We're proud to tell you Kinaxis was named as SupplyChainBrain's 100 Great Supply Chain Partners. This is the eighth consecutive year that Kinaxis has been included in the list. Check out the complete list here.
What we love the most about this recognition is that our customers nominate us for the award. The acknowledgment validates our belief of putting customers first and being committed to their success. One of our nominations came from Agilent who recently published a case study.
The case study outlines how the company undertook a supply chain improvement program which included the creation of a vertically integrated planning process that consolidates all of its different MRP and those of its contract manufacturers, to create a virtual, vertical supply chain. With this in place, Agilent can understand the consequences of decisions up and down the multi-tier supply chain giving them greater levels of supply chain visibility and coordination.
As I was reading the article titled " Big Data: Go Big or Go Home?" by Lora Cecere ( Supply Chain Insights, LLC), I was thinking how far we have come from the days when megabytes of data storage was a luxury. Today, terabyte hard drives are commonplace on home computers. The amount of data we store has also grown exponentially as more and more cheap storage became available. As individuals, we are storing thousands of pictures, videos, songs and documents that we may never retrieve or review again. It is easy to lose sight of content and context and simply become a data hoarder — guilty as charged. Are companies any better equipped than us to not just store but benefit from large amounts of data, let alone Big Data which is mostly a nebulous concept for non-IT?
Capturing, moving and storing data has become relatively easy. And various sensors that follow interactions throughout the supply chain and wireless communications that move data rapidly give access to data that so difficult to get to previously. On the other hand, analyzing and understanding even transactional supply chain data can be a daunting task for a lot of organizations. I think there is still an opportunity for many companies to review how and why they store what they collect and be aware of the content, context and last but not least integrity of the data. After all, to paraphrase Lora, dirty data is the number one issue for supply chain teams.
When you add social data from various sources to the mix, it becomes even more important for the various parties that handle data between the source and the consumer of the data to work together and understand the common goal. Silo mentality will not work with Big Data. Big Data is coming and we should welcome Big Data as with the right tools, techniques and approaches, we can answer many more questions that we don't even think of asking today.
As we prepare for the Big Data revolution, companies need to consider putting in necessary controls and tools in place in order to ensure integrity of existing data as well as the deluge of new data that is created both by individuals who may or may not be customers as well as sensors that are making their way into all aspects of supply chains. The goal should be to extract information and answers from Big Data and not end up a hoarder of even more data.
I have spent numerous years of my life implementing change in the supply chain, changing business processes, changing toolsets, changing metrics, and changing employees' roles. I have had success and, as comes with the territory, have experienced failure. The lessons learned along the way came flooding back to me when I came across a great white paper, Supply Chain Strategy in the Boardroom – The reality: Closing the implementation gap, which came out of a joint collaboration between Solving Efeso, a consulting firm, and Cranfield University School of Management.
The whitepaper pointed out that statistics reveal a 50/50 chance of success or failure for a supply chain strategy implementation. With the odds of failure so high combined with the associated high cost of capital expenditure for these initiatives, it is no wonder there are so many supply chain leaders hesitant to drink from the 'poison chalice' by embarking on 'another futile attempt' to move the needle. Worse still, failure in some organizations is so frequent and widespread that it should be called the 'poisoned water cooler'.
So why is there so much failure? The paper points out that there are 2 main types of barriers to supply chain strategy implementation: Technical and People. More dramatically, people made up 80% of the challenge. Realizing this, only when organizations address the reasons why people resist change will they start to see traction toward successful supply chain strategy implementation. The following link, Resistance to Change, from the University of Wisconsin Milwaukee is a great summary of the resistance drivers as well as success factors which positively affect the adoption of change.
One significant success factor is that there is greater perceived benefit than the cost. In any organization, especially large ones, this can only come through organizational alignment. All parties need to be on the same page. To that point, the whitepaper identifies a high correlation between board involvement and successful supply chain strategy implementation. The reason is that it aligns the organizational objectives and doesn't allow them to be 'short-circuited' by the problem of the day or other groups within the organization. More importantly, it shows commitment in terms of resources, measurement, and rewards.
