Demand Segmentation Examples December 14, 2008
Posted by Lawrence Loucka in : Definitions, Lean Sigma, Logistics, Supply Chain , add a commentHere are several examples of Demand Segmentation Analysis:
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5 Rules of On-error Training July 6, 2008
Posted by Lawrence Loucka in : Definitions, Lean, Logistics, Sigma , add a comment
1. Ownership Rule - the person who first detects the problem is responsible for finding the root cause of the problem.
2. Quickly Rule - the problem must be dealt with and solved within 30 minutes, not put on a list or in a report for action at another time.
3. Actually Rule - if possible play back or recreate the process that occurred before the defect.
4. Support Rule - the person who detects the problem has primary responsibility for solving it, but supervisor and fellow workers can stop working and lend problem solving support.
5. Shut Up Rule - the discoverer is expected to solve the problem and be allowed time to dicsuss the problem and attempt to solve it. Others can help but the supervisor or manager must keep quiet and give the person a chance to solve the problem.
Demand Segmentation November 8, 2007
Posted by Lawrence Loucka in : Definitions, Lean, Lean Sigma, Logistics, Supply Chain , 2comments
Traditional Product Quantity (PQ) or ABC analysis fails to recognize that high volume may not be predictable and that low volume can be. So when trying to determine which products may lend themselves readily to pull techniques we can run into trouble if we don’t understand demand variation. Get some demand data - build a spreadsheet or table listing products in rows and demand in columns, calculate average demand, Cv (the standard deviation divided by demand average), and plot it as here. Here are some considerations:
- Altitude - this analysis typically starts with customer demand but can be done by market, customer, finished goods, in-process, raw materials, suppliers i.e. anywhere along the supply chain where there is consumption.
- Time Bucket - depending on the ‘clock speed’ of the enterprise the demand data can be aggregated by month, week, day, or hour. When gathering data start with the smallest time period possible. It’s easy to convert daily demand to week or month but impossible to take monthly and say anything about daily demand.
- Unit of Measure - the units of volume may be straight forward, for example ‘pieces’ or may be complicated due to different value streams or product lines having a mix of units. You may need to convert your data to a common unit. Dollars can be a common unit.
- Demand - sometimes not easy to get. Shipments may not represent true customer demand, especially if on-time delivery and order fill aren’t very high. Getting original customer demand can be difficult if the customer order entry system forces ship complete or when back orders lose original request dates (and quantities).
- Geography - like altitude, this analysis can be done by production cell, value stream, plant or DC, business unit, or enterprise.
- Horizon - use forecast to determine volume, especially if product life cycles are short. Using history to predict future volume is like driving by looking in the rear view mirror - it can be done, but reaction time is a little slow, and its hard to see the ‘cliff event’ until it’s too late. Sometime forecast is crap or isn’t available, so then use very recent history.
- Timeline - usually want at least 25 data points to calculate a meaningful standard deviation of the demand history.
- Scrub - other than filtering out abnormal orders consider weekend transactions, huge one-time orders, and zeros. Excel and Access treat blanks and zeros differently. As you take the transaction log and build a pivot table of part number vs. date you’ll have cells with no data because no transactions occur on that day for that item. Use search/replace to replace blank with zero. But for new products coming to life during the study period you might leave cells blank prior to the launch date. Similarly if a product has regular demand, say Monday, Wednesday, Friday you might want to leave Tuesday and Thursday blank.
- Plot - volume vs. Cv
- Interpret - Cv’s less than 1.0 lend themselves to flow and pull techniques. Cv’s less than 0.5 can often be handled with rate-based replenishment methods. Remember a Cv of 1.0 means the demand variation is a great as the demand average. Say a part has an average daily demand of 100 with a Cv of 1.0 the demand one day could be zero and the next 200 or more - not very predictable. High Cv items are usually low volume, but not always. Take a look at the three data points in the top center of the graph above. Must be a story here - why are the highest volume parts so unpredictable?
