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.
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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The science and practice of predictive analytics is well established and rapidly gaining ground in the public and private sectors. It’s not magic anymore because we now have advanced analytics systems that harness and organize massive amounts of disparate data and model that data in ways that allow humans to be proactive and make informed decisions. Take a moment to read our extremely popular post on selecting the right descriptive, predictive, and prescriptive analytics here. To review:
|Type of analytics||What does it do?|
|Descriptive Analytics||Answers the question, “What happened?” Uses data aggregation and data mining techniques.|
|Predictive Analytics||Answers the question, “What could happen?” Uses statistical models and forecasting techniques to understand the future.|
|Prescriptive Analytics||Answers the question, “What should we do?” Uses optimization and simulation algorithms to suggest the best course of action.|
The Science of Predictive Analytics
Predictive analytics encompasses a variety of statistical techniques from predictive modeling, machine learning, and data mining that analyze current and historical facts to make predictions about future or otherwise unknown events.
Predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. These models capture relationships amongst many factors to allow assessment of risk or potential associated with a set of conditions, guiding decision-making with better accuracy and significant cost savings.
Who Knew Supply Chain Optimization and Crime Fighting Were Related?
Let’s examine two popular applications: supply chain optimization and crime fighting. How can predictive analytics effectively address these seemingly unrelated topics? Because at a macro level the issues are identical. Consider this abbreviated chronology of our quest to make better, faster, data-driven decisions regardless of the setting and the objectives:
- We had no data. We used unstructured observations and gut feel
- We got some data, but it was incomplete and resided in silos
- We got more (and more comprehensive) data, eliminated silos, filled in the gaps, but lacked modeling tools. This was the era of data-rich but information-poor.
Today, predictive analytics tools allow us to compare possible outcomes of events using scenario analysis and foresee challenges and potential disruptions before they happen.
Supply Chain Optimization – Use Case for a Domestic Brewery
Our supply chain optimization use case comes from a top-10 domestic brewery that used Logility to gain better insight into production. Before Logility, this brewery had plenty of data, but was unable to make sense of it and “make it tell us something useful about the future”. Sound familiar?
The data needed to be more easily translated into actionable information for managers and executives. The company had a variety of tools in-house, but the fragmented technical environment was too difficult to manage for quick scalability. They needed a powerful, analytics-driven solution to integrate and transform the data from their disparate systems, along with a front end for visual analytics, designed for the specific challenges of the beverage industry.
A key point of differentiation for Logility was the ability to link multiple data sources to a single supply chain planning platform with reporting and analytics capabilities built into the functionality. Logility’s rapid integration framework enables a one-time setup of the platform, followed by easy report creation and access to analytics by business users. An early win included creating a daily shipments and depletions report for the CFO. Using a mobile-ready interface, the CFO can quickly scan variances each morning and immediately drill down to SKU and account-level data to see what’s driving exceptions.
Based on these early victories, the brewery believes that the early detection of production efficiencies will yield $550,000 to $800,000 savings within 18 months. In addition, the company points to a two full-time staffing equivalent reduction (about $300,000 annually), and the value of faster decision-making by business managers.
The Crime Fighting Analogy
Now let’s consider the case of crime fighting and a diagnostic technique called Risk Terrain Management (RTM). The premise of RTM is that location matters. However, it’s no secret that location matters. The question is: how do you utilize data that you currently have to assess spatial risks and prevent undesired outcomes? RTM helps in this process. With a diagnosis of how the environment correlates with certain behaviors or outcomes, you can make very accurate forecasts.
The reason RTM is used by practitioners across many disciplines, not just law enforcement, is because it was originally developed to solve a problem faced by many: how to leverage data and insights from various sources, using readily accessible methods. RTM gained fame as a crime prevention tool, but today it’s being used in urban planning, injury prevention, public health, traffic safety, pollution, and stopping maritime piracy. (Note that this is at its core the same problem the brewery faced, only the vocabulary and the objectives are different.)
In the context of crime prevention, the RTM process begins by selecting and weighting factors that are geographically related to crime incidents. Then a final model is produced that basically ‘paints a picture’ of places where criminal behavior is statistically most likely to occur.
With knowledge of spatial risk factors, intervention activities can be designed to suppress crime in the short term and mitigate the risk factors at these areas so they are less attractive to criminals for the long term. For instance, in one National Institute of Justice (NIJ) study, a 42% (statistically significant) reduction in robberies was achieved by focusing on environmental features of high-risk places, not merely the people located there. With RTM, you can prioritize risk factors and prescribe actions to mitigate these factors, even within the confines of limited resources.
As you can see, predictive analytics and the underlying tools that support the discipline can be applied in many settings. People like to solve problems, but they need the right information. As business leaders we need to make sure they have it and then set them free.
Interested to know where your ROI from a supply chain analytics platform will come from?
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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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