per line. The number of lines can range from 2,000–8,000 lines per day (0.5–2 million lines per year) for a moderately active facility (for example, office product, fine paper, telecommunications distribution) to 10,000–40,000 lines per day (2.5–10 million lines per year) for an extremely active facility (for example, service parts, retail drug) to more than 80,000 lines per day (20 million lines per year) for the most active facilities (for example, pharmaceutical or catalog distribution). Consequently, a year’s order data could exceed 100 megabytes.

— Warehouse & Distribution Science (Bartholdi and Hackman 2019, 260).

Warehouse layout

A map of the warehouse.

• Help estimating traveled distances to retrieve products.
• Least standardized information (blueprints, sketches, etc.).

Figure 4: Example of warehouse layout (Anylogic)

Warehouse layout and location addresses A map of the warehouse allows us to see where each sku is stored. We can infer that an order-picker had to travel to this location to retrieve the product; and from the map we can infer something about the required travel. This will enable us to evaluate alternative layouts and warehouse designs. This type of information is generally least standardized and may be found in the form of blueprints, sketches, CAD files such as dwg format, and so on.

— Warehouse & Distribution Science (Bartholdi and Hackman 2019, 261).

Warehouse location addresses

Addresses identify storage locations using terms such as: floor, zone, area, section, aisle, unit, row, bay, level, shelf, bin, space.

(a) Shelf address

Bin address

Figure 5: Example of storage location ID (SKUVault)

2D representation

Figure 6: Creating a 2D warehouse heatmap in Excel WarehouseBlueprint

Excel example: sample-warehouse-map.xlsx (live.com)

Wh Sci: Warehouse heat map (warehouse-science.com)

How to use the program

1. Draw a map of the warehouse within a spreadsheet and label every section of rack/shelving with a unique address. Save the result in xlsx format (the newest MS Excel format), as in this example. (You need do this only once.)
2. Prepare a location-based statistic that you want to see, such as pick-lines per section of rack/shelving. This should be formatted as a tab-delimited text file with each line containing the unique address of one section of rack/shelving, followed by a tab, followed by the statistic of interest, such as this example, that lists the number of picks at each location during an interval of time.
3. Start the Heat Map program and follow the three steps (load the data file, choose the color scheme, color and save the map).

Tips

• The program colors a copy of your map. The spreadsheet that holds your warehouse map is not altered in any way and so can be used again later.
• The program colors only cells that contain an address that matches one in the data file. Nothing else is changed.
• Write the addresses of the rack/shelves exactly the same way in both the spreadsheet map and the data file. The program will treat “A-213-18” as a different address than “a-213-18”. Also, note that the program expects to find the addresses in the spreadsheet as text. It will ignore any entry, such as “21318”, that contain only numeric characters.
• Besides coloring the map, the program helps identify data flaws by listing any duplicate location addresses as well as any addresses in the data that are not found on the map.
• Here are some additional controls that you may find useful:
  • Click on the table headings of the input data (panel 1) to sort location addresses alphabetically or the statistic by size. One click sorts in ascending order; a subsequent click sorts in descending order; a third click returns to the initial sequence.
  • Control-click on the chart (panel 2) to copy, save, print, zoom or re-format it.

Remember that you can display any location-based statistic. Figures 1-4 show some examples.

The linked spreadsheet is the Warehouse Science sample warehouse-map teaching artifact. See the surrounding layout and address discussion in Warehouse & Distribution Science (Bartholdi and Hackman 2019, 261–63).

3D representation

Figure 7: Creating 3D warehouse in Power BI

Storage unit data

For each location at which a SKU is stored:

• Unique address
• Scale of the storage unit, such as pallets or cases (useful in validation and error checking)
• Physical dimensions of the storage unit (length, width, height, weight) (useful in understanding space requirements)
• Selling unit IDs and quantities

(bol.com max. 2 items)

Sku data

Useful information to gather about each sku include - A unique ID that distinguishes it from all other skus, which allows us to connect this data with that from other sources - A short text description, which is useful in validation and error checking - Product family, which may have implications for storage and/or handling. These tend to be particular to an industry and so require knowledge of the context. For example, product families for a drug store chain might include hair care products, dental products, shaving products and so on, which are displayed together at the retail store. For a grocery distributor product families might include dry goods, dairy, produce, refrigerated, frozen and so on. For a candy distributor product families might include chocolate (sensitive to heat), mint-flavored candies (odoriferous), and marshmallow (light and tends to absorb the smells of its neighbors), and so on. For an apparel distributor product families might include garment type, mill, style, color, or size. Note that a sku might be in more than one product family. - Addresses of storage locations within the warehouse. This might include zone, aisle, section, shelf and position on the shelf. - For each location at which this sku is stored - Scale of the storage unit, such as pallets or cases. This is useful in validation and error checking - Physical dimensions of the storage unit (length, width, height, weight), which are useful in understanding space requirements. - Scale of the selling unit, such as cases or pieces, which is useful for validation and error-checking – Number of selling units per storage unit. This could be 1. - Date introduced, which helps identify skus that may be underrepresented in activity because newly introduced - Maximum inventory levels by month or week, which helps determine how much space must be provided for this sku

It is particularly important to understand the conventions the warehouse uses to distinguish among different types of storage units and selling units. For example, the word “case” is often called by other names (“carton”, “box”) and, depending on its use, can have substantially different meanings for a warehouse. For example, a vendor may ship a case that contains several inner packs each of which contains several boxes each of which contains pieces (Figure 2.2). A standard example is an office products distributor supplying a standard type of ball point pen. The manufacturer may supply the product 12 pieces to a box (as you find it in the store), 12 boxes to an inner pack (stored in a thin carton container), and 4 inner packs to a case for a grand total of 576 pens in the vendor’s case or shipping unit. While each of the terms each, box, inner pack, case, shipping unit are commonly used, there is no convention as to which level of packing they apply. It is important to understand how this packing data is stored in the database. Often, the retail customer (of the distributor) is required to purchase an integer multiple of a selling unit. For the pen the selling unit may be an each, which means that a customer can actually order less than a full box of 12 pens. In the database this information may be stored as either the number “1” or by the symbol “EA” for each. If the customer is required to purchase boxes, then the selling unit may be listed as “12” or “box”. Now suppose the database records a customer purchase of 12, which appears on the order picker’s pick ticket? What exactly does this mean: 12 pens (1 box) or 12 boxes? If you think it means 12 pens when in fact it means 12 boxes, you would be underestimating demand by a factor of 12. If the manufacturer sells the pens as pieces, accounting will record transactions in units of a single pen, notwithstanding the restriction on the outbound side on how it is to be resold. To ensure consistency, one could always record demand in the smallest physical unit, pens, but this would require an order picker to know that 156 = 13 boxes, for example. To facilitate picking accuracy, the demand may be recorded as 13, and the order picker would know that this means 13 boxes. (Of course, if another pen is resold as pieces, this would lead to confusion.) To avoid this confusion, many facilities separate how demand is recorded for accounting purposes from how it is presented on a pick ticket, e.g., the pick ticket should say something like demand = 156 = 13 boxes of 12 each. There is another reason why such packing data is important. It is always more efficient to store and handle product in some kind of easily-held container as opposed to loose pieces. In the case of the pen this handling unit may be the box of 12 or the inner pack of 12 boxes. For purposes of restocking a shelf of product it would be much easier to restock in units of the inner pack. For purposes of space efficiency on the shelf and order picking efficiency, it would be better to store the product as boxes neatly stacked. The sku data may reside in different databases within a company and so it can present a challenge to collect it all. As a general rule, if you think there is the smallest chance that some data may be relevant, collect it!

— Warehouse & Distribution Science (Bartholdi and Hackman 2019, 259).

Activity analysis

03

Where and when is the work?

Work = customer orders = shopping lists comprised of pick lines

A pick line generates travel to the appropriate storage location and subsequent picking, checking, packing, shipping.

How work is distributed among

• SKUs

• Product families

• Storage locations

• Zones of the warehouse

• Time (time of day, days of the week, weeks of the year, and so on)

• How many times was an SKU requested? (how many orders did it appear?)

• How much of an SKU sold? (how many pieces, cases, or pallets moved through the warehouse?)

