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by Alexa Cheater

Focus on a practical approach to implementing artificial intelligence (AI) and machine learning (ML) in your supply chain planning.

A pragmatic approach to getting started with artificial intelligence in supply chain planningThat’s the advice from industry-leading experts, as heard in our recent webinar, A pragmatic approach to getting started with artificial intelligence in supply chain planning, now available on-demand. Hosted by Robert Bowman from SupplyChainBrain, guest speakers Brian Tessier from Schneider Electric, Paul Cocuzzo from Merck and Trevor Miles from Kinaxis, discussed what you could do right now to take advantage of this trend.


From finding a practical application that provides tangible value for your business, to ensuring you have the right organizational structure in place, Tessier and Cocuzzo provided real-world advice driven by their organization’s own quests to implement AI in supply chain planning.


“Having AI take a world view based on best practices, and then applying it to your legacy data structures and business rules can give you insights into things you don’t even know are problems for you,” notes Tessier. “We found very quickly we had some very poor assumptions about lead times, both from suppliers and interplant shipments from within our supply chain. Given the number of transaction we do, the complexity of our product portfolio and the number of entities involved, there’s no way we would have found this any other way.”


“You need to get out of the classroom and into the lab,” explains Cocuzzo. “You need to experiment as often as possible. Don’t be afraid to fail. You really need to get yourself out there practicing with these new capabilities, attempting some of these experiments with these new technologies. And you need to start to thinking about how you’re going to execute both on these experiments and for those experiments that are successful, how are you going to bring them forward?”


Speaking on the required talent and organizational structure to implement AI, Miles added, “It’s about having the right combination of skills, because you do need to go and make those algorithms do stuff, but you must always know it in the context of the overall business objectives that you’re trying to achieve.”


Interested in hearing more from these experts on how to implement AI and ML in your supply chain planning? Watch the complete on-demand webinar now.




The post [On-Demand Webinar] Getting started with artificial intelligence in supply chain planning appeared first on The 21st Century Supply Chain.


Demand planning: 4 ways to outplay your competition


Originally posted by Alexa Cheater at

by Jonathan Matthews

Supply Chain Software Christmas Story T’was the night before Christmas and all through the house, not a creature was stirring, not even a… well, that’s not quite correct.


There was some stirring in the Clause house as Santa slowly shifted positions in his easy chair, looking out the window into the howling snow storm outside. The weather forecast said the storm would be blowing over soon and, and truth be told, it wasn’t that bad of a snowstorm. Certainly not like the famous storm eleven years ago.


But the weather had been poor the past month, and certainly not conducive for the last minute production crunch leading up to Christmas. Mentally preparing himself for the insanity the next 24 hours would bring, Santa mulled over the past year.


He should have been notified about production delays by now, but strangely, nothing had crossed his plate. Not even the unsavory incident he had overheard about in early November, when an elf drank a little too much eggnog and broke machinery, leading to a production delay. Even in the best years there are delays and problems. Right?


“Honey,” Santa Clause said softly, getting Mrs. Clause’s attention from the book she was reading. He noticed that it wasn’t her usual novel type book, but rather looked more like a technical manual. Without his glasses on, Santa didn’t try to strain his eyes to make out the title; he’d ask her later about it.


“Why haven’t I been told about the production delays? Tonight is, after all Christmas Eve and with all this poor weather we’ve been having, I’m sure we’ve run into delays. Yet nobody has said anything to me about it!”


“Well,” Mrs. Clause responded, “You haven’t been told anything because there’s been nothing to report. You know the old saying, no news is good news!”


With a puzzled look in his eyes, Santa sat upright in his chair and turned his focus on Mrs. Clause.


“But how can that be?” exclaimed Santa. “Remember the great present shortage of two years ago? That was a disaster! I had to “borrow” some toys from the local toy stores,” Santa gestured air quotes around borrow.


“Well,” Mrs. Clause responded “After that Christmas, I met with Alabaster Snowball and the other department head elves to discuss how we could better prepare for future problems that we may run into. We brought in a supply chain expert who suggested using a specialized supply chain software rather than just the worksheets we had cobbled together.”


