Industry: Electronics and High-Tech
Logility is working with a leader in the distribution and logistics space that provides delivery to auto dealers for the dealers’ parts and services business.
This specialization allows them to be more efficient and knowledgeable about their customers and the specific parts they are delivering. Because these parts cannot be sold and services cannot be scheduled and performed until the parts are delivered, it is exceptionally important that they deliver on time and in full.
The problem this organization faced was knowing which routes were responsible for delayed deliveries and what was causing the delays. The company felt their routes and stopping points caused inefficiency, but they also wanted to forecast the impact of extraneous factors such as weather, road construction, and traffic patterns. With this information, they could adjust schedules to proactively service their customers, adjust delivery times, and gain greater efficiencies from their fleet. In addition, they wanted to incorporate the inventories from their parts suppliers and their geographic locations so they could minimize empty return-trip trucks and duplicate runs, as well as minimize the inventory that they had to keep on hand in their distribution centers while knowing which suppliers had what parts in stock.
Logistical Challenges
With 10 distribution centers, each with over 100 routes, and each route with over 15 stops, this posed a significant logistics problem. Add to that over 2,500 parts from over 200 manufacturers and this became a significant supply chain opportunity. In addition, each truck itself – and there are over 1,000 of them – is a constant big data generator, capturing location, efficiency, speed, right turns, left turns, stop times and the length of each stop. How do you coordinate all this data against the business issue of on-time deliveries with the greatest efficiency?
The Logility Difference
Using Logility, they first aggregated their enterprise inventory and logistics data with two external data feeds. Then they created a series of operating metrics to be used by managers as well as KPIs for reporting to executives. An important feature of these reports was predictive metrics. These metrics, using Logility’s predictive modeling capabilities, identify routes with risk factors and suggest the likelihood of one or multiple delays in the near future (weeks). Lastly, they distribute these efficiency reports and interactive dashboards using the Logility® Digital Supply Chain Platform. An important feature of this approach was deploying mobile versions of each report for traveling executives.
Today, everyone from the CEO down to the route dispatcher at each distribution center knows where any issues are and what needs to be done to correct them. It also allows our customer to be much more proactive in dealing with both the delivery points and their suppliers. As orders and inventory stocks can be readily shared, increasing parts production efficiency at the manufacturer and delayed delivery points can be more readily notified so they can more effectively reschedule their work loads. First-year savings from reduced inventory and greater fleet efficiencies are estimated at over $3 million.
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This post has been prompted by conversations I’ve had recently with a client in the early stages of implementing a Sales and Operations Planning (S&OP) strategy. They had been relying on a phased approach and they knew that they needed an integrated set of business processes to go with their newly purchased technology.
They understood that the focus needed to be on information, not just the volumes of data they had at hand. They knew that in order to implement a successful S&OP strategy, they needed clean, current, and accurate data. As with many organizations, time and effort was being wasted gathering data that had minimal importance to the overall project. But in this case, senior leadership was able to articulate the business problem they were trying to solve, and were able to help define, with some difficulty mind you, the minimum data necessary for the project.
It all sounds wonderful on paper, and they were destined for success! But like other businesses, their attempts to implement S&OP were frustrated by internal tensions between departments. What followed was this seemingly innocent statement on my part: “Not everyone will be a convert immediately, so we watch for resistance and address it as part of our strategy. Push, but not too hard, or we will get resistance.”
And that’s when the fireworks started. Or to be precise, my somewhat nonchalant mention of possible resistance sparked some great comments and questions.
Dealing With the Resistance
Classic best practice suggests that S&OP must ‘belong’ to the Chief Executive Officer. If that’s not possible then a strong, united coalition of department heads may be able to lead the process if they set clear ground rules and boundaries for working together. In this case, we had senior management buy-in and support, but what we really needed was their ‘ownership’ of the project.
Some amount of resistance is inevitable, and it usually boils down to cultural or people issues, not in any way exclusive to S&OP implementations, but let’s go ahead and tackle them in the context of an S&OP project. Here are the two most prevalent issues:
1 The internal obstructionist
This is the presence of a few highly regarded and influential employees who either passively or actively undermine the changes in behavior that the new initiative requires. You know you have this problem if the water-cooler conversation sounds something like this: “That new analytics program won’t work for us…”; “We’ve always done it this way…”; or, “That new initiative will make us have to change.” (Basically, anything eluding to “change is bad”.)
2 The dirty-data diversion
This is the belief that there’s no use starting an analytics implementation until the company’s data is polished, scrubbed, cleaned, pressed and folded to perfection. It’s the same argument some use to avoid going to the gym: “I’ve got to get in shape first!”
I’ll address the Internal Obstructionist in another post. Today, let’s grapple with #2.
The evil genius of the dirty-data roadblock is its apparent logic and deceptive concern for company well-being. In an analytics context, artful proponents of this argument appear to have the best interests of the company in mind. “We don’t want the C-suite making decisions based on bad data; that’s bad for all of us. Let’s get it cleaned up first.”
Of course what’s really at work here is what Seth Godin calls the assertiveness of the lizard brain, also known as ‘the resistance’, also known as fear. No one wants the bright light pointed at their bad data or poor processes.
And since no data-scrubbing project has ever succeeded, there’s little risk in promising a thorough clean-up as a prelude to an S&OP kick-off. That’s the diversion. Confucius might say it like this: “If you think you need to finish before you can begin, you will never begin.”
Here’s how to overcome this type of resistance in three steps:
1. Inject some honesty. Everyone knows there are data quality issues. Yes, even management knows. In fact, they’ve known for a long time. That’s not news. The point is to work together to improve the speed and decision-making ability of the enterprise, not place blame. This gives everyone who needs it some ‘cover’ and puts the lizard back in its cage.
2. As soon as possible, start reviewing some basic KPIs in the new S&OP system.
Embrace the fact that the data isn’t where it needs to be. Make reliable data part of the project, not a prerequisite. Doing this will help everyone envision the desired future and will set the stage for some quick wins. And quick wins will unleash the dynamic duo of momentum and optimism. The system should be viewed as a catalyst for operational improvement, not a tyrant that demands operational perfection to function.
3. Meet often, especially in the beginning. In fact, consider daily meetings. More importantly, set overall priorities in a way that gives the team the time and the freedom they need to make progress between meetings. This helps reinforce everyone’s commitment to a successful outcome. In every meeting talk about how awesome life will be when the system is trusted and providing accurate information.
Nike got it right with its slogan, “Just do it”. Starting has its own virtues.
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