Picking method

• Broken-case pick: order quantity is less than a full case.
  • Depending on the packaging, inner-pack pick is also possible.
• Full-case pick: order quantity is an integer multiple of a case but less than a pallet (unit) load.
• Pallet pick: order quantity multiple of pallet-load quantity.

If normalized by quantity handled, broken-case picks take more time to process.

Example: Heatmap of pick frequency

Figure 8: Shelf sections colored in proportion to the frequency that SKUs are retrieved. Popular SKUs are stored throughout the warehouse leading to extra labor and response time. (Bartholdi and Hackman 2019)

— Warehouse & Distribution Science (Bartholdi and Hackman 2019, 264).

A tutorial on how to generate this heatmap can be found here.

Warehouse activity profiles

04

Warehouse activity profile hierarchy

Figure 9: Warehouse activity profiles divide into order and item perspectives, with specialized diagnostic profiles under each branch (Frazelle 2016, 44).

2.2 Warehouse Activity Profiles

Over the years we have developed a structured and comprehensive suite of activity profiles to address the major planning, design, and management decisions in a supply chain strategy. The profiles are constructed with our proprietary pattern recognition algorithms and are fed from a supply chain data warehouse (Figure 2.5). The warehouse activity profiles are organized according to Figure 2.6.

— World-Class Warehousing and Material Handling (Frazelle 2016, 44).

Customer order profiles

What do customers want from a warehouse?

• Customers want their orders filled in an accurate, timely, and cost-effective manner.

Material and information should flow through a warehouse to facilitate excellent customer service.

Order Profiles

Order profiles include customer order profiles, purchase order profiles, and inbound/outbound transportation order profiles. They jointly portray the major patterns of warehouse activity. Time and space do not permit a full description of each of the four major profiles. I selected customer order profiles as the context for our presentation on order profiles. Material and information should flow through a warehouse to facilitate excellent customer service. What do customers really want from a warehouse? They want their orders filled in an accurate, timely, and cost-effective manner. Accordingly, the first thing we must understand to plan and design warehouse operations is the profile of customers and their orders

— World-Class Warehousing and Material Handling (Frazelle 2016, 40).

Order profiles include customer, purchase, and inbound/outbound transportation order profiles. They jointly portray the major patterns of warehouse activity.

Customer Paretos

Tyipically, in warehousing operations, a small group of customers:

• Place high demands on a warehouse.
• Represent a large portion of the activity of the warehouse,
• Have demanding customer-service requirements.

Figure 10: Customer Pareto for a food and beverage company. Few customers (3) generate most of picking and shipping activity (50%). Having dedicated zones for them within the warehouse may reduce travel time and improve customer service (Frazelle 2016)

Customer Paretos

Some customers place such high demands on a warehouse, represent such a large portion of the activity of the warehouse, and have such demanding customer-service requirements that it may make sense to establish a separate area within the warehouse for those customers. For example, one of our apparel manufacturing clients does so much business with JC Penney that they have a JC Penney warehouse within their warehouse. It is not uncommon for consumer products and food manufacturing companies to have areas within their warehousing dedicated to Wal-Mart activity.

The customer pareto in Figure 2.7 is from one of our food and beverage clients. Note that just three customers make up 50 percent of our client’s distribution center activity. Accordingly, we assigned each of those customers their own zones in the warehouse to reduce travel time and improve customer service.

In another example (Figure 2.8), in a large publisher’s distribution center, a central pool of reserve stock is used to support three distinct internal customers/business units—retail, trade, and periodicals. We designed the central, shared warehouse with allocated, random storage and the forward picking areas with dedicated, distinct forward picking zones. The manager of each forward picking zone has a dotted-line reporting relationship with the business unit to which his or her picking zone reports. The solid-line reporting relationship is to the director of distribution. This warehouse-within-a-warehouse design yielded the best of both worlds—shared receiving resources, efficient handling of central inventory, dedicated forward picking lines, shared shipping resources, and business unit accountability. The warehouse-within-a-warehouse design philosophy works because small warehouses, in general, have higher productivity, response time, and accuracy performance than large warehouses.

Many of the customer order and item-activity profiles identify opportunities to subdivide a warehouse operation into self-contained warehouse processing cells, virtual warehouses, or warehouses within the warehouse. A warehouse within a warehouse is similar to a flexible manufacturing cell in a factory, where the productivity, quality, and cycle time are much better than in the factory as a whole.

— World-Class Warehousing and Material Handling (Frazelle 2016, 43, 45).

Warehouse-within-a-warehouse

Many of the customer-order and item-activity profiles identify opportunities to subdivide a warehouse operation into

• self-contained warehouse processing cells,
• virtual warehouses, or
• warehouses within the warehouse.

For example:

• Consumer products and food manufacturing companies frequently have areas within their warehousing dedicated to Wal-Mart activity.

Warehouse-within-a-warehouse works because small warehouses, in general, have higher productivity, response time, and accuracy performance than large warehouses.

• A warehouse within a warehouse is similar to a flexible manufacturing cell in a factory, where the productivity, quality, and cycle time are much better than in the factory as a whole.

The warehouse-within-a-warehouse design philosophy works because small warehouses, in general, have higher productivity, response time, and accuracy performance than large warehouses. Many of the customer-order and item-activity profiles identify opportunities to subdivide a warehouse operation into self-contained warehouse processing cells, virtual warehouses, or warehouses within the warehouse. A warehouse within a warehouse is similar to a flexible manufacturing cell in a factory, where the productivity, quality, and cycle time are much better than in the factory as a whole.

— World-Class Warehousing and Material Handling (Frazelle 2016, 46).

Warehouse-within-a-warehouse (design example)

Figure 11: Shared receiving resources, efficient handling of central reserve stock, dedicated forward picking lines within business units (BU), shared shipping resources, BU accountability (Frazelle 2016)

— World-Class Warehousing and Material Handling (Frazelle 2016, 46).

Composition profiles

• Family-mix
• Handling-mix
• Order-increment

Composition Profiles

Customer order composition profiles include the following: ■ Family-mix profiles ■ Handling-mix profiles ■ Order-increment profiles

— World-Class Warehousing and Material Handling (Frazelle 2016, 47).

Family-mix profiles

If orders are mostly pure (i.e., drawn from a single product family), zoning the warehouse by product family will establish efficient warehouse processing cells.

Figure 12: Family-mix profile for a large paper distributor. 75% of the orders (flat stock, cut stock, envelope) can be completed within a single product family (family-based zoning yielded a 38% productivity increase) (Frazelle 2016)

Family-Mix Profiles

The overall operating strategy of a warehouse should be influenced by the extent to which orders require items from multiple families. If the majority of the orders are pure, drawing from a single product family, then zoning the warehouse on that basis could create virtual warehouses within the warehouse.

For example, the family-mix profile in Figure 2.9 was developed for a wholesale distributor of fine papers, copy/laser paper, and envelopes.

Printers make high-quality brochures from flat stocks of fine papers.

A carton of flat stock is 30 inches long, 24 inches wide, and 9 inches deep. A carton weighs 80 pounds.

Cut stock is basic 8½- by 11-inch copier and laser printer paper. A carton of cut stock is 24 inches long, 10 inches wide, and 10 inches deep. A carton weighs 20 pounds.

The third product family is envelopes—extremely small and lightweight merchandise. In this example (Figure 2.9), we are evaluating the merits of zoning based on those three product families—flat stock, cut stock, and envelopes.

If the orders are mixed—that is, flat stock, cut stock, and envelopes tend to appear together on customer orders—then in pallet building, we would start with flat stock, put cut stock on top of that, and put envelopes on top of that. If we zone the warehouse that way, we may pay a big travel-time penalty passing pallets between those zones. However, if the orders are pure—that is, they tend to be “complete-able” out of a single product family—then zoning the warehouse by product family will establish efficient warehouse processing cells, especially since inbound loads already arrive in those same product families.

In Figure 2.9, 35 percent of the orders can be completed solely from flat stock items, 25 percent can be completed solely from cut stock items, and 15 percent can be completed solely envelope items. The good news is that 75 percent of the orders (35 percent + 25 percent + 15 percent) can be completed within a single product family. Zoning the warehouse based upon those families yielded a 38 percent increase in overall operating productivity.