“Go on,” Santa said, shifting forward in his chair with keen attentiveness.


“We moved forward, bringing in an evaluation team,” Mrs. Clause explained, pausing to snicker as she recalled the events. “The team knew we would be their biggest implementation ever, by a long shot, but they really hadn’t expected us to have a Bill of Material for every toy made that year! One even fainted in disbelief! Poor fellow. Fortunately, Pepper was nearby with some of her famous eggnog.”


“Oh yes!” Santa said, his eyes growing wide. “Her eggnog would be world famous, if she hadn’t signed my NDA, ho ho!”


“Once the consulting team overcame shock, we were able to quickly and efficiently get the software implemented last year, with go-live in January, perfect for starting this year. We were able to use the software to account for every scenario we may encounter, like poor weather! On top of it all, we were able to work around that nogg-induced production shut-down we had last month.”


“Hmmmm….” Santa pondered, stroking his long white beard hair, recalling that stoppage. “I remember the sense of urgency, but nobody panicked! I never heard much of it since, so I just sort of, let it go!”


“And thanks to our excellent software, we were able to accommodate that outage, so we really didn’t lose much production at all.”


Santa, now standing and stretching, became even more curious.


“And what of the weather these past couple of months?”


Chuckling to herself, Mrs. Clause responded, “Do you want me to go into the advanced analytics behind our what if’s?”


“Ho, Ho, Ho,” Santa laughed, “I knew there was a reason I married you! I shall leave that to people smarter than me. After all, I’m just the guy that travels at light speed!”


Santa walked over to Mrs. Clause, bent down to kiss her cheek, and captured a glance at the title of the book she was reading — Strategic Supply Chain Essentials and Master Management Planning!?!


“Well, that explains the book,” Santa said “But tell me, since when did you become a supply chain expert?”


To which Mrs. Clause said with a wink, “You don’t think I sit around all year just baking your cookies? Somebody has to get some real work done around here!”


With another jolly laugh, Santa headed towards the coat rack and silently mumbled under his breath, ‘advanced this-and-that’.


“I heard you,” Mrs. Clause said. “And don’t think I haven’t met with the master supply chain planner to account for ALL possibilities… including any and every *cough* unfortunate happenings.”


This caused Santa Clause to stop dead in his tracks. He looked at his wife with a hint of concern.


“Don’t worry Dear Santa,” Mrs. Clause re-assured him, “I learned how to fly that fancy sleigh just this summer. And with a little tailoring, your jacket will fit me just fine!”


“I don’t know if I should be worried, or if I should be surprised!” remarked Santa. “But then again, that’s why I married you! You are amazing, always thinking ahead!”


And with that, Santa flung open the front door while bellowing out, “Ho, Ho, Ho! Merry Christmas to all!” And walked through the door.


In the comfort of knowing his wife and elves had a mastery over the entire North Pole Supply Chain, Santa would never need to worry again. Now he could focus on his expertise, delivering presents to children in peace.


“Merry Christmas to all, and to all, a Happy Supply Chain New Year!”


The post A Supply Chain Software Christmas Story appeared first on The 21st Century Supply Chain.


Demand planning: 4 ways to outplay your competition


Originally posted by Jonathan Matthews at

by Alexa Cheater

Outplay your competition with a smarter, stronger demand planning strategy

Demand Planning GameCustomer demands are changing. So why isn’t your demand planning strategy? It’s time to level up your demand planning and experience revolutionary breakthroughs in supply chain performance, planning and profitability.


Demand complexity is increasing thanks to consumers who now want more customization, omni-channel purchasing options, rush delivery, easy returns, and environmentally and ethically crafted merchandise, just to name a few present-day requirements. So how can your supply chain handle it all?


The key is to recognize solving today’s demand planning challenges just isn’t possible with yesterday’s dated processes and technology. It’s like trying to play Call of Duty: WWII on a system designed only to handle the technical requirements of Duck Hunt. The inevitable lag time, glitches and poor visibility destroys the experience. Yes, once upon a time you may have considered those old systems cutting edge. Now they just don’t have the capabilities you need.


