Nov
23
Using wave management to improve efficiency in the warehouse
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Efficient utilization of the labor force is often a key objective for many warehouses. Grouping transactions is often an effective method as it facilitates better staff utilization and can shorten overall transaction times helping to better meet schedules and requirements.
Within logistics this grouping of common activity is called wave management. Wave management can relate to various tasks including allocating stock/inventory, picking, packing and cycle counting.
Effective wave management intelligently groups warehouse activities prior to “releasing” duties to the workforce. The resultant batch of work is carried out together in an allotted time window.
Warehouse management systems are often used to facilitate better wave management (this is common rationale behind many businesses cases looking to implement WMS as it generates improved productivity form the workforce.).
Whilst wave management doesn’t require technology – the advantage of utilizing a WMS for wave planning is that the WMS is better equipped to quickly process the levels of transactions, look for synergy and produce an output plan that the team can follow.
But what activities should you group?
Whilst businesses may view logistics activity in a holistic framework tasks can be broken down into smaller common groups. For example
Picking
Picking is often one of the first activities to be reviewed by wave management. As a process, its probably one of the most labor intense which provides one of the greater opportunities for improvement. Many organizations look to simplify picking by grouping the activity around customers, product groups or destination.
Routing
With routing and picking being so closely aligned its not a surprise to see that wave management can offer improvements. Routing management will usually commence prior to picking (determining number of parcels pickups, schedules, locations etc). Once completed it easier to establish picking requirements.
Cycle counting
Most organizations do not shut down for physical inventory counts. And as such many organizations look towards perpetual stock checks as a means of validating stock. Whilst often an overlooked process, developing a suitable schedule and process which takes account of labor efficiencies can still pay dividends
Packing
Packing activities, reliant on completed pick tasks can be segmented to take into account of customer specific handling requirements (e.g. special packaging or sorting).
Replenishment
For many organizations (especially those in high volume manufacturing situation) stock replenishment is a key task and one which can benefit from wave management. Once again this task is closely coupled with the pick wave. Replenishment tasks are initiated to re-stock Kanbans or forward holding storage locations. Following a pick wave the replenishment wave moves the inventory in the pick locations to the new stores location.
Summary
Wave management can greatly improve the use of labor resulting in increased productivity. There can be many types of waves many of which are closely related. Dedicated warehouse management systems (WMS) usually have some form of wave management functionality.
Nov
23
The importance of supply chain data integrity should not be overlooked.
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The recent report from Oracle presented data issues and information quality as being a key facilitator in lost business revenues and inefficiency (you can see the report here http://emeapressoffice.oracle.com/Press-Releases/Oracle-Research-Reveals-1bn-Cost-of-Fragmented-Supply-Chains-183a.aspx). The Oracle report, indicated that executives spent a significant time reviewing information, but over 30% suspected that physical products flowed better than the data within their organizations.
When you consider the sheer volume of data that is created and managed by a typical supply chain this is unsurprising. Consider the following data that most organizations will have within their systems
• Open Purchase Orders
• List of suppliers
• Price lists
• Parts Master
• Customer Lists
• Authority lists
• Material demand and requisitions
Now consider the impact of that data either being incorrect or slow to move from one place to the next. Whilst it’s a key business issue its one that is all too common.
Lets look at one example – the parts master. Typically controlled within the ERP system most parts master databases will consist of part numbers, descriptions, unit of measure etc. Where there are no robust processes providing controls it is not uncommon to find that
• Obsolete parts remain live within the database
• Part number syntax rules are not adhered to and the part appears multiple times in various guises (e.g. ABC123, ABC-123, abc1-23) resulting in confusion
• Free text is just that – consider – “Metallic clip”, “clip, metallic”, “clip”, or even the dreaded “-“ killing any hope of reliable management information.
• Associated data (e.g. unit of measure, Min/Max buy quantities) are incorrect or poorly maintained
• Etc etc etc
The impact of these errors and inconsistencies is that data that triggers decision making is poor (often resulting in the role strategic choice) and the processes that consume this data are inappropriately slow and cumbersome.
Many systems do not make managing data simple
One of the key issues especially within the supply chain is that there are often numerous (stand alone) systems at play which requires manual alignment of key datasets. For example, the procurement system and financial systems are often divorced but supplier data needs to be shared between them to maintain bought ledger requirements. I have lost count of how many times I have reviewed live supplier lists that include terms such as “Do not use” or contain multiple version of the same supplier with different spellings. Managing and maintaining isolated databases can be a key reason for poor data with companies often overlooking the benefits of system integration.
The result
But why is this important? Purchase orders still get sent, suppliers are still paid, inventory is still supplied. The true cost of poor data is efficiency and agility. Organizations that struggle on a daily basis with data are most likely both un-responsive and unaware of true performance (and therefore how they can improve). This does result in a cost to the business and at worst poor processes that are executed without true requirement.
In today’s highly competitive market this is not a problem that businesses can afford to live with. How do you solve it? Business must make data integrity and communication seriously it must become a key strategic objective. Resources must be appointed to ensure quality and the businesses must work with its supplier network improve data across the supply chain.