— World-Class Warehousing and Material Handling (Frazelle 2016, 47, 48).

Example of family pair analysis

Table 1: Families represented in each order SKUs

Order ID	Families represented
100	A, B, C
200	A,B
300	C,D,E
400	B,D,E
500	D,E
600	A,D,E
700	B,D,E

Table 2: Frequency that each family appears together

Family pairs:	AB	AC	AD	AE	BC	BD	BE	CD	CE	DE
Frequency:	2	1	1	1	1	2	2	1	1	5

Handling-mix profiles

• Full/partial-pallet mix?
• Full/broke-case mix?

Handling-Mix Profiles

The full/partial-pallet mix profile and the full/broken-case mix profile are two handling-unit mix profiles.

Full/Partial-Pallet Mix Profile

With a full/partial-pallet mix profile, we determine whether full and partial pallet picking should take place in the same or separate areas.

For example, in Figure 2.10, 52 percent of the orders are “complete-able” out of partial-pallet quantities, that is, just case picks; 29 percent of the orders are “complete-able” from full-pallet quantities, and the remaining 19 percent of the orders require both partial- and full-pallet quantities. The profile strongly suggests that separate areas for full and partial picking should be established. Should we have separate case picking and pallet picking areas? If so, would we pay a big penalty for mixed orders that require merging of the partial- and full-pallet portions of the order? No, only a small minority of the orders are mixed. For 81 percent of the orders, zoning based on pallet/ case picking creates warehouses within-the-warehouse. When the orders are released into the warehouse management system, it should classify them immediately as pallet pick orders, carton pick orders, or mixed orders. For mixed orders, the warehouse management system should create a pallet portion and a case pick portion and either pass the full-pallet portion to the case pick area or merge the case pick and pallet portions downstream from picking.

Full/Broken-Case Mix Profile

The full/broken-case mix profile helps us determine whether we should create separate areas for full- and broken-case picking. As was the case with the pallet/case-mix profile, the profile in Figure 2.11 indicates that only a small portion of the orders requires both a full- and broken-case quantity. Hence, creating separate areas for full- and brokencase picking will yield two order-completion zones with very little mixing between them.

— World-Class Warehousing and Material Handling (Frazelle 2016, 49).

Full/Partial-pallet mix profile

Should full and partial pallet picking occur in the same or separate areas?

Figure 13: Full/partial-pallet mix profile for a large office supplies company. The profile suggests separate areas should be established as only 19% of the orders require both partial- and full-pallet quantities (Frazelle 2016)

— World-Class Warehousing and Material Handling (Frazelle 2016, 50).

Full/Broken-case mix profile

Should full- and broken-case picking occur in separate areas?

Figure 14: Full/broken-case mix profile. Creating separate areas for full- and broken-case picking will yield two order-completion zones with very little mixing between them (Frazelle 2016)

— World-Class Warehousing and Material Handling (Frazelle 2016, 52).

Order-increment profiles

Illustrate the portion of a unit load (e.g., pallet) requested on a customer order. Examples:

• Customer orders 50 cartons from a 100-carton pallet (i.e., 50% of the pallet).
• Customer orders 25 cartons from a 100-carton pallet (i.e., 25% of the pallet).

How to avoid loose cartons?

• If the warehouse is attached to a manufacturing facility:

  • Palletizer can build full-, half-, and quarter-pallet unit loads.

• If the warehouse is not attached to a manufacturing facility:

  • Supplier build the quarter- and half-pallet loads.
  • Pre-configure the unit loads at receiving.

Figure 15: Palletizer

Order-Increment Profiles

Order-increment profiles (Figure 2.12), illustrate the portion of a unit load (in this situation, a pallet) requested on a customer order. For example, suppose that there are 100 cartons on a pallet and a customer orders 50 cartons. In this case, the customer ordered 50 percent of the pallet. If there are 80 cartons on a pallet and that customer orders 20, they ordered 25 percent of the pallet. What do you notice that is unusual about this distribution? (In almost all these distributions, the key insights are in the peaks and valleys). Where are the peaks? The peaks are around 25 and 50 percent of a pallet. Suppose that there are 100 cartons on a pallet and a customer places an order for 100 cartons.

Would you rather pick a full pallet or 100 individual cartons? You did not have to read this book to figure out that you would prefer to pick a whole pallet at a time.

In addition, a customer would rather receive a full-pallet quantity that they can handle in one unit load as opposed to having to handle 100 loose cartons.

How can we build half- and quarter-pallet unit loads?

In this particular case, the manufacturing facility is attached to the warehouse. There is a palletizer that sits on the border, and all we have to do is set the palletizer to put a pallet in place about four times as often as it builds quarter pallets and twice as often as it builds half pallets. If the warehouse is not attached to a manufacturing facility, the next-best scenario is to have the supplier build the quarter- and half-pallet loads. And if not the supplier, then we can preconfigure the unit loads at receiving.

— World-Class Warehousing and Material Handling (Frazelle 2016, 51).

Resetting price breaks

Figure 16: Pallet order-increment distribution for a large office supplies distributor. Order lines peak around 25% and 50% of a pallet. Resetting price breaks on the quarter- and half-pallet quantities improved picking productivity by 68% (Frazelle 2016)

— World-Class Warehousing and Material Handling (Frazelle 2016, 53).

Ordering in pre-configured unit loads

How to influence customers to order in half-, quarter-, and/or layer quantity increments?

• Making the pallet/ layer quantities accurate and visible to the customer and the order-entry personnel.
• Offering price discounts designed around efficient handling increments.

Can we influence customers to order in half-, quarter-, and/or layer quantity increments? Absolutely! In many cases, simply by making the pallet/ layer quantities accurate and visible to the customer and the order-entry personnel, we can encourage the practice of ordering in preconfigured-unit loads.

We can further encourage the practice by offering price discounts designed around efficient handling increments. In this case, there was a representative from the sales organization on the cross-functional team who literally reset the price breaks on the quarter- and half-pallet quantities the next day. Picking productivity increased by 68%.

— World-Class Warehousing and Material Handling (Frazelle 2016, 51).

Downside of preconfiguring sub-pallet unit loads

• Strict product rotation requirements (FIFO).
• Loss of storage density (4 \(\times\) quarter-pallet quantities \(>\) opening for singles).

Figure 17: Stacking quarter pallets may need 15% taller row openings than those for singles (Image: DELSOL )

The two potential downsides of preconfiguring sub-pallet unit loads are (1) the complications of strict product rotation requirements rotation requirements and (2) loss of storage density. I believe that in many industries first in, first out FIFO requirements are exaggerated. For example, I recently worked with a candy company that continued to hold out FIFO as a barrier to productivity improvements. I can remember a design meeting on Valentine’s Day when our client was receiving product for the Halloween season. There can be some large time windows within FIFO requirements. There will be some loss in storage density because a pallet worth of cartons may now have two or four pallets supporting it. For half-pallet quantities, we should be able to stack two halves in a full opening. For quarter-pallet quantities, we may need a row of openings that are 15 percent taller than the opening for singles. As a result, the loss in storage density should be less than 5 percent for the entire warehouse.

— World-Class Warehousing and Material Handling (Frazelle 2016, 54).

Layer picking

Customers order in full-layer quantities.

Figure 18: Layer picking profile for a large food and beverage company (most order-lines are for full-layer increments) (Frazelle 2016)

Layer Picking

Customers often order in full layer quantities. An example from a food and beverage client is illustrated in Figure 2.13. Note that 80 percent of the order lines are for full-layer increments, leading to our strong recommendation that layer picking be implemented to dramatically increase the productivity and throughput capacity. (A variety of layer picking systems are described in Chapter 6.)

— World-Class Warehousing and Material Handling (Frazelle 2016, 54, 55).

Split-case inner packs

Case order-increment profile determines the portion of a full carton that is requested on customer orders. For example:

• 100 pieces/carton and a customer orders 50, the customer ordered half the carton.