Successful demand planning is quick, collaborative and up-to-date – not slow, siloed and full of stale data. It can’t take weeks to make critical decisions that don’t even align with reality. When changes to your demand plan happen, communication between business functions has to be immediate. Everyone needs to understand the ramifications of the change and come to a compromise-based corrective path.


The only way to do that is to have processes and technology that enable critical demand planning functionality like:


Forecasting: Stop chasing that perfect score. It doesn’t exist! You’ll never reach 100% accuracy. Instead, work to improve your forecasting by using a complete, accurate data set that includes information from across the organization – sales, marketing, finance, etc. You’ll get a more complete picture. And just as importantly, develop a supply chain that lets you plan, monitor and respond simultaneously and continuously. That way you’ll be able to spot trends, or get ahead of a problem, before your supply chain starts to feel the impact.


Segmentation: Balance complexity with efficiency and flexibility by segmenting your supply chain. You’ll be better equipped to meet shifting demand. Since segmentation doesn’t have a one-size fits all approach, weigh the demands of your customer base against corporate priorities and key performance indicators (KPIs). Then decide how to segment, basing your decision on factors like product complexity, market demands, manufacturing process or risk.


Collaboration: Make sure everyone’s playing the same game, on the same platform. Eliminate corporate silos to avoid fractured functionality and get everyone focused on the same end goal. That will help create a seamless, responsive supply chain. Don’t forget, the best demand plans are ones everyone has confidence in and include input from all stakeholders.


Technology: Go next-gen with your demand planning software. Pick a solution that gives you the ability to connect data, processes and people in a single system. The right technology for game-winning demand planning will provide end-to-end supply chain visibility, cross-functional collaboration, prescriptive analytics and some level of automation.


No matter who your customer is, or what you’re supplying to them, the growing number of potential combinations means your supply chain has to be ready to respond. When it comes to demand planning, you’ll need to be faster, smarter and more flexible if you want to outplay your competition.


Want to learn more about how you can level up your demand planning processes? Check out our latest eBook, Demand planning: 4 ways to outplay your competition.


The post Get your demand planning and forecasting game on appeared first on The 21st Century Supply Chain.


Demand planning: 4 ways to outplay your competition


Originally posted by Alexa Cheater at

by John Westerveld

supply chain planning crystal ballTwenty-five years ago, I bought my first personal computer. One of the first applications I installed was a cookbook — the killer home PC application of the time. The second application was Quicken to manage my finances. Funds were tight then and I really needed to keep tabs on my spending.


Every transaction was meticulously entered, every statement validated against my records. Then I discovered the calendar function. With the calendar, I was able to schedule my known income (paycheck) and my known expenses (car loan, mortgage, utilities, taxes, groceries, etc.) and Quicken would project my bank balance into the future.


For me, this was game changing!


When making discretionary purchases, I could look at my projection to make sure that if I made that purchase, I would have enough money in the bank, not only now, but at the end of the month when my mortgage and car loan came out. It was my crystal ball, and I regularly asked it questions like:


What if I buy that awesome new 27″ Sony Trinitron television this week, could I still make my mortgage payment? What if I save my money this month? Or don’t go out for supper on the weekend? Then could I buy it?


Today’s supply chain professionals need a crystal ball, too. The only difference is that the decisions are much more complex and far reaching than balancing my finances.


For example, if a supply chain professional accepts a new order, can they deliver it on time? If they offer a promotion, can the supply chain support it? When should they shut down the line for scheduled maintenance, and what orders are impacted if they do it now?


If we think about traditional supply chain planning systems, they’re like balancing your check book by hand, which while necessary, is time consuming and error-prone. Planning systems, however, are not designed to allow you to ask ‘what if?’ questions.


Traditional supply chain planning systems have rudimentary scenario support — at best. Worse, once you have configured a scenario, understanding the impact of a change is very difficult given the silo-based data and the challenges of reporting from these systems. This tends to be why supply chain professionals are still forced to use Excel to model so many of the decisions they need to make. This isn’t to disparage Excel because it’s a great tool for many things — managing your supply chain just isn’t one of them.


If you were to design a crystal ball for your supply chain, what would it look like?