Implementing changes to supply chain strategy and the associated tools, processes, and role changes that come with it do not have to be made in vain. Organizational commitment from top to bottom, while actively engaging the people on the ground, will greatly increase the chances of a successful outcome. Once you have organizational commitment, removing the mindset of the 'poisoned chalice' is easy, and the road to success is much smoother.
Kinaxis recently had the pleasure of participating in IEG's recent Sales and Operations Planning (S&OP) conference in Seattle. WA.
Kirk Munroe, Kinaxis vice president of product, spoke on the topic of " Concept, Components and Consequences of Control Towers" and we were lucky enough to record his presentation.
In the you will hear:
How companies respond to change is where profit can be made or lost. Success depends not only on how fast manufacturers can act, but the effectiveness of the decisions they make. Hear how companies are extending their S&OP process into an enterprise control tower model to impact how they synchronize their actions across several tiers of the extended value chain to respond quickly and profitably to customer demand.
I read with great interest a recently published report from Lora Cecere ( Building Market-driven Value Networks, Supply Chain Insights LLC, 7/10/2012) that outlines where she believes the market is going. I agree with her. While many of the examples she uses come from outside high-tech/electronics, it doesn't take rocket science to translate the examples into other industries, such as the Cargill Meats example:
Cargill Beef is a market-driven leader. The Company uses price optimization tools to evaluate the market potential for beef. Before the company decides what to package for the market, they first evaluate the market potential for each cut of beef and then optimize how they harvest their inbound herds to maximize the opportunity and minimize the risk. There are 197 ways to cut up beef cattle. Since each breed of cow has a different potential or finite mix of products—steaks, ground beef, roast, etc.—Cargill uses the technology in Sales and Operations Planning to drive rancher insights to define which breeds are best for customer demand. This process of being adaptable to trade-offs from market-to-market based on the use of optimization technologies is termed demand.
This is a great example of Alignment across the supply chain. As always I am not thrilled by the term "optimization technologies", and I am sure we could convince Lora that demand prioritization and smart heuristics could provide answers that are close enough. More important is the description of how the end-to-end supply network is aligned around market drivers. If you think of the lead time from calf to cow, changing a farmer's breed mix has a significant lead time, and market needs will change in this time.
There is also a great definition of Agility , include a maturity definition (which by the way I believe is a key outcome of deploying RapidResponse, the others being Visibility and Alignment).
From the report:
I also like Lora's use of the term Sensing in the report to denote a higher order Visibility. My take is that Visibility is knowing not only where stuff is, but also the business outcomes that result. I agree that lots of people are looking for the physical visibility as a starting point. Hence the difficulty of coming up with the right term because I wanted to capture the "know-where-my-stuff-is" visibility, but not lose the "what-does-it-mean" visibility. Sensing is "what-does-it-mean" visibility, but Sensing is an action, not an outcome. Any suggestions?
Our battle cry at Kinaxis is 'Know Sooner, Act Faster'. Sensing is related to the "Know Sooner" part of the strap line. Agility is related to the "Act Faster" part of the strap line. And of course, Alignment is what you need to enable both. So I think we are on the right track.
Excellent report Lora.
I was searching our blog today for a post that Trevor Miles wrote a little while back (because we writers like to do a good 'cut and paste' job some days!) And a long list of postings were revealed to me. I realized (as I have often done before) that our man Trevor is prolific. And he's resolute in his opinions....and he has many of them!
His posts offer such a wealth of information and insight that I perused the list with a bit of regret as I considered that once posted, they are left to gather dust on the online shelf, unless or until it pops up in a Google search, or someone like me recalls a comment and goes back to find it.
In the spirit of reminiscing, and in the interest of making sure Trevor's gems of wisdom get another opportunity to shine, here's my top 12 of Trevor's quotable quotes over the last 24 months.