Case order-increment distribution for a large pharmaceuticals firm (y=percent of order lines, x = percent of full carton that is requested on customer orders). Order lines peak around half a carton and a full carton. Resetting price breaks at these quantities (half and full) could encourage customers who are almost ordering that quantity to order in full-carton increments (Frazelle 2016)

Split Case Inner Packs

The case order-increment profile (Figure 2.14), determines the portion of a full carton that is requested on customer orders. For example, if there are 100 pieces in a carton and a customer orders 50, the customer ordered half the carton. What do you notice that is unusual about this profile? In this example, customers tend to order around half a carton and a quantity close to a full carton. As a result, we would like to set price breaks at a half carton (and create an inner pack for a half carton) and at a full carton to encourage customers who are almost ordering that quantity to order in full-carton increments.

— World-Class Warehousing and Material Handling (Frazelle 2016, 54, 56).

Principles

• Prepackage in increments that customers are likely to order in.
• Encourage customers to order in those increments (price breaks).
• Supplier should do as much as possible to help prepare the product for picking and shipping.
• Warehouse should do as much as possible to prepare products at the receiving dock (leaving buffer time for picking/shipping preparation).
• After an order is placed, handling and preparation for shipping should be minimized.

The general principle is to prepackage in increments that customers are likely to order in and to encourage customers to order in those increments. Ideally the supplier should do as much as possible to help prepare the product for picking and shipping. Then we should do as much as possible at the receiving dock to prepare product for shipping immediately upon receipt as we have the longest time window available for picking/shipping preparation. As soon as an order drops for that product, the handling and preparation of the product for shipping should be minimized.

— World-Class Warehousing and Material Handling (Frazelle 2016, 57).

Customer order volumetric profiles

Lines-, units-, cube-, and weight-per-order profiles, and joint profiles.

Customer Order Volumetric Profiles

Our customer order volumetrics include lines-; units-; cube-; and weight-per-order profiles; and joint profiles comprised of the same. Some sample profiles are provided below.

— World-Class Warehousing and Material Handling (Frazelle 2016, 57).

Lines-per-order profile

Number of unique SKUs on an order.

• Each unique SKU line represents a visit to the unique location for that SKU.
• Location visits are the majority of the workload in any warehouse.

Example: High number of single-line orders (singles)

Figure 19: Lines-per-order distribution for a large mail-order¹ company. Single-line orders are most frequent (63%) (Frazelle 2016)

Lines-per-Order Profile

The lines-per-order profile portrays the number of unique stock-keeping units (SKUs) on an order. It is one of the most important profiles because each unique SKU or line on an order represents a visit to the unique location for that SKU. Location visits are the majority of the workload in any warehouse. Consequently, managing location visits is perhaps the master key to maximizing warehouse productivity.

The lines-per-order profile in Figure 2.15 indicates that 63 percent of the orders in the warehouse are for one line item, 13 percent for two, 9 percent for three to five, 8 percent for six to nine, and 7 percent for 10 or more. Where is the peak? It is around single-line orders. This distribution is common in the mail-order industry or when individual consumers or technicians order directly for a warehouse. We now need to consider the operating strategies that take advantage of this order profile.

Figure:

— World-Class Warehousing and Material Handling (Frazelle 2016, 57, 58).

1. Mail-order industry: Sells directly to customers (e.g., Amazon).

Taking advantage of single-line dominant

• If the singles are back orders¹, there is an opportunity for cross docking.
• Singles may be batched together for picking on efficient single-line picking tours:
  • Location sequence representing shortest length to visit all singles.
  • Single-line order batches naturally break the warehouse into zones defined by the length of the picking tour.
• Dynamic forward pick line: an automated lookahead into the day’s or shift’s orders may yield a number of SKUs for which there is at least a full carton’s worth of single-line orders. Those SKUs can be batch picked and set up along fast pick-pack lines.

Singles may be back orders. Back orders are an excellent opportunity for cross-docking. Singles may be batched together for picking on single-line picking tours. By presenting single-line orders in location sequence, we create very efficient picking tours.

In addition, the single-line order batches naturally break the warehouse into zones defined by the length of the picking tour.

Single-line orders also may represent an opportunity to create a dynamic forward pick line. In this operating scenario, an automated lookahead into the day’s or shift’s orders may yield a number of SKUs for which there is at least a full carton’s worth of single-line orders. Those SKUs can be batch picked and set up along fast pick-pack lines.

Pedidos de linha única também podem representar uma oportunidade para criar uma linha dinâmica de coleta direta. Nesse cenário operacional, uma previsão automatizada dos pedidos do dia ou do turno pode resultar em vários SKUs para os quais há pelo menos uma caixa cheia de pedidos de linha única. Esses SKUs podem ser separados em lote e configurados em linhas rápidas de coleta e embalagem.

Another common lines-per-order profile is illustrated in Figure 2.16. The peak is around 10+ lines per order. This profile is common in retail/ grocery/dealer distribution, where the customer is a retail store/grocery store/ dealership. In this case, there is typically enough work to do within an order that the order itself represents an efficient work set. Or the order may be so large that it may be split across multiple order fillers for zone-wave picking.

https://redstagfulfillment.com/manage-ecommerce-backorders/

The main difference between an item marked “out of stock” and an item available for “backorder” is that the former cannot be ordered at all, while the latter can be ordered but with a longer than normal shipping time. Backordered items are guaranteed to come back in stock and be delivered at a later date, while there is no guarantee that out-of-stock items will come back.

— World-Class Warehousing and Material Handling (Frazelle 2016, 59).

1. Back order: Out-of-stock items that will be back in stock soon.

Example: High number of lines per order

Figure 20: Retail lines-per-order profile for a large convenience store chain. Lines per order peak around 10+ (common profile in retail/grocery/dealer distribution). There is typically enough work to do within an order, and larger orders may be split across multiple order fillers for zone-wave picking (Frazelle 2016)

— World-Class Warehousing and Material Handling (Frazelle 2016, 60).

Cube-per-order profile

Portion of outbound orders falling into pre-defined cube classifications.

• Suggest alternative sizes for shipping containers, alternative picking methods, alternative transportation modes, and requirements for staging space.

Figure 21: Cube-per-order profile for a large toy company (CF = cubic feet). The profile can help select picking methods and container sizes for their mix of picking tours (Frazelle 2016)

Cube-per-Order Profile The cube-per-order distribution (Figure 2.17) illustrates the portion of outbound orders falling into pre-defined cube classifications. It may suggest alternative sizes for shipping containers, alternative picking methods, alternative transportation modes, and requirements for staging space. The figure depicts a cube-per-order distribution for a large toy company client. We use the profile to help select picking methods and container sizes for their mix of picking tours.

— World-Class Warehousing and Material Handling (Frazelle 2016, 59, 61).

Lines- and cube-per-order profile

Joint distribution that classifies all orders into lines-per and cube-per families.

Figure 22: Lines- and cube-per-order grid analysis. 176 single-line orders occupy less than 1 CF of space (these are candidates for batch-picking into compartmentalized picking carts, totes, or shipping containers).There is one order with more than 10 line items that occupies more than 20 CF (~1/3 pallet) (a candidate for a single operator to pick to a pallet) (Frazelle 2016)

Lines- and Cube-per-Order Profile

The lines- and cube-per-order profile (Figure 2.18) brings together in one profile the critical information needed to define order picking strategy. It is a joint distribution that classifies all orders into lines-per and cube-per families. In this example, there are 176 orders with one line item and that occupy less than a cubic foot of space. These orders are probably candidates for a single operator to batch together for picking into compartmentalized picking carts, totes, or shipping containers. There is one order with more than 10 line items that occupies more than 20 cubic feet, about a third of a pallet. That order is a candidate for a single operator to pick to a pallet.

— World-Class Warehousing and Material Handling (Frazelle 2016, 59, 62).

Lines- and cube-per-order profile vs. MHE

Figure 23: Lines- and cube-per-order distribution from a retail company (Frazelle 2016)

Table 3: Adequate MHE for each order size

Order size (#lines)	MHE
Small (\(<10\))	Special pick-pack carts holding 10–20 orders/cart
Medium(\(\leq 100\))	Medium-sized carts holding 2–5 orders/cart
Large (100+)	Large-order carts holding a single order per tour

The example in Figure 2.19 is a lines- and cube-per-order distribution from a major retail client. Note that the orders tend to fall into one small one medium and one large group. In the example, small orders are picked via special pick-pack carts holding 10 to 20 orders per cart. Medium-sized orders are picked with medium-sized carts holding two to five orders per cart. Large orders are picked one at a time in large-order carts holding a single order per tour.