What capabilities would be needed to help you anticipate and eliminate risk in your supply chain? How would you answer some of those what-if questions posed above?


  • What-if planning

Most traditional ERP systems (if they support scenarios at all) limit them to either a subset of data or to a single scenario, significantly reducing the effectiveness of scenario planning.


Imagine having the ability to instantly create a scenario using a complete copy of all your supply chain data. You’d also be able to make changes to the scenario, and if necessary, create additional scenarios to further explore options and solutions. You also have the ability to understand the implications of the changes you’ve made to key corporate metrics, and finally, the ability to accept and implement those changes within the business process.


With all this, you would be able to make decisions based on real data, with a true understanding of the impact those decisions will have.


  • In-memory analytics

Supply chain decisions can’t wait. When a customer wants to place an order, you can’t tell them to wait for three and half weeks while you figure out if you can deliver it when they want. Traditional systems are slow because they use disk-based IO to store and retrieve data, which is why they must run batch processes overnight.


Imagine if you can store your planning data in-memory and use highly optimized analytics. Now you can assess the impact of a demand change across your entire supply chain in mere seconds.


How would this impact your decision making process?


  • End-to-End visibility

A supply chain is a collection of interconnected nodes. The problem is that with most large companies, these nodes were either managed as discrete units or were obtained through mergers and acquisitions. This means that a given company is often a mosaic of different ERP systems and versions. Changes in demand and supply are often communicated via system to system transfers, often with nightly batch job processing required at each node. A change can take days to work its way from one end of the supply chain to the other.


Imagine if the supply chain data from all these systems could be brought into a single environment. Within this environment, the analytics from each system could be emulated so that decisions made based on this data could be reliably executed in the host environment.


  • Collaboration

A supply chain is not a single person, it’s the combined effort of many. And in most cases, supply chain decisions can’t be made unilaterally. It takes the cooperation and agreement of several people to resolve issues and make decisions. Unfortunately, traditional ERP tools do a terrible job facilitating this interaction, leaving planners to rely on e-mails, phone calls, screenshots and again, Excel.


Imagine if the system helped identify the best person to collaborate with on a specific issue, then brought you together in a supply chain centric collaboration space within the tool. Then, as discussions occur and decisions made, these decisions are tracked and the impact of these decisions are displayed in a scorecard.


Now, let’s put this all in our supply chain planning system crystal ball.


Imagine you’re planning a promotion for a family of parts and that if successful, a 20 percent increase in demand would result. You create a new scenario within the tool and modify the forecast for the time period of the promotion. You instantly get visibility into a new supply chain risk associated with that change – an alert that’s a result of the demand planning system and supply planning system now reside in the same system, use the same software and share the same data.


Using interactive visuals, you can then drill into the data and identify a set of components and a production constraint that is causing the supply chain risk. You discover the issue is the components are at one plant and the constraint is at another. You can see this because data from multiple disparate ERP systems are brought together into a single view.


Next, you create a collaboration that brings together the responsible planners who can see the scenario you created and who add scenarios of their own. By sending messages using the built in collaboration tool, a couple of solution scenarios are proposed.


By bringing all of the scenarios together, you compare them against key corporate metrics and discover that one solves the issue with a minor impact to margin, while the other solves it by stealing supply from another product line.


Based on this information, it’s determined that the margin hit is acceptable and the decision is executed.


Total time? Hours.


Total time using traditional ERP planning? Days. And you still probably didn’t know for sure if that was the right decision.


How do you make supply chain decisions today? Comment back and let us know!


The post The supply chain planning system crystal ball appeared first on The 21st Century Supply Chain.


Get started with AI in SCP


Originally posted by John Westerveld at

by Trevor Miles

Much is being written about Artificial Intelligence (AI) and Machine Learning (ML) recently. It is the topic du jour. There is undoubtedly a lot of opportunity in this space for automating highly manual and repetitive tasks, and even for redefining tasks. But there is little evidence that we have even begun to explore the opportunity to redefine whole supply chain planning processes.