Unlike Adam and Eve I feel as if we have not yet eaten from the "Tree of Knowledge" and it is this that is preventing us from progressing to higher levels of process maturity. We need to come to the realization that the "paradise" of the perfect plan is unattainable, which has so often been promised over the past 30 year, since the birth of Supply Chain Management.
So let me take a crack at defining the three greatest advancements of supply chain management… I decided to make this personal—to be less analytical—which is tough for me.
I am not sure that I can define a dynamic or agile supply chain. But as sure as heck I know it when I see it…
Planning is necessary, but not sufficient.
Not too long ago companies suffered from having too little data with which to manage the company's operations. The ERP age has brought in a different problem of too much data, but too little information.
Every minute that we waste in making a decision is a minute less that we have available to actually respond to the situation
The fundamental problem is that we behave as if the plan is 100 percent correct. This is manifested in the fact that we even develop metrics such a 'Plan Conformance' or 'Plan Adherence', and measure our factories and supply chains as if these are attainable. How silly is that?!
How would we behave if our kids fell off their bikes 30 percent of the time they went riding? One natural response is to teach them to ride better and therefore fall less frequently. How would we respond if after 10 years of trying, they were still falling off 30 percent of the time they rode their bike? Would we not at some point want to teach our kids to anticipate when they are going to fall and how to fall to minimize injury? ... [In the supply chain world], aren't we duty bound to teach our supply chains not only how to ride better, but also how to anticipate that they are going to fall, how to fall gracefully to reduce the likelihood of major injury, and most importantly, how to quickly get back up and on the path?
The notion of responsibility is necessary to make the use of social media in supply chains effective rather than overwhelming.
The conclusion I came to is that the real barrier to collaboration is not technology, but trust.
Often when talking about the role of technology in S&OP I think of my father-in-law, who is a dear man, but only learned to use a computer in his 60's. To him technology is a bolt-on, not an integral part of the process. When he receives an email he prints it out and deletes the email. He has a big metal filling cabinet in his home office where he stores the emails he wants to keep. Until recently he would often write back to us on paper, often by hand but sometimes on the computer, sometimes including a printout of our original email on which he had written comments and questions. All of you who are smiling quietly in amusement at this anecdote, how many of you are running S&OP meetings using print-outs of PowerPoint or Excel?
During my time as a management consultant I learned a fundamental truth: Analyzing a situation is fairly easy, defining a future state is a lot harder, but the really hard part is defining the path to achieve the future state.
Those of you who have followed this blog over the past several years know that I have a strong interest in supply chain risk management. I've posted articles talking about everything from Chaos monkeys to Irradiated materials to pirates and zombies. Recently I've had the opportunity to put together a more comprehensive discussion on risk management in the form of a white paper titled: “ Supply Chain Risk Management Knowing the Risks — Mitigating and Responding for Success”
The abstract on the website is as follows:
The last few years have seen significant events around the world that have only heightened the awareness of how detrimental risk can be to the business. This has caused supply chain risk management to become front and center in organizations' minds. But the question is... are we looking at risk in the right way?
Supply chain risk management has a component that many companies (and many risk management experts) fail to consider; the ability to respond.
Some risk can be managed through better supply chain design; assessing where supply "choke points" are, and building in supply and logistical redundancy. Other risk, however, must be managed through better response capabilities because:
This paper takes the reader through typical mitigation planning strategies, to new risk management approaches, to the specific capabilities required to succeed. It highlights:
Regardless if you are just now realizing that supply chain risks exist or if you have a mature risk management process this white paper will make for thought provoking reading. I encourage you to download it today. Before you read the paper, think about your supply chain and where some of the risks are today. When you are done, look at those risks again. I'm hoping this paper will expose some areas of risk that you may not have thought of, and more importantly, mitigation strategies to help address this risk.
For some good insights and recomendations, download it today.