— World-Class Warehousing and Material Handling (Frazelle 2016, 64).

Calendar-clock profiles

Reveal peaks and valleys in warehouse activity. Include the following:

• Month-of-year (MoY) activity profile
• Week-of-month (WoM) activity profile
• Day-of-week (DoW) activity profile
• Hour-of-day (HoD) activity profile

Calendar-clock profiles help to optimally dimension the elements of the warehouse infrastructure (e.g., workforce, material handling systems, storage systems, and dock doors).

2.3 Calendar-Clock Profiles

Calendar-clock profiles reveal peaks and valleys in warehouse activity so that all the elements of the warehouse infrastructure—workforce, materialhandling systems, storage systems, and dock doors—can be sized optimally. Calendar-clock profiles include the following:

■ Month-of-year (MoY) activity profile ■ Week-of-month (WoM) activity profile ■ Day-of-week (DoW) activity profile ■ Hour-of-day (HoD) activity profile

— World-Class Warehousing and Material Handling (Frazelle 2016, 63).

Some seasonalities can be hard to predict: For example, in Mexico it is popular to eat a sweet bread called marinela when it rains; but rain is very hard to predict beyond a week (Bartholdi and Hackman 2019).

Month-of-year profile (seasonality activity distribution)

Portrays the impact of seasons (e.g., Christmas, back to school, fall, winter, spring) on warehouse activity.

Figure 24: Month-of-year (seasonality) profile for a large retailer. Receipts peak in Aug/Sep, inventory peaks in Sep/Oct, shipping peaks in Oct/Nov, and returns peak in Jan. Accordingly, extra staff can be consecutively allocated to receiving, shipping, and returns (Frazelle 2016)

Month-of-Year Activity Profile

The month-of-year activity profile is sometimes referred to as the seasonality activity distribution because it typically portrays the impact of seasons such as Christmas, back to school, fall, winter, spring, and so forth on warehouse activity.

An example month-of-year activity profile for a large retailer is illustrated in Figure 2.20. The seasonality profile indicates the peaks and valleys in inventory levels, as well as receiving, shipping, and returns activity. Because storage systems need to be sized to accommodate near-peak inventory levels and material-handling systems need to be sized to accommodate near-peak activity levels, it is critical to identify peak inventory and activity levels. The example is typical of retail distribution, with receipts peaking in August/September, inventory peaking in September/October, shipping peaking in October/November, and returns peaking in January. A distribution presents an opportunity to shift the extra staff required for receiving in August/September to shipping in October/November to returns handling in January.

One of our furniture clients experiences the same volatility but in different seasons (Figure 2.21). As we learned from our work with them, summer is their busiest season based upon their major sales during the Memorial Day and Labor Day holidays.

— World-Class Warehousing and Material Handling (Frazelle 2016, 63, 65).

Seasonality distribution

Figure 25: Seasonality distribution (total fleet pieces per month) for a large furniture company. Summer is the busiest season based upon sales during Memorial Day and Labor Day holidays (Frazelle 2016)

One of our furniture clients experiences the same volatility but in different seasons (Figure 2.21). As we learned from our work with them, summer is their busiest season based upon their major sales during the Memorial Day and Labor Day holidays.

— World-Class Warehousing and Material Handling (Frazelle 2016, 67).

Week-of-month activity distribution

Figure 26: Total load unload activity per week (52 wks). Most shipping activity occurs during the last week of the month, especially during the last month of a quarter. Most receiving activity occurs during the first week of the month, especially during the first month of a quarter (Frazelle 2016)

Week-of-Month Activity Distribution

Most shipping activity occurs during the last week of the month, especially during the last month of a quarter (Figure 2.22). Conversely, most receiving activity occurs during the first week of the month, especially during the first month of a quarter.

— World-Class Warehousing and Material Handling (Frazelle 2016, 66, 68).

Day-of-week activitiy distribution

The day of the week is a driving force in many warehouse activity peaks and valleys.

Day-of-Week Activity Distribution

The day of the week is another driving force in many warehouse activity peaks and valleys. We recently completed a supply chain strategy project for one of the world’s largest bedding manufacturers (Figure 2.23). Most of the mattresses purchased in the world are purchased on Saturday, with deliveries scheduled for the following Friday. Needless to say, Thursday and Friday are very busy days in their warehouses. In recent project in the food and beverage industry, two retailers comprised nearly 50 percent of their outbound volume. Unfortunately, each wanted deliveries executed on the same two days of each week. Needless to say, the two days before were overwhelming peaks for the warehouse operations (Figure 2.24)

— World-Class Warehousing and Material Handling (Frazelle 2016, 69).

Day-of-week activity profile vs. outbound (1)

Figure 27: Day-of-week activity profile (total fleet pieces per day[Mon–Fri]) for a large bedding company. Most mattresses are purchased on Sat with deliveries scheduled for the following Fri (the warehouse is busiest on Thu and Fri — highest total fleet pieces) (Frazelle 2016)

— World-Class Warehousing and Material Handling (Frazelle 2016, 69).

Day-of-week activity profile vs. outbound (2)

Figure 28: Day-of-week activity profile (total load/unload per day [Sun–Sat]) for a food and beverage company. Two retailers comprised nearly 50% of the outbound volume and wanted deliveries executed on the same two days of each week (the two days before were overwhelming peaks for the warehouse operations) (Frazelle 2016)

— World-Class Warehousing and Material Handling (Frazelle 2016, 70).

Hour-of-day activity distribution

Indicates receiving, putaway, picking, and shipping activity by hour of the day.

• MHS should be designed for peak activity periods.

Figure 29: Hour-of-day distribution for dock-door receiving activity (n. of. cases received per hour). Offsetting peaks represent opportunities for shift staggering¹ and inter-department workforce shifting (Frazelle 2016)

Hour of Day Activity Distribution

As the name suggests, the Hour-of-Day profile indicates (Figure 2.25) the receiving, putaway, picking, and shipping activity by hour of the day. Material handling systems should be designed for peak activity periods. Offsetting peaks represent opportunities for shift staggering and inter-department workforce shifting.

Figure:

— World-Class Warehousing and Material Handling (Frazelle 2016, 71, 72).

1. Staggered hours: Workers start and finish work at different times.

Item activity profiles

Used primarily to slot the warehouse to decide:

1. What storage mode the item should be assigned to
2. How much space the item should be allocated in the storage mode
3. Where in the storage mode the item should be located

Item Activity Profiling The item activity profile is used primarily to slot the warehouse, to decide for each item (1) what storage mode the item should be assigned to, (2) how much space the item should be allocated in the storage mode, and (3) where in the storage mode the item should be located. The item activity profile includes the following activity profiles:

■ Popularity profile ■ Cube–movement profile ■ Popularity-cube–movement profile ■ Order-completion profile ■ Demand-correlation profile ■ Demand-variability profile

— World-Class Warehousing and Material Handling (Frazelle 2016, 71).

Item popularity profile (ABC curve / Pareto distribution)

Indicates the cumulative sum of picks associated with SKUs (ranked by descending popularity).

Figure 30: Item popularity profile for a large service parts company. 10% most popular items yield 70% of the picking activity, 50% most popular items yield 90% of the picking activity and so on (Frazelle 2016)

Item Popularity Profile

As mentioned earlier, a minority of the items in a warehouse typically generates a majority of the picking activity. The popularity profile (sometimes called an ABC curve or a Pareto distribution) indicates the x percent of picks associated with y percent of the SKUs (ranked by descending popularity). Figure 2.26 is a classic popularity profile indicating that the 10 percent most popular items yield 70 percent of the picking activity, the 50 percent most popular items yield 90 percent of the picking activity, and so on.

— World-Class Warehousing and Material Handling (Frazelle 2016, 71, 73).

Item popularity families

Key breakpoints in the distribution suggest delineation between item popularity families.

Table 4: Candidate storage modes for families A, B, and C

Breakpoint (family)	Picking activity	Storage mode	Location within storage
Top 5% (family A)	50% picks	automated, highly-productive storage mode	golden zone (close to a travel aisle and/or near waist level)
Next 15% (family B)	20% picks	semi-automated, moderately productive picking mode	silver zone
Next 80% (family C)	30% picks manual picking mode that offers high storage density	remaining space

The overriding principle is to assign the most popular items to the most accessible warehouse locations.