To be honest, I have some doubts about the use of AI or ML to assist significantly in this space. More importantly, there is compelling reason to rewrite many processes with or without AI/ML. Most supply chain planning process definitions date back to before the advent of computers. In fact, most organizational structures, which dictate the processes, date back to military concepts of communications.Process definition


I was prompted to write this blog based on an article published in Inc on Aug 30, 2017: This Email From Elon Musk to Tesla Employees Describes What Great Communication Looks Like. I will quote from the article quite liberally because there is a lot that Musk writes that is relevant to this discussion.


“There are two schools of thought about how information should flow within companies,” he writes. “By far the most common way is chain of command, which means that you always flow communication through your manager. The problem with this approach is that, while it serves to enhance the power of the manager, it fails to serve the company.


“Instead of a problem getting solved quickly, where a person in one dept. talks to a person in another dept. and makes the right thing happen, people are forced to talk to their manager who talks to their manager who talks to the manager in the other dept. who talks to someone on his team. Then the info has to flow back the other way again. This is incredibly dumb. Any manager who allows this to happen, let alone encourages it, will soon find themselves working at another company. No kidding.


“Anyone at Tesla can and should email/talk to anyone else according to what they think is the fastest way to solve a problem for the benefit of the whole company. You can talk to your manager’s manager without his permission, you can talk directly to a VP in another dept., you can talk to me, you can talk to anyone without anyone else’s permission. Moreover, you should consider yourself obligated to do so until the right thing happens. The point here is not random chitchat, but rather ensuring that we execute ultra-fast and well. We obviously cannot compete with the big car companies in size, so we must do so with intelligence and agility.


“One final point is that managers should work hard to ensure that they are not creating silos within the company that create an us vs. them mentality or impede communication in any way. This is unfortunately a natural tendency and needs to be actively fought. How can it possibly help Tesla for depts. to erect barriers between themselves or see their success as relative within the company instead of collective? We are all in the same boat. Always view yourself as working for the good of the company and never your dept.”


At the heart of Musk’s email is a challenge to existing company structures of command and control hierarchies. And Musk articulates the business impact very succinctly: “Instead of a problem getting solved quickly, where a person in one dept. talks to a person in another dept. and makes the right thing happen, people are forced to talk to their manager who talks to their manager who talks to the manager in the other dept. who talks to someone on his team. Then the info has to flow back the other way again. This is incredibly dumb.” In other words, Musk is talking about much leaner processes. And these changes do not require AI or ML. Notice that Musk is referring to email and human-to-human communication. He does not mention mathematics at all.


My challenge to Musk would be to change the organizational structures first and the process/communication challenge will resolve itself automatically.


Why is Musk’s email relevant to supply chain planning?


In reality, Musk’s email is all about information flow, and making this as short as possible. Notice that Musk isn’t talking about material flow through a factory or supply chain. Instead, he is talking about information flow through an organization, particularly when there is a business issue that needs to be resolved across several functional groups such as a large customer placing an unexpected order, a supplier going bankrupt, or a hurricane wiping out a large part of the supply for a particular commodity group.


Each of these situations requires rapid decision making across organizational boundaries within teams. Often in cases such as a hurricane, companies will form a ‘tiger team’ to analyze and resolve the issue. But once the crisis is over, the teams revert back to the traditional hierarchy and silos. And yet, these tiger teams contain all the aspects referred to by Musk, and they were formed specifically to overcome the issues highlighted by Musk.


As a practice, supply chains have spent a lot of time analyzing the flow of materials through a factory or supply chain. One of my heroes, George Stalk of BCG, wrote a seminal piece in 1988 called “Competing Against Time”. I have referred to Stalk’s work many times in my blogs because the key concepts simply do not fade in significance, and are only made more apparent in Musk’s email. Stalk sets out some Rules of Response very clearly:


  • The .05 to 5 Rule
    Across a spectrum of businesses, the amount of time required to execute a service or to order, manufacture, and deliver a product is far less than the actual time the service or product spends in the value-delivery system.
  • The 3/3 Rule
    During the 95 to 99.95 percent of the time where a product or service is not receiving value while in the value-delivery system, the product or service is waiting. (Stalk breaks this out into 3 components of waiting, hence the 3/3.) The amount of time lost is affected very little by working harder. But working smarter has tremendous impac
  • The 1/4-2-20 Rule
    For every quartering of the time interval required to provide a service or product, the productivity of labor and of working capital can often double. These productivity gains result in as much as a 20 percent reduction in costs.
  • The 3 x 2 Rule
    Companies that cut the time consumption of their value-delivery systems turn the basis of competitive advantage to their favor. Growth rates of three times the industry average with two times the industry profit margins are exciting – and achievable – targets.

