Warehouse Management

Voice picking in warehouses work in a similar fashion as RF devices. Instead of picking tasks displayed on an RF screen, warehouse operators listen to task information on their headsets through a voice systems connected to WMS through a Wi-Fi network. Voice picking also allows task confirmation through spoken commands. Voice picking has numerous advantages in a warehouse such as:

• Voice Picking makes the data entry operation hands free. You do not need to hold an RF device or scan a barcode to confirm a pick task thereby leaving both hands free for physical movement of goods. This significantly boosts operator productivity.

• Picking by voice improves accuracy and as I indicated in a previous post, importance of accuracy can not be underemined

• Voice Picking  is particularly suitable for environments where punching data on RF devices is not feasible such as freezer section for storing perishables. In such an environment you can imagine the plight of the warehouse operators with gloves fumbling and punching data on keyboard

Does it make sense to use voice picking in the entire warehouse? Possibly not. Voice picking may not make sense for warehouse area with low volume or pallet picks which can be done equally effectively using traditional RF devices.Voice picking is also most effective for repetitive tasks. If the warehouse operators perform a large number of different transactions, voice picking may not be very effective. A careful cost-benefit analysis is needed to determine what areas of the warehouse can benefit from voice picking.

If you wish to enable Voice-picking, Oracle WMS has capabilities to interface with any external voice picking system such as VoCollect. It also gives you the flexibility to have voice picking enabled for certain areas or operations of the warehouse e.g. use voice picking for high volume unit picks and RF pick for pallet picks, putaway, receipt and inspection transactions.

The approach needed to interface with a voice picking system is similar to the interface with any other material handling equipment. The interface with voice picking can be initiated at the time of pick release. Its possible to define a business event at pick release for certain areas of the warehouse to compile the task information and interface it to a voice picking systems. Subsequently voice picking system dispatches the tasks and sends a confirmation back to WMS.

Stocking policy for an item dictates the location and desired inventory levels in the pick face. If you think about it, where you store an item in the warehouse can actually make a profound impact on warehouse efficiency. Just imagine, if a frequently picked item is stored in a location that is further along from the shipping area, how much time would be spent in a non-valude added activity such as extra travel time. How about placing a frequently ordered heavy item in  a lower rack where the operator needs to bend down and physically pick?

Most warehouse pick faces have so called "golden" zones. As you can guess, these are the "sweet spots" for picking. Typically these bins are closest to the end of picking line and usually at a level that is most convenient for picking. Alas such bins in the warehouse are few! They have to be; otherwise the pick face itself would become large and unmanageable. How then does one choose locators for an item with a view to the maximum impact in boosting productivity? Naturally you would want to slot your most active items to be placed in most convenient locations.

A distribution center typically consolidates demand across customers and then fulfills this demand using bulk sourcing. Bulk sourcing implies that items will be received in Pallets and master cases and later shipped to customers as eaches or inner packs. To handle this need, you need a pick face optimized for picking eaches (or another lower UOM) and a reserve area in the warehouse that is optimized for bulk storage. You also need to setup replenishments from reserve area to your pick face. The inventory levels to maintain in the pick face are also equally important. The minimum and maximum quantity to store in the pick face should be optimized to avoid stock outs as well prevent frequent replenishments from the reserve area to the pick face.

In order to determine an appropriate stocking policy for your pick face, you need to analyze the demand pattern of the item from the pick face and determine the location based on other constraints such as locator capacity. The parameters to be looked into are specifically the demand pattern and constraints. The demand pattern could be based on historically data or based on forecasts.

Demand Pattern

• Horizon: This is the time period in future for which you want to analyze the demand and determine the stocking policy. The demand for items is dependent on a number of factors such as seasonality, external events, promotions and stages in the product life cycle. To determine stocking policy you need to have a clear idea for item demand during the period e.g. if you want a stocking policy for holiday season, you need to know the time horizon during which this policy will be in effect and demand pattern expected for an item within this horizon.

• Pick Frequency: If the pick frequency is higher you would want to place the item in a convenient location to pick regardless of the quantity ordered for each pick. What this means is that you would place an item with 10 picks of 1EA at a favorable location as compared to say another item with 2 picks of 5 EA even though the net demand for each individual item is the same.

• Pick Demand: The order quantity for each item determines the stocking levels for that item in the pick face. If the demand is high, you would want to keep a high "Maximum" quantity so that frequent replenishments are avoided.

• Variability of frequency and demand: Does the item exhibit an irregular demand pattern during the time horizon? Is it likely to have a much higher pick frequency on a given day and no activity on some other day? This factor would determine if you want to dedicate a locator for an item in the pick face.

Constraints

• Item Dimensions: Item dimensions are an important factor in identifying pick face locations where the maximum replenishment quantity for an item can fit. You would also want to maximize the cubic volume available to you in the pick face.