Key breakpoints in the distribution suggest delineations between item popularity families. For example, the top 5 percent of items (Family A) may make up 50 percent of the picking activity, the next 15 percent of items (Family B) may take us to 80 percent of the picking activity, and the remaining 80 percent of items (Family C) cover the remaining picking activity. These families, in turn, may suggest three alternative storage modes—Family A in an automated, highly productive storage mode; Family B in a semi-automated, moderately productive picking mode; and Family C in a manual picking mode that offers high storage density. The family breakpoints also may suggest the location of the items within a storage mode—A items located in the golden zone (close to a travel aisle and/or at or near waist level), B items in the silver zone, and C items in the remaining spaces. The overriding principle is to assign the most popular items to the most accessible warehouse locations.

— World-Class Warehousing and Material Handling (Frazelle 2016, 74).

Cube-movement profile

Used to make storage-mode and space-allocation decisions.

• Indicates the portion of items that falls into pre-specified cube-movement ranges.

• If the pre-specified ranges correspond to storage-mode alternatives, then the cube-movement profile will solve the storage-mode assignment problem.

Figure 31: Cube-movement profile for a large valves and fittings company (Frazelle 2016)

Table 5: Candidate storage modes for items based on cube-movement

Items (%)	CF/Month	Candidate storage
15	0.1	storage drawers / bin shelving
12	1,000 (~20 pallets)	block stacking, double-deep rack, push-back rack, and/or pallet flow lanes

Cube-Movement Profile

The cube-movement profile is used primarily for making storage-mode and space-allocation decisions. The cube-movement profile indicates the portion of items that falls into pre-specified cube-movement ranges. If the pre-specified ranges correspond to storage-mode alternatives, then the cube-movement profile will essentially solve the storage-mode assignment problem. For example, in Figure 2.27, 15 percent of the items ship less than 0.1 cubic feet per month. Those items may be good candidates for storage drawers or bin shelving. At the other end of the distribution, we find 12 percent of the items that move more than 1,000 cubic feet (nearly 20 pallets) per month. Those items may be candidates for block stacking, double-deep rack, push-back rack, and/or pallet flow lanes.

— World-Class Warehousing and Material Handling (Frazelle 2016, 74, 75).

Popularity-cube-movement profile

Slotting takes into account both item popularity and cube-movement.

Figure 32: Popularity-cube-movement profile for broken-case picking (Frazelle 2016)

Table 6: Inner regions

Quadrant	#Picks/space unit	Zone
Bottom right-hand	High	Golden (most accessible)
Upper right-hand / lower left-hand	Moderate	Silver
Upper left-hand	Low	Bronze (least accessible)

Important

Slotting broken-case picking systems also depends on: planning horizon, wage rate, and cost of space and capital.

Popularity–Cube-Movement Profile

Done properly, slotting takes into account both the item popularity and cube-movement.

An example popularity-cube-movement profile for broken-case picking is presented in Figure 2.28. In this example, those items exceeding a certain cube-movement threshold are assigned to a carton flow rack. Items with high cube-movement need to be restocked frequently and need a larger storage location compared to items with medium and low cube movement.

Hence they need to be assigned to a storage mode that facilitates restocking and condenses large storage locations along the pick line-carton flow rack.

Items with low cube movement and high popularity generate many picks per unit of space that they occupy and do not occupy much space along the pick line. They need to be in a highly productive picking mode. In this case, light directed carousels are recommended because the picking productivity is high, and we can afford the carousels for items that do not need large storage housings on the pick line and do not need to be restocked frequently. (Carousels do not lend themselves to restocking and are expensive per cubic foot of space.) Items with low popularity and low cube movement cannot be justifiably housed in an expensive storage mode. Hence they are candidates for bin shelving and modular storage drawers.

Items in the bottom right-hand quadrant of each preference region generate the most picking activity per unit of space they occupy in the storage mode. Hence they should be assigned to positions in the golden zone (most accessible pick locations) of their storage mode. Items in the upper right-hand and lower left-hand quadrants generate a moderate number of picks per unit of space they occupy in the storage mode. Hence they should be assigned to positions in the silver zone. Finally, items in the upper lefthand quadrant of the distribution generate the fewest picks per unit of space they occupy and they should be assigned positions in the bronze (least accessible) zone. This example is not an end-all recommendation for slotting broken-case picking systems. This depends on many other factors, including the wage rate, the cost of space, the cost of capital, the planning horizon, and so on. Instead, this example is presented to illustrate how the popularity–cubemovement distribution is used in slotting.

https://www.google.com/url?sa=i&url=https%3A%2F%2Fwww.cisco-eagle.com%2Fblog%2F2014%2F10%2F28%2Fquestions-to-ask-carousel-picking-systems%2F&psig=AOvVaw14Z3drB9rn8Z7qF6Y22eeT&ust=1683267317011000&source=images&cd=vfe&ved=2ahUKEwi05ZrFgdv-AhW_7bsIHZswC8sQr4kDegUIARDDAQ

— World-Class Warehousing and Material Handling (Frazelle 2016, 74, 76).

Popularity-cube-movement profile (MHE)

Storage mode	Popularity/Cube-movement
Carton flow rack	High/High
Light-directed carousels	Low/High
Bin shelving/modular storage drawers	Low/Low

Figure 33: Carton flow rack

Figure 34: Modular storage drawers

Figure 35: Bin shelvings

Figure 36: Carousels

Recommendations

• Carton flow rack: Items exceeding a certain cube-movement threshold.
  • Items with high-cube movement need to be restocked frequently and need a larger storage location compared to items with medium and low cube movement.
  • Hence they need to be assigned to a storage mode that facilitates restocking and condenses large storage locations along the pick line-carton flow rack.
• Light-directed carousels
  • Low cube movement and high popularity
  • Generate many picks per unit of space (highly-productive picking mode)
• Bin shelving/modular storage drawers
  • Items with low popularity and low cube movement.

These recommendations are preserved as course synthesis grounded in the popularity–cube movement discussion in World-Class Warehousing and Material Handling (Frazelle 2016, 74–76).

Item-order-completion profile

Identifies small groups of items that can fill large groups of orders.

Construction:

1. Rank items from most to least popular. For example:
   • most popular item,
   • two most popular items,
   • three most popular items, etc.
2. Compare items with the order pool to determine what portion of the orders a given subset of the items can complete.

Item-Order-Completion Profile

The item-order-completion profile (Figure 2.29) identifies small groups of items that can fill large groups of orders. Those small groups of items often can be assigned to small order-completion zones in which the productivity, processing rate, and processing quality are two to five times better than those found in the general warehouse. The item-order-completion profile is constructed by ranking items from most to least popular. Beginning with the most popular item, then the two most popular items, then the three most popular items, and so on, the items are compared with the order pool to determine what portion of the orders a given subset of the items can complete. In this example, 10 percent of the items can complete 50 percent of the orders. Suppose that I walk into your warehouse and identify 10 percent of the items that can completely fill 50 percent of the orders. What would you do with those 10 percent of the items? I hope that you would create a warehouse within the warehouse or order-completion zone for those 10 percent. The design principle is similar to that used in agile manufacturing, where we look for small groups of parts that have similar machine routings. Those machines and those parts make up a small-group technology cell wherein the manufacturing efficiency, quality, and cycle time are dramatically improved over those found in the factory as a whole.

We recently worked with a large media (i.e., compact discs, cassettes, videos, etc.) distributor and helped to identify 5 percent of its 4,000 SKUs that could complete 35 percent of its orders. We assigned those 5 percent to carton flow-rack pods (three flow-rack bays per pod, one operator per pod) at the front of the distribution center. Operators could pick/pack orders from the flow rack at nearly six times the overall rate of the distribution center. The distribution center has won its industry’s productivity award each of the last two years.

Very frequently, there is a driving force behind order completion. Examples could include customers ordering within a brand (Figure 2.30) or, as depicted in Figure 2.31, a product group, a supplier, a size, a color, a kit, and so on. Our pattern-recognition algorithms seek and surface those patterns in an attempt to identify order-completion zoning opportunities within the warehouse.