Stalk describes both the costs and benefits of operating in silos, in the manner Musk described in his email. In supply chain, we have paid tremendous attention to the flow of materials through factories or the entire supply chain. But all too seldom do we sit down to analyze the process by which we plan for the material flow. These processes are stuck in the 1950s, and are based upon the organizational hierarchies we inherited even earlier than that.


Did I mention that getting rid of the silos does not require AI or ML?


So how then does this topic relate to AI and ML?


I attended the Constellation Research Connected Enterprise conference earlier this autumn and was very fortunate to hear Tricia Wang speak. I felt as if I had come home. For one thing, Tricia talks about “thick data” and contrasts this with “big data”. Thick data is all about defining new operating models and new business models. As Tricia states “your surveys and questionnaires have been designed to optimize an existing business model”, which is a big data approach. As Tricia states, “[Companies are] so focused on getting the right data to fit their models, that they never even bothered asking the right questions.” Tricia also commented on the manner in which decisions are made in organizations, which I could not resist tweeting:




And of course, this wraps all the way back to the email from Elon Musk. To be fair, the author of the Inc article states:


“There’s only one problem with Musk’s proposed solution: It’s extremely difficult to cultivate in the real world.”


But that is only because we have been conditioned to operate in hierarchical command-and-control organizations. Let’s not forget that digitization is not the same as digital transformation. AI and ML are the former. I’m talking about the latter, which I think is a lot more interesting.


The question of course is what is meant by digital transformation. I think the terms transparency and visibility are over-used and don’t go far enough. The Sloan Management Review has a comprehensive discussion “The Nine Elements of Digital Transformation” published four years ago. Interestingly, the article breaks digital transformation out into three buckets:


  • Transforming Customer Experience
  • Transforming Operational Processes
  • Transforming Business Models

In this context, I’d like to focus on the operational processes. After all, that is at the heart of Musk’s email. But it is in the section of business models that they make the point in which I’m most interested:


Companies are not only changing how their functions work, but also redefining how functions interact and even evolving the boundaries and activities of the firm.


In the section called Worker Enablement they state that:


The tools that virtualize individual work, while implemented for cost reasons, have become powerful enablers for knowledge sharing. Salespeople and frontline employees, for example, are beginning to benefit from collaborative tools in which they can identify experts and get questions answered in real time. They are also increasingly gaining access to a single, global view of the company’s interactions with a customer.


But it is to Tricia Wang and others that I turn for pithy statements. I asked on Twitter how several analysts would distinguish between digitization and digital transformation, to which Tricia replied:


A lot of companies treat digital as if they are “doing digital” – this is “digitization” at its worst – as if it’s some checklist of things to do. It’s very transactional, and people are so busy doing digital they don’t even know WHY they are doing it in the first place!


Whereas companies that embrace “being digital” – this is “digital transformation” at its best – it’s a total paradigm shift in the culture and operations – it’s not just about buying the latest digital tool, but about creating a new system, new cadence, new mindset.


Amen to that.


What’s the role of Kinaxis?


Our purpose at Kinaxis is to revolutionize supply chain planning for all the reasons outlined by Musk and described by Stalk: because it makes sense. While undoubtedly we need all the mathematics that has formed the basis of the individual supply chain planning functions such as demand, inventory, and capacity planning, it is how the functions are linked that defines the processes, and where the opportunity lies for digital transformation. Applying yet more mathematics or AI/ML algorithms may make individual functions more efficient, which is what Tricia Wang describes as “doing digital”, but doing so will not make the overall process of supply chain planning and response management more effective. That is the prize.


