3 Ways Enterprise Labor Management Systems Fail

Posted On : Dec 3, 2014

Labor Management technology is not new. Yet only 10% of warehouse logistics environments have an LMS. The rest are relying on spreadsheet data and their WMS. Part of the reason is that enterprise LMS are prone to failure. Troubled LM implementations are costly, at $150-$500K per package; and swift, taking an average of just ~18 months for complete abandonment of the program.

So what causes Enterprise Labor Management to fail?  In a word: COMPLEXITY.

1. Implementation and Billable Hours: The Wrong Incentive

In Enterprise Labor Management, there are typically two implementation groups involved, both of which are paid on a per diem basis: a consulting group and a software group.  The consulting group manages the program standards, configuration, and process.  The software group provides additional consulting to customize the software.  Because both groups are billed on a per diem basis, they are incentivized to work more.  More complexity drives more billable hours, which drives more revenue for the consultants.

Manhattan Associates, an Enterprise WMS company,  gets 78.8% of its company revenues from consulting services. At approximately $1B annual revenues, there are serious financial incentives for providers to cling to this model.

Failure occurs because the needs of the client – getting a working system up and running as fast as possible – and the provider – billing lots of hours – are at odds. Statistically speaking 55% of enterprise implementations fail. So during implementation it’s important that your needs align with the software provider.

2. Rigidly coupled architecture

Enterprise systems can only handle data in a very specific format. It does not adapt and bend to your data. Enterprise providers will require data to be served up according to their own requirements – which requires up front data manipulation by IT staff or – again – enterprise consultants.

In SaaS Labor Management, the LM architecture is adaptable to any software feed, so the client’s software “talks” to the LMS software in a simple, easy-to-understand manner.  The SaaS model begins by accessing a client’s data, using a regression model approach with a small dataset.  This enables significant progress to be made in the early stages of the project, without the large cost of upfront industrial engineering.

SaaS models also prevent the conflict of interest that’s common in consulting.  SaaS revenue is generated by a successful software implementation – plain and simple.  The incentive is to get the program implemented and running as quickly as possible.  If a SaaS solution adds value quickly, the client will (hopefully) buy more software.  Successful software rollouts build happy customers, which lead to revenue for the SaaS provider.

3. Change happens, but it’s costly

Let’s say you land a new customer that requires a new process. And then another customer, and suddenly you have made 15 changes. Guess what – your software becomes outdated and is rendered obsolete because the LMS was designed to measure your old processes before the change happened. Change is expensive with enterprise systems. An enterprise LMS company will require you to either have an IE on staff, or to hire one of theirs, to constantly keep measuring and changing the standards each time you change one small thing in your operation.

The better way is to have leverage big data to refine and re-calibrate your standards after each change. It only takes a push of a button, and 5 minutes. Big data is smart enough to gather historical data and figure out what the new standard should be – or to measure it and compile say, 30 days, worth of data, process it, and define the new standard that way. It’s better to re-optimize using data than the consultant-heavy enterprise model.

Once complete, SaaS Labor Management solutions can be redone as the client’s operation changes, simply by re-optimizing the operation through the software model.  The SaaS solution begins with the data, then moves to an Industrial Engineer, if desired, to interpret and fine tune the productivity goals.  We believe it is the perfect combination of leveraging Big Data with the value-add that an Industrial Engineer brings.  Modern SaaS Labor Management tools bring the “more, better, faster” to implementation and improvement – making failure virtually obsolete.

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