— World-Class Warehousing and Material Handling (Frazelle 2016, 77).

Item-order-completion profile

What to do with the items that fill large groups of orders?

Figure 37: 10% of the items can complete 50% of the orders (Frazelle 2016)

— World-Class Warehousing and Material Handling (Frazelle 2016, 78).

Warehouse-within-warehouse

What to do with the items that fill large groups of orders?

• These items can be assigned to small order-completion zones (warehouse-within-warehouse).
• Within these zones, productivity, processing rate, and processing quality can be \(2\times\) to \(5\times\) better than the general warehouse.

Industry case: Media distributor

A large media company assigned a subset of 5% of its 4,000 SKUs that could fulfill 35% of its orders to carton 3-bay flow-rack pods situated at the front of the DC. By utilizing the flow rack, operators were able to pick and pack orders at a rate almost \(6\times\) higher than the overall rate of the DC.

The design principle is similar to that used in agile manufacturing, where we look for small groups of parts that have similar machine routings. Those machines and those parts make up a small-group technology cell wherein the manufacturing efficiency, quality, and cycle time are dramatically improved over those found in the factory as a whole. I hope that you would create a warehouse within the warehouse or order-completion zone for those 10 percent. The design principle is similar to that used in agile manufacturing, where we look for small groups of parts that have similar machine routings. Those machines and those parts make up a small-group technology cell wherein the manufacturing efficiency, quality, and cycle time are dramatically improved over those found in the factory as a whole. We recently worked with a large media (i.e., compact discs, cassettes, videos, etc.) distributor and helped to identify 5 percent of its 4,000 SKUs that could complete 35 percent of its orders.

We assigned those 5 percent to carton flow-rack pods (three flow-rack bays per pod, one operator per pod) at the front of the distribution center. Operators could pick/pack orders from the flow rack at nearly six times the overall rate of the distribution center. The distribution center has won its industry’s productivity award each of the last two years.

— World-Class Warehousing and Material Handling (Frazelle 2016, 79).

Item-order-completion profile (by brand count)

Figure 38: Item-order-completion profile from a large food and beverage company (x = order quantity / y = distinct count of brand name [1–9]) (Frazelle 2016)

— World-Class Warehousing and Material Handling (Frazelle 2016, 80).

Order-completion profile (by product characteristics)

Figure 39: Order-completion profile for a large industrial supplies company. Driving force behind order completion could be a product group, a supplier, a size, a color, a kit, etc. (Frazelle 2016)

— World-Class Warehousing and Material Handling (Frazelle 2016, 81).

Demand-correlation profile (family)

Items are ranked based on their frequency of appearing together on orders.

	Item Number (A)	Item Number (B)	Pair Frequency
0	201-2-1	202-2-1	74
1	301-2-1	101-2-3	61
2	401-1-2	501-1-2	48
3	102-2-1	103-1-1	42
4	601-2-1	602-2-1	33
5	701-2-1	702-2-1	28
6	801-3-1	802-2-1	22
7	901-2-1	902-2-1	18
8	201-3-1	202-3-1	14
9	301-3-1	102-2-1	12

Figure 40: Demand-correlation distribution for a grocery retailer. Customers tend to order items¹ like Dry Pasta and Pasta Sauce together, as well as Milk and Cereal.

This coded table preserves the previous deck’s reproducible example. It ranks SKU pairs by the frequency with which they appear together on orders.

— World-Class Warehousing and Material Handling (Frazelle 2016, 79, 82).

1. Item number: First three digits = product category (e.g., Milk, Bread, Eggs, etc.), the middle digit = size (e.g., 1 = Small, 2 = Standard, 3 = Large, 4 = Family/Club), and the last digit = variant (e.g., 1 = Regular, 2 = Organic, 3 = Low-fat, 4 = Gluten-free, 5 = Sugar-free).

Demand-correlation profile (decoded SKUs)

Decoding the SKU fields makes the co-occurrence pattern interpretable for warehouse zoning and slotting decisions.

Table 7: Highest-frequency SKU pairs after decoding.

	Item Number (A)	A (decoded)	Item Number (B)	B (decoded)	Pair Frequency
0	201-2-1	Dry Pasta • Standard • Regular	202-2-1	Pasta Sauce • Standard • Regular	74
1	301-2-1	Cereal • Standard • Regular	101-2-3	Milk • Standard • Low-fat	61
2	401-1-2	Bananas • Small • Organic	501-1-2	Yogurt • Small • Organic	48
3	102-2-1	Bread • Standard • Regular	103-1-1	Eggs • Small • Regular	42
4	601-2-1	Tortillas • Standard • Regular	602-2-1	Salsa • Standard • Regular	33

The decoded labels expose the product, size, and variant fields behind the highest-frequency SKU pairs. This is a course reconstruction of the method; the cited book example uses apparel style, size, and color codes.

— World-Class Warehousing and Material Handling (Frazelle 2016, 79, 82).

Demand-correlation profile (decoded SKUs, continued)

The remaining pairs complete the ranked example.

Table 8: Remaining decoded SKU pairs in the ranked example.

	Item Number (A)	A (decoded)	Item Number (B)	B (decoded)	Pair Frequency
0	701-2-1	Ground Coffee • Standard • Regular	702-2-1	Coffee Filters • Standard • Regular	28
1	801-3-1	Chicken Breasts • Large • Regular	802-2-1	Seasoning Blend • Standard • Regular	22
2	901-2-1	Paper Towels • Standard • Regular	902-2-1	Toilet Paper • Standard • Regular	18
3	201-3-1	Dry Pasta • Large • Regular	202-3-1	Pasta Sauce • Large • Regular	14
4	301-3-1	Cereal • Large • Regular	102-2-1	Bread • Standard • Regular	12

Demand-Correlation Profile

Just as a minority of the items in a warehouse makes up a majority of the picking activity, certain items in the warehouse tend to be requested together. In the example (Figure 2.32), pairs of items are ranked based on their frequency of appearing together on orders. In this case, we are examining data from a mail-order apparel company. The first three digits represent the style of the item (i.e., crew-neck sweater, V-neck sweater, turtle-neck shirt, pleated pants, etc.), the middle digit represents the size of the item (i.e., 1 = small, 2 = medium, 3 = large, and 4 = extra large), and the last digit represents the color (i.e., 1 = white, 2 = black, 3 = red, 4 = blue, 5 = green, etc.). What do you think people tend to order together from this mail-order company? (I thought it would be shirts and pants that looked good together in the catalog.) What does the profile in Figure 2.32 suggest? In this case, customers tend to order items of the same style and size together. The explanation is that customers tend to get comfortable with a certain style and tend to order in multiple colors to add variety to their wardrobes. Of course, they order the same size unless they will return one for fitting. This pattern was a surprise to me. It was also a surprise to the marketing people. Surfacing truth is the crucial benefits of profiling process. (The myriad SKUs, order patterns, suppliers, and interdependent decisions make it difficult to form a reliable intuition about logistics operations.) How do we take advantage of this demand-correlation information in slotting a warehouse? We zone the warehouse by item size first, creating a zone for the smalls, mediums, larges, and extra larges of all styles (Figure 2.33). Within each size area, we store items of the same style together, mixing colors within a style. This zoning strategy allows us to create picking tours based on size and style. As a result, order pickers pick many items on short-distance picking tours. Simultaneously, we manage congestion by spreading out the sizes. Golden zoning is used to slot the most popular color for each style at or near waist level.

— World-Class Warehousing and Material Handling (Frazelle 2016, 79, 82).

Slotting optimization (based on clothes features)

• Picking tours are based primarily on:
  • Item size (smalls, mediums, largers, and extra larges of all styles)
  • Item style (sweaters, accessories, pats, shirts but mixing colors)
• Golden zoning (slot the most popular color for each style at or near waist level)
• Congestion is managed by spreading out the sizes.

Figure 41: Slotting optimization for a large omni-channel apparel retailer (Frazelle 2016)

— World-Class Warehousing and Material Handling (Frazelle 2016, 83).

Demand-variability profile

Indicates the standard deviation of daily demand for each item.