We have long described the value of Concurrent Planning, which is our term for the digital transformation of supply chain planning, in our tag line of “Know Sooner. Act Faster.” One of our customers, Atul Tandon of Mylan, described the result of digital transformation best to his senior team when he said:


Two to three years after deploying RapidResponse we will no longer have demand planners, and capacity planners, and inventory planners, and material managers. We will just have network planners.


That is what digital transformation looks like, and how we achieve this is by connecting data + process + people:


  • Data: Until you have the data connected, you don’t even know that you have a problem.
    • We do this with a single data model that spans the supply chain across multiple ERPs and functions.
  • Process: Until you have the processes connected, you don’t know the scale of the problem because you cannot calculate the knock-on effect on other areas of the supply chain.
    • We do this with a single set of very fast in-memory analytics and scenarios that give a “before” and “after” picture.
  • People: Until you have the people connected, you have no-one to take action and no team to resolve the issue in “earth time”.
    • We do this by codifying self-declared responsibilities and providing an in-context adaptive collaboration capability.

SCM World Concurrency ModelSCM World, a division of Gartner has described the future of supply chain planning in a white paper titled “Concurrency”, in which they outline the core capabilities required for Concurrent Planning. In the white paper they state:


One benefit of democratizing decision-making is that the ability to simulate various scenarios can be distributed across the entire organization. … Further, the democratization of decision making will equip all areas to better understand the end-to-end financial impacts of decisions and, therefore, make more informed trade-offs.


These capabilities are available and deployed today.


Like everyone else, Kinaxis has active projects in AI and ML. These projects in isolation would be “doing digital”. However, when coupled with the concept of Concurrent Planning, we are enabling our customers to “become digital”.


The post Let’s be clear: Digitization is not the same as Digital Transformation appeared first on The 21st Century Supply Chain.


Get started with AI in SCP


Originally posted by Trevor Miles at

by Bill DuBois

Supply chain planning - data integrityEvery business plans, but not every business runs as planned. Delays, shortages, quality issues, catastrophic weather events and fluctuating commodity prices are just a few examples of the exhaustive list of worries that will throw plan into disarray. Achieving a realistic forecast and aligning supply plans is an extreme long shot at best. The best supply chains need to manage business when it’s not business as usual. That’s what sets them apart.


However, even the best supply chains struggle with a recurring issue – data integrity. The alignment of demand and supply is more difficult because most, if not all, supply chains have data integrity issues. That means even if you take away all the supply chain disruptions, your plans are off before you even get started.


Successful supply chain planning starts with data

Setting yourself up for successful planning starts with your data. What could arguably be the single biggest deterrent to undertaking a supply chain planning improvement project is, “my data is crap.” Even though it’s likely true, you’re using the current state of your data to plan, and there’s still value in that. Data integrity shouldn’t be the reason not to take on a process improvement initiative, it should be a part of any supply chain planning improvement project.


Why the data issues?

Like the supply chain disruptions listed earlier, there are just as many reasons why data accuracy is as difficult as maintaining forecast accuracy. Here are the big reasons:


  • First off, there is just a lot of data. Depending on your company, you’re likely looking at record counts in the millions or billions. In addition, these record counts are never static. With that amount of data, something is going to be off.
  • New data sources. With mergers and acquisitions, new data sources are added along with the data in these systems. Depending on the system and processes inherited with the new source, data issues could be significant.
  • Product proliferation. Product innovation means new products being added and older versions or products becoming obsolete. One data slip and you could be planning to the wrong revision of a product.
  • New supply chain relationships. Establishing new customer or supplier relationships brings with it all the data elements like order policies, cost and lead times, all of which are error prone.
  • Things change! As we all know, when it comes to supply chains, things change. Planning parameters set today may not be what’s required tomorrow.

For these reasons, it’s wise to have a plan of attack against data integrity issues.


What’s wrong with data?

The low hanging fruit of data integrity issues is simply missing data. Standard costs, bill of material records like quantity per, safety stock or order policy information are all typical records that may be left unattended for any of the reasons mentioned above. Missing a lead-time or quantity per record means your plan will be wrong no matter how good your planning processes are.


