 Cubic velocity calculation is used for storage equipment selection. The formula consisting of the average quantity ordered, the product’s dimensions, the desired pick location, the number of days on hand, and the pick unit of measure (full case or piece).
Multiply the average quantity ordered by the product’s cubic dimension to calculate the cubic velocity per day.
Then, to define the equipment location size required, multiply the days-on-hand inventory target by the cubic velocity per day.
Based on the resulting cubic velocity needed to support the days on hand in the picking area, you can identify the equipment required. Equipment types include modular drawers, bin shelving, standard shelving, carton flow racks, decked racks, pallet racks, and pallet flow racks, floor stack, etc. Once you match the cubic velocity with the equipment type, you can organize the equipment within the layout in zones for efficient picking.
In warehouses, distribution centers, or even stores the placement of each item can be a science, sometimes it’s an art, often it just is what it is. Stuff goes wherever it will fit, entropy kicks in and randomness takes over. Then before you know it there’s little rhyme or reason as to which items go where.
Product Slotting is defined as finding the optimal location of product in a warehouse or distribution center for the purpose of improving material handling efficiency. Sometimes called inventory slotting, profiling, or warehouse optimization slotting identifies the most efficient placement for each item. Product slotting depends on a variety of factors such as picking volume and frequency, receiving and put-away volume and frequency, package dimensions and weight, picked package size, storage package size, material handling equipment used, layout of the facility, labor rates, etc.
Continue reading Slotting Defined
Evolutionary Operations, first described by George E. P. Box and Norman R. Draper in in their book Evolutionary Operations – A statistical method for process improvement, New York: John Wiley and Sons, 1969.
EVOP is Continuous Improvement + Design of Experiments.
Basic idea is to replace the routine operation of a process by continuous and systematic plan of slight adjustments of the control variables. The effects of the adjustments are then evaluated just as with DOE. The process is then shifted in the desired direction of improvement. In many product and service processes it is impossible or very expensive to do DOE, especially where trials can be disruptive or the process owner would let you have the necessary time, materials, labor to run your experiments. So rather than running experimental production runs you use actual production by shifting off the base point left, right, up, down all within "spec".
Continue reading EVOP – Evolutionary Operations
Here’s another example for “sizing the wheel” for a mixed model fixed repeating schedule.
Given:
- Changeover Time = 100 minutes
- Available Production Time = 2 shifts * 7 hrs/shift * 60 min/hr = 840 min/day
and
| Product |
Daily Demand (pcs) |
Cycle Time (min.) |
Load (min.) |
| A |
80 |
5 |
400 |
| B |
40 |
4 |
160 |
| C |
20 |
4 |
80 |
| D |
10 |
6 |
60 |
|
|
|
700 |
Now some math:
- Total Load = 700 min.
- Time Available for Changeovers = 840 min/day – 700 min/day = 140 min/day
- Changeover Time for group = 100 min * 4 products = 400 min/family
- Changeovers per day = 140 min/day / 400 min/family = 0.35 group/day or a Replenishment Time of 2.85 days/family
So the fixed repeating wheel will turn once in 2.86 days. Production run sizes as follows:
| Product |
Daily Demand * Replen Time |
Cycle Time (min) |
Load (min) |
| A |
229 |
5 |
1145 |
| B |
114 |
4 |
456 |
| C |
57 |
4 |
228 |
| D |
29 |
6 |
174 |
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|
|
2003 |
Plus 4 changeovers of 100 min each = 2403 min = 2.86 days.
When it comes time to run product A, run 2.86 days worth. Got it?
Would be nice if we could run just one piece. But until we can make the 100 minute changeover go away we’re stuck running a batch of some size.
 Running out of room? Consolidating operations? Relocating to a new location? Need to liberate some cash? It’s time to purge your warehouse! When looking at all the stuff that’s accumulated in your warehouse over the years you’ll often find orphans, cripples, mistakes, bad dreams lurking in the far reaches of the racks and tucked away in the back corners. Where to start? You can use the white glove test – the thicker the dust on the case the more likely you can do with out it. Or look to see how many physical inventory tags are on the box – more than two, then throw it out!
Excess and Slow Moving inventory is defined as the quantity above a specific need such as beyond a certain time period of demand or days of supply. Excess can also be determined as inventory beyond current safety stock level plus lot size (order quantity). Excess inventory is almost always a result of poor stck demand management. Excess stock can result from over delivery from a supplier, but morelikely bad ordering and demand management. It’s easy to blame the buyer, but buyers rarely create the sales forecast, maintain the sales orders, set the performance metrics, or the service policies.
A common analysis is to rank sort the parts by their recent sales as shown in the graph here. Where to attack? Head for the Tail is a common approach. If it’s not selling let’s dump it, goes the conventional thinking.
ABC Analysis can be misleading; some times the tail has pearls, or at least consequences if you blindly purge.
Some things you’ll find in the Tail:
- Lifetime Buy – part, material, component is going out of production and you need time to find substitutes. Common these days with RoHS and electronic parts.
- Brand New parts – don’t have any sales yet obviously.
- Seasonal "Murphy" Stock – winter is coming and a key supplier is on the other side of the Rocky Mountains.
- Economic Order Quantity, often abused, but can be a good business decision.
- Supplier order volume deep Discount – a really sweet deal, see EOQ.
- Commodity price hedging – if you are a commodity buyer you know what I mean. Example copper prices in 2006 and 2007:
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