Figure 42: Demand-variability profile for a large textile company. MHS should accommodate the demand variability (standard deviation) rather than average day’s demand (Frazelle 2016)

Demand-Variability Profile The demand-variability profile (Figure 2.34) indicates the standard deviation of daily demand for each item. Unfortunately, an item’s daily demand is not predictable. During a recent project, we were trying to size the pick faces along a case picking line such that each pick face held a day’s worth of stock. The motivation was to make sure that we did not need to restock a location during the day. The current design had the pick faces sized for an average day’s demand, and the client could not figure out why it had to restock so many locations during the course of a day. You can probably see why. If the pick face is sized for the average day, unless the same quantity is picked every single day, there will be many days when the pick face is oversized and many days when the pick face is undersized. Once the pick faces were resized to accommodate this variability in demand, the restocking during the pick shift was virtually eliminated.

Figure:

— World-Class Warehousing and Material Handling (Frazelle 2016, 82, 84).

Inventory profiles

• Item-family (on-hand) inventory profile: Reveal opportunities for improved inventory management practices.
• Handling-unit inventory profile: Used in storage systems planning.

2.5 Inventory Profile

The inventory profile includes the item-family inventory profile used to reveal opportunities for improved inventory management practices and the handling-unit inventory profile used in storage systems planning.

— World-Class Warehousing and Material Handling (Frazelle 2016, 85).

Item-family (on-hand) inventory profile

Helps to identify slow-moving inventory occupying coveted space.

Figure 43: Item-family inventory distribution. Most companies have too little type A inventory (leading to back orders and lost sales) and too much type C inventory (obsolete stock) (Frazelle 2016)

On-Hand Inventory Profile

I receive a lot of phone calls that begin with clients complaining about the lack of space in their warehouse. More often than not, the problem is not too little space, but too much inventory. This profile (Figure 2.35) helps us identify the source of the inventory problem. As is true in the example, most companies have too little type A inventory (back orders and customers screaming for those products) and too much type C inventory (obsolete stock that nobody wants and nobody has the courage to discard). By drawing the picture, we can at least illustrate the magnitude of the problem to management and present a list of “problem items” for their review. In some cases, C items should be removed from inventory. However, in many cases, warehouses are required to house the C items. One example is in the service parts business, where you may be required to support a certain model number in the field for up to 5, 10, even 20 years. Another example is in retailing when some key type C items protect the sales of items A and B. On a recent project in the grocery industry, the chairman of the company was presented with a recommendation to eliminate the C items from inventory. Let’s pause and consider the consequences. How many items do you buy on your weekly trip to the grocery store to restock the kitchen cabinets? Say its 50 items. In this case, there is at least a 70 percent chance that one of those 50 items is a C item. Why did you go to that grocery store? In the case of this grocery chain, it was probably because it stocked that C item. Otherwise, you could and would shop at a warehouse store. Even though you may not be able to eliminate the C item inventory you can at least efficiently store and pick the items. To conserve space, you can store the C item inventory in dense, high-rise racking or on the second or third level of a mezzanine. To achieve high productivity at the same time, you can batch-pick the C item pick line and locate the batch in a dedicated location along the forward pick line or introduce the batch into an automated sorting system.

— World-Class Warehousing and Material Handling (Frazelle 2016, 85).

Dealing with slow-moving inventory

In many cases, warehouses are required to house C items (service-parts required to support a certain model number for up to 5, 10, 20 years). If C can’t be eliminated:

• Store in dense, high-rise racking.
• Store on a 2nd or 3rd level of a mezzanine.
• Batch-pick the C item.
• Locate the batch in a dedicated location along the forward pick line.
• Introduce the batch into an automated sorting system.

Handling unit inventory profile

Useful for storage systems design and planning and managing warehouse operations.

• Describe inventory in material-handling terms (i.e., pallets, cases, eaches, etc.) of merchandise on hand.

Figure 44: Handling-unit inventory distribution for a large healthcare company (#SKUs and #pallets) (Frazelle 2016)

Table 9: Adequate mix of pallet storage modes

#SKU (#pallets)	Storage mode
91 (\(<1\))	shelving or decked racking
104 (\(1\))	single-deep pallet racks
68 SKUs (\(2-5\))	double-deep, push-back racks
Remaining SKUs (\(>10\))	floor (deep block stacking lanes, drive-in/through racks, pallet flow lanes)

Most corporate data used in planning warehouse operations are expressed in terms of dollars, pounds, pieces, days of supply, turns, etc.

• Useful for business planning but not helpful for planning and managing warehouse operations.

Handling Unit Inventory

Profile

The item-family inventory profile is not very useful for storage systems design because the information is not presented in material-handling terms (i.e., pallets, cases, eaches, etc.). This is a common problem with most corporate data used in planning warehouse operations—the data are expressed in terms of dollars, pounds, pieces, days of supply, turns, and so on. Although this is useful for business planning purposes, the data are not very helpful for planning and managing warehouse operations. This is another motivation for the profiling exercise—to give managers and designers of the warehouse operations a presentation of the activity of the warehouse in their terminology. In this example (Figure 2.36), we convert the item-family inventory distribution into a distribution describing on-hand inventory in terms of pallets of merchandise on hand. As a result, we can recommend the appropriate mix of pallet storage modes. For example, the 91 SKUs with less than a pallet of inventory on hand probably should be stored in shelving or decked racking. The 104 SKUs that have one or two pallets on hand probably should be stored in single-deep pallet racks. The 68 SKUs that have three to five pallets on hand probably should be stored in doubledeep and/or push-back racks. The remaining SKUs, those with more than 10 pallets on hand, probably should be stored on the floor in deep block stacking lanes, in drive-in/through racks, and/or in pallet flow lanes. In this example (Figure 2.36), we convert the item-family inventory distribution into a distribution describing on-hand inventory in terms of pallets of merchandise on hand. As a result, we can recommend the appropriate mix of pallet storage modes. For example, the 91 SKUs with less than a pallet of inventory on hand probably should be stored in shelving or decked racking. The 104 SKUs that have one or two pallets on hand probably should be stored in single-deep pallet racks. The 68 SKUs that have three to five pallets on hand probably should be stored in double-deep and/or push-back racks. The remaining SKUs, those with more than 10 pallets on hand, probably should be stored on the floor in deep block stacking lanes, in drive-in/through racks, and/or in pallet flow lanes.

— World-Class Warehousing and Material Handling (Frazelle 2016, 86, 88).

Practice and references

05

Exercises

Improving Profile Clarity

In this exercise, you will analyze and improve the clarity of a warehouse activity profile. The goal is to ensure that the data presented is both informative and easily understood by warehouse managers and staff.

When creating profiles, we should aim for clarity and intuitiveness: how can we present the data in a way that is easily understandable for warehouse managers and staff?

Figure 45 shows an example of a profile presented in a report on a company’s warehousing operations.

The accompanying text provides insights into the order processing dynamics:

At Company X, a high volume of orders is processed across a diverse customer base of more than 30 customers. On average, about 225 orders arrive per day, with peaks in the middle of the week. Certain clients of Company X generate substantial number of orders, with two clients generating over 150 orders per day combined. In Figure 2.6, the total number of orders per day are shown, in addition to the shipped orders per day. As can be seen, there are different spikes in the number of orders that are shipped. This is because Company X does not operate in the weekends, so the orders that have accumulated over the weekend need to be processes and shipped at the start of the workweek. Where the shipped orders have a weekly cycle, the incoming orders follow a more evenly distribution. However, there are still some fluctuations, mostly between the weekdays and weekends.

This exercise text is preserved from the previous course deck. The company identity and upstream source of the supplied profile were not recoverable, so the passage is treated as course material rather than external evidence.

Repair the unclear profile

Figure 45: Example of profile concerning order processing dynamics at Company X.

Task: Analyze the provided profile and accompanying text and propose improvements so the chart is more informative and independent.

References

Bartholdi, John J., and Steven T. Hackman. 2019. Warehouse
& Distribution Science. Release 0.98.1. The Supply Chain
& Logistics Institute, Georgia Tech. https://doi.org/10.13140/RG.2.1.4264.0481.

Frazelle, Edward. 2016. World-Class Warehousing and Material Handling. Second Edition. McGraw-Hill Education.