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

Read this similar story: 

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: 

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”.) 

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. 

For more information, check out this e-book: 

Success story

Mid-Continent Instruments and Avionics understands profitability and improves margins.

Logility helped Mid-Continent enjoy a quick ROI with improved product and service margins, superior data quality and root cause analysis enabled through comprehensive, intuitive dashboards.

Challenges

Mid-Continent Instruments and Avionics designs, manufactures, repairs, overhauls and exchanges aircraft instruments, avionics and advanced power solutions. 

To maintain the high levels of service and quality products Mid-Continent’s customers have come to expect, the company needed access to real-time sales and operational analytics to better understand key drivers of profitability by product, service and customer. 

Better, faster decision-making

Mid-Continent needed to drive decision-making through real-time queries, compelling dashboard visualizations and best-in-class analytics.

Understanding profitability and improving margins

Mid-Continent had plenty of data but not the means to tease out actionable information. Excel pivot tables hid subtleties and insight.

Solutions

From a feature/function perspective, Logility was selected based on ease of use, visual appeal, intuitive design and drag-and-drop dashboard creation. Another important capability was data consolidation from Excel and Mid-Continent’s ERP system. 

The team believed that starting the Logility roll-out with Sales offered the quickest ’wins’ and a good opportunity to build the skills needed for self-sufficiency. After Sales, Inventory and Purchasing were implemented. There are now up to 15 ’power users’ across the company accessing Logility every day.
 

 

Advanced Analytics

Data Management

Inventory Planning and Optimization

Lack of data was never the problem at Mid-Continent; teasing out the underlying meaning of the data and turning it into actionable information was the challenge. Using Excel pivot tables, subtleties were easy to miss. By contrast, while tracking the performance of a sales promotion for a key customer, Logility analytics surfaced a slow-moving product. Within two minutes, sales management understood the root cause and took corrective action, calling the finding “eye-opening”. 

“With Logility, answering questions — and the inevitable follow-up questions — takes just a couple of clicks,” says Lindsay Koster, director of finance. “For example, it’s very easy to change the time horizon on a report. Using pivot tables in Excel usually took us hours.” 

With Logility, Mid-Continent can also view transactions that fail to meet minimum contracted pricing. “Thanks to Logility, our transactions are now analyzed in real time. This helps the company better understand profitability by product, service and customer,” says Steve Macari, Director of Information Technology. 

Mid-Continent employees can now monitor activity and look for trends and exceptions every minute of every day. Performance metrics and “what-if” analysis can answer questions such as, “How are current sales promotions working versus goal?”, “What has shipped / can be shipped today?”, “What’s been invoiced today?”, and “Where will the business end up this month compared to goal? 

Another big win for Mid-Continent has been the improvement in data quality. Logility has helped the company detect holes and mistakes in its master data, most noticeably in pricing. Correcting these errors helped streamline operations and created a positive feedback loop by increasing the trust that employees have in the data. 

 

Results

With Logility, Mid-Continent was able to increase sales margins with a clearer view of pricing strategies, accelerate in-depth root cause analysis and reduce the amount of its slow-moving stock. Data quality improved, and employees came to trust the data they were seeing and using every day. 

With Logility, we’ve been able to identify sources of growth and attack those items and give them the diligence that they need.

Change is the only constant in supply chain and often can cloud our visibility. ChainLink Research examines the need for accepting change and the importance of discovery as two foundations to help supply chain teams move forward and succeed in their use of artificial intelligence (AI) and machine learning (ML). As supply chainers we have learned and already codified much about every little grain of data about physical inventory and its dimensions, where it can be stored, and various routes to get it to the customer. However, there is so much more data that must be turned into actionable insights. The goal of the article, AI / Machine Learning for Supply Chain – Into the Future, is to examine what the new machine learning-enabled supply chain team should do to set the stage for now and in the future.

Unexpected events – pandemics, weather conditions, recessions and more – are inevitable. How can you best protect your organization from supply chain disruptions? Advanced Analytics can help you weather the storm by optimizing your supply chain now and in the future.

This on-demand webcast highlights how a supply chain digital twin powered by artificial intelligence (AI) can quickly analyze the impact of potential changes to accelerate the speed and precision required to make informed decisions. The webcast also highlights real-world examples of leading companies that use advanced analytics to turn vast amounts of data into actionable insights to improve service levels, margins and speed to market.

Listen and learn how to use Advanced Analytics to:

  • Create a strong analytics foundation for modeling the unexpected, including using a digital twin to consider the impact of changes on business goals and customer service levels before you act
  • Make the best decisions quickly when the worst-case scenario happens, including quickly anticipates spikes or drops in demand using machine learning (ML) and artificial intelligence (AI) including robust demand sensing
  • Hear real-world examples of how companies are doing these things now

Supply chain transformation involves improving an organization’s abilities to make decisions about which products to keep in stock, where to keep them, when to replenish them, how to improve

service levels for customers, how to liquidate excess stock in the most profitable way and how to quickly respond to changes in customer demand. Supply chain planning transformation can enable real-time tracking and analysis of customer and product data, decision-making based on predictive and prescriptive models, and the use of new capabilities enabled by artificial intelligence, machine learning, social media and the Internet of things. It can also automate daily operational decisions to free up talent to work on higher value activities.

Supply chain planning is complex and a transformation initiative requires getting off to a good start with the support of senior management and a business case that outlines the benefits as well as the impact to the organization. This is a multi-dimensional journey that must ask the four following questions:

  1. What new process capabilities do you want your future supply chain planning platform to enable?
  2. What new data sources do you plan to utilize with your future supply chain planning platform?
  3. What new solution capabilities do you want to adopt to enable your transformed planning process?
  4. What new people skills will be needed to analyze data, operate new processes and use new solution capabilities?

Agile, data driven, speedy and highly automated supply chain planning operations are becoming increasingly critical in today’s fast-paced, global business world. This e-book provides practical steps and a best practice roadmap to guide you on your transformative journey.