• Item Attributes: These are factors such as crushability, weight, etc. If an item is crushable you would want this item to be picked up last. Similarly if an item is heavy you would want to pick this item towards the end of picking. Some warehouses also store items that are similar in appearance further apart to minimize the possibility of pick inaccuracies. For the same reason, it's never a good idea to commingle items in the same bin.
  

• Standard Packs: Most warehouses typically replenish in integer quantities of UOM (e.g. cases, pallets) to minimize material handling. Therefore standard packs determine the replenishment lot size.

• Storage Attributes: These are attributes that dictate locator capacity i.e. if the locator can hold the weight, has enough cubic volume to store the item and has dimensions to fit the item.

In the Part-2 of this post, I will discuss process that can be used to formulate the stocking policy in the warehouse. All that is fine and good but how can Oracle WMS help? That is a good question and will be addressed in Part -3 of this post. So stay tuned!

The previous post discussed parameters that influence stocking policy in warehouse pick face. The stocking policy should answer questions such as:

• What items should be stocked in the pick face?

• How much items should be stocked in the pick face?

• Where should a specific item be stocked in the pick face?

• How do you replenish items to pick face?

To answer these questions we need to simplify our approach. One way to do that is to use profiling i.e. grouping items and locators using Pareto Principle. The typical ABC analysis is an example of Pareto Principle. The idea is that certain items or locators can be grouped together in order to simplify analysis.

Locator Profiling

Profiling locators in pick face implies grouping locators into categories based on pick convenience. Pick convenience is a measure of the distance a pick operator travels to perform  a pick and the pick complexity required to perform a pick. Based on these factors, locators can be grouped into categories. A sample grouping could look like:

If you prefer a more graphical look, here is how the locator can be categorized:

Item Profiling

In order to perform this grouping, we need to analyze item demand patterns expected from the pick face. If the pick face in your warehouse is designed to store only Eaches, you absolutely need to remove full case or pallet picks from this analysis. We are also not concerned with the pick quantity either as we are assuming that the amount of time to pick is not not that dependent on pick quantity. Once the pick frequency is available, its possible to rank the items in descending order of pick frequency. Ideally you will see a small percentage of items constitute a large proportion of pick task.

Update: The following graphs shows this type of "Long Tail" distribution of pick activity. The picks for each item on the X-Axis are plotted against absolute number of picks on Y-Axis.

In this example, 80% of picks are coming from 20% or less items.

If the demand pattern in your warehouse is somewhat uniform across items, you will need a large proportion of items to cover a smaller percent of pick tasks.

Fat Tail Pick Distribution

In this example it takes 40% of items to cover 60% of tasks.

If you have a "Fat Tail" pick distribution, having dedicated locations in the pick face may not be that efficient.

Slotting

Next step is to use this information to slot items based on locator information coming from locator profiles. So for example, if there are 5 "gold" locators, you need to identify the top 5 items that constitute the bulk of pick activity, then identify the remaining items to slot the "silver" locators and so on.

By profiling locators and items, we have significantly simplified the problem. Instead of slotting hundreds or thousands of items into a few locators, we now have to slot a few item categories into a very limited number of locator categories. An obvious solution would appear something like this:

If a given slot could not be assigned to an item due to capacity or other constraints, that slot can be assigned to another item with a lower pick frequency. Not all items may get slotted based on number of locators available in pick face.

Fixed or Floating locators

What if the demand for an item or a set of items is highly volatile? Does it makes sense to assign a "gold" locator to an item on a lean day? Possibly not. Sure you can reslot an item during lean times. However reslotting is costly and could get complex especially when there are not enough empty locators.  A possible solution may be floating locators. In such a situation, the locator in the pick face is dynamically determined based on demand for that item on a particular day or even within a pick wave. Sometimes when demand is very volatile and non-uniform across items, its also possible to have a floating locator for an item with a "pick to zero" policy i.e. determine replenishment needs across all orders within a wave, replenish the total demand to pick face and draw down the locator to empty when the wave is completely picked.

Replenishment Policy

Since items that are stocked in pick face are usually fast movers, its not a good idea to have a stock out in the pick face. Therefore the locator should carry a minimum level of stock during the time it takes to replenish. Thus if it takes 1 day to replenish pick face, the average demand for a day should be the minimum quantity of stock at the pick face. The maximum quantity is usually determined by the locator capacity. To fully utilize pick face capacity, you may want to maximize the cubic volume of the pick face.

To reduce material handling during replenishments, its a good idea to specify replenishment lot size as the UOM that is stored in reserve area e.g. if pick face is replenished from reserve area storing master cases, specify replenishment lot size as the quantity of Eaches in a master case. This would ensure replenishments are done in integer quantity of master cases.

Clearly there are number of other factors that go into making an optimum slotting decision. Factors such as order correlation (slot items usually ordered together in the same aisle), crushability (slot crushable items at the end of pick travel path), item likeness attributes (slot like items apart from each other), etc. are important. However this simple approach should be a good starting point.

In the next post, I will cover how Oracle WMS can help in formulating and implementing a stocking policy for your pick face.