Muda in the Warehouse September 25, 2007
Posted by Lawrence Loucka in : Consulting, Definitions, Lean, Lean Sigma, Logistics, Supply Chain , add a comment
Although created in the manufacturing environment of Toyota by Taiichi Ohno, the Seven Wastes can be found almost everywhere, if you learn how to see them. Here’s some lean thinking for the warehouse:
Overproduction - Think about the consequences when consumers, retailers, wholesalers, distributors, and manufacturers justify "just in case" or Murphy stock as a hedge against unplanned demand. Money, time, people, physical assets, the environment have all been tied up for something that isn’t needed.
Waiting - the ‘hurry up and wait’ of trucks sitting idle or drivers killing time awaiting their turn at the dock, or DC workers or lifts standing by waiting for tools, instructions, materials to arrive or to be taken away. Waiting comes from poor layout, lumpy demand, system batching. Then once the blockage is cleared we hustle.
Defective Product or Service - from picking errors, incorrect order quantities, misplaced stock to shipping on the wrong carrier or the wrong mode these errors consume resources of time, people and materials to no useful end. Worse yet, additional resources, often 2 or 3 times the original, are usually needed to correct the error.
Overprocessing - how about dock audits, redundant approvals, pick/pack/ship audits, cycle counting? Another example of overprocessing the the warehouse is the failure to rationalize the supply base and concentrate relationship management on a few top-tier suppliers. What about rationalizing the carriers? Both result in inefficient duplication of resources, decisions, and communications.
Moving Product - like overproduction, the unnecessary movement of product can occure both within the warehouse and throughout the entire supply chain. Too many steps, too many stops, unnecessary movement from suppliers though master DC’s to regional DC’s for further deployment to customers can be deadly drivers of cost and time, labor, and space.
Moving People - in the warehouse an enormous percentage of people’s time is devoted to movement, such as picking, put-away, and replenishment. If a facility isn’t well laidout with easy access to "A" items an enormous amount of time can be wasted in traveling empty. When good aren’t where they’re supposed to be the movement to the wrong location is both a defect and a waste of human motion.
Ineffective Inventory Control - creates waste a several levels. Excess inventory based on bad inventory data diverts limited capital into creation and maintenance of waste. Excess inventory results in consuming valuable storage space to hold unnecessary goods. A scarcity of items, on the other hand, results in stock outs, expediting, or lost orders.
50 things to do to free up warehouse space August 31, 2007
Posted by Lawrence Loucka in : Lean, Logistics, Supply Chain , add a comment
Business is growing and running out of space in the warehouse. What to do before moving to a new facility or pouring concrete? Fifty things to consider:
- Cross dock
- Narrow aisles
- Double deep racks
- Bridges over aisles, cross aisles, aisle ends, truck doors
- Re-slot forward pick locations
- Relocate slow movers and consolidate
- Change batteries rather than park and charge
- Pushback racks
- Pallet flow racks
- Carton flow racks
- Carousels horizontal or vertical
- Use uprights that only go to the top beam, close pack the top deck
- Shorter beams; 96" not 108"
- Triple wide beams
- Vary beam heights
- Double stack pallets
- Mobile shelving
- Purge excess, slow moving, obsolete
- Improve put away and pick cycle time and then cut safety stock
- Direct or Drop Ship
- Drop items from the catalog
- Put carton flow racks under pallet racks
- Put pick shelves and bins under pallet racks
- Slip sheets or low profile pallets
- Daily delivery of new pallets and packaging supplies
- Check out bound while picking or loading
- Check in bound while unloading or put away
- Store more than one item per shelf or pallet
- Consolidate partial pallets, cartons, bins
- Receive and ship on different shifts
- Redesign package
- Optimize pallet stacking pattern
- Select the right pallet
- Buy/Make to Order
- Buy in smaller lots
- Ship in smaller lots
- Receive and ship more often
- Make inbound receipt appointments
- Make delivery appointments
- Spot out bound trailers & load directly into trailer
- Eliminate inbound inspection
- Recalculate safety stock
- Recalculate order quantities
- Sell slow moving, return for credit, fire sale
- Donate, scrap, recycle obsolete
- Take assemblies apart and sell spare parts
- Combine parts in to kits
- Reduce the in and out queues
- Control SKU proliferation
- Pick directly into the shipping container