Speaking of being wrong often, even if the field is populated the number can be wrong. With fractured data systems, data elements may be different in each system and it can become a challenge to know which one is right. Yield and scrap factors will change as manufacturing processes improve after first runs of a part. You may set a Kanban policy based on current demand patterns, cycle times and lot sizes, but if any of these factors change, like demand, Kanban policies could be driving excess or shortage conditions. You may also have order status details that are not up to date, one of the most common being completed orders not closed. For these reasons, cleansing data isn’t a one-time event.


What’s the data integrity plan?

  1. Shine a light on the problem. There are companies that have found a way to interrogate their data with standard data integrity processes, views and metrics. It’s important to be able to identify and prioritize data cleansing efforts. One company had a “top 10” view into data cleansing priorities. They knew they couldn’t fix all of them, but were able to sort based on revenue impact or customer to ensure the highest priority issues were addressed first. Because you need to be able to look at all data across the entire network, one source of the truth will be invaluable.
  2. Continually monitor your data. Some data anomalies may be less obvious to spot and slowly come to a boil rather than explode on the scene. Actual lead times may trend away from planned lead times, so it’s important to get notifications when planning parameters may need to be reviewed and adjusted. In these types of situations, it would be difficult for a planner to spot the trend and will make a strong use case for some machine learning.
  3. Use scenario planning. When it’s time to set or modify data like planning parameters, it’s beneficial to be able to test the results of changes to planning data. Planning parameters impact each level of the supply network and the compounding effect on demand, supply, capacity and inventory can be staggering. Testing the impact of data changes will not only let you set more realistic performance expectations, but will also put out some fires before they start.

Data integrity challenges are not going anywhere anytime soon. Recognizing there will always be issues and that data cleansing is a continual process is the hardest part of the battle. Let us know if you’ve taken any unique approaches to solving your own data issues.


The post Data integrity: Bad data and 3 good things to do about it when supply chain planning appeared first on The 21st Century Supply Chain.


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Originally posted by Bill DuBois at

by Alexa Cheater

TrinityRail Supply chain managementSupply chain management without an operational forecast – is it possible?

Yes. Yes it is. At least if you’re one of the world’s largest providers of railcar services and products. TrinityRail, part of Trinity Industries, Inc., ditched operational forecasting in favor of a sense and respond supply chain, and the results speak for themselves.


As outlined in a recent Kinaxis case study, TrinityRail was able to realize sizable improvements in its supply chain, including nearly removing its reliance on Excel for planning and dramatically reducing the need for manual data transfers. Using supply chain management software that connected its data, processes and people into a single, harmonized system, TrinityRail reduced the risk of error, since everyone was using the same, up-to-date data set. It was even able to reduce its days of inventory on-hand (DIOH) by an average of 12 days and reduce its buyer team by more than 25% while still improving the on-time delivery (OTD) of inbound materials.


Dealing with a complex, make-to-order environment

“Rather than trying to get better at forecasting, we decided just to figure out how to live without one [a forecast],” explained Mike Hegedus, Vice President Supply Chain Management, Trinity Industries, in the case study.


That bold decision came on the heels of a supply chain transformation project that highlighted how simulation was the better goal than optimization for their unique supply chain. TrinityRail needed to have the available capacity to meet lead-time demands on products with a 30 to 50 year lifecycle and a two to three year backlog at any given time. It’s increasingly complex manufacturing environment—every freight railcar is configured to unique customer specifications—made accurately forecasting demand a near impossible feat.


TrinityRail used rapid scenario simulations, robust exception management, enhanced visibility and collaborative planning to remove operational forecasting and change the very culture of how the company works. Now TrinityRail works by focusing on action, not anticipation, and has discovered how to operate “on the edge of control” without sacrificing results.


Want to learn more on how TrinityRail ditched its operational forecast and developed a more robust sense and respond supply chain as a result? Check out the full case study, Where’s the easy button? Enabling a Sense and Respond Supply Chain.


The post TrinityRail ditches operational forecasting, gains sense and respond supply chain appeared first on The 21st Century Supply Chain.


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Originally posted by Alexa Cheater at

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