I want to bring together a number of thoughts in this blog, most importantly the idea that as the practice of supply chain management has aged, there is a more rounded approach to the education and training of people entering the practice. Lora Cecere's view is that we are entering the 3rd generation. I hope so because, by Lora's definition, I am 2nd generation and I had a far too narrow education. Perhaps this was more a reflection of my choices than what was available, but I did take English, History of Art, German, and Philosophy as electives during undergrad and grad school. But I never took Finance or Accounting. I could have taken some Biz Studies courses, but I thought these were way too applied. I wanted 'hard science'; things with differential equations and mathematical optimization. There was a Finance in Engineering course that was considered an easy option, but there were very few girls who took the course. Oh well, that wasn't the only mistake I made in the past that was based more on testosterone than an understanding of skills or knowledge I needed to acquire.
Over the years, I have come to understand and appreciate the importance of financial outcomes, but I was well into my 30's before I could read a financial statement. In truth, it was probably a few years after that before I understood what lay behind a financial statement. And being self-taught, I have no doubt that there is much I still do not understand. But I would still rate my knowledge to be well above average amongst the self-taught supply chain community that studied one form or another of engineering or computer science. And that is a pity.
But this blog isn't about education for supply chain or finance for engineers. It is about how one narrow aspect of financial analysis taught me to view supply chain decisions differently, Real Options Analysis.
As I have commented in the past ( I am adamant that an accurate forecast does not reduce demand volatility), a tipping point in my life was when I understood the effect uncertainty or randomness has on our ability to analyze a situation with any degree of precision. I have come to the conclusion that other than for high-level network design or very detailed production planning/sequencing, optimization techniques have a limited application in supply chain management. I had got to this point because:
â€¢ There is very limited capability to represent uncertainty in linear programming (LP, IP, or MILP), or there was in the early 1980's when I was studying this stuff
o Nearly all variables, such as lead time, throughput, and demand have some level of uncertainty to them, often quite a lot
â€¢ I had come to realize that these techniques force you to linearize highly non-linear systems, such as manufacturing or supply chains
o Which is adding approximation to uncertainty
â€¢ All optimization techniques force you to select some common unit-of-measure, usually money, for the objective function
o This necessitates the development of ‘factors’ to convert operational measures, such as customer service, into a currency, when this relationship is not proven, known, linear, or static
As part of my graduate research I had come to the conclusion that I had to use some modeling tool that allowed me to incorporate uncertainty and that also did not force me to linearize the system I was modeling, which made me investigate discrete-event simulation tools. From an optimization perspective I had to adopt pattern search techniques, such as Hooke-Jeeves. I was familiar with both non-linear systems and search techniques from my Chem Eng background but this was a whole new world for the Industrial Engineering department, while the IE department knew a lot more about uncertainty than I did as a Chem Eng. There was some vigorous discussion with my committee and advisor because the purists did not see search techniques as real optimization. The pivotal point was when I convinced them that if the model is not sufficiently accurate you can’t prove that you have found the optimum – max or min – anyway. They reluctantly allowed me to continue with my ‘practical’ research as opposed to a more theoretical approach, because at the heart of my research was the understanding of risk, which I thought was a novel approach at the time.
Once I managed to hook up the discrete-event model with the optimization technique it was taking over 40 hours to run an optimization of a fairly simple manufacturing cell with 5 machines, 3 operators, and 50 SKUs. Obviously this was way too long for practical use. That didn’t matter in academia, but it did matter to me, and it did matter to the manufacturing company with whom I was working. From an academic perspective, the interesting outcome was that the shape of the result was like a bath tub. In other words, there was a huge area where you couldn’t tell with 80% confidence where the true optimum lay, which meant you could select a wide range of values for the input variables without having a measurable effect on the result. Using a 95% confidence level only makes the bottom of the bath tub even wider. I then investigated the effect the degree of uncertainty/randomness of the input variables had on the shape of the objective function. Unfortunately it was more than 1:1 and it was not linear. Even if I made all but one input variable fixed – I chose to keep production rate variable – the degree of uncertainty in the objective function was greater than the degree of variability of the input variable, and as the variability increased the degree of uncertainty went up faster.
Using the analogy of a bath tub implies that there are only 2 input variables when in fact there are many. In a multi-tier supply chains there are thousands.
As an example of real-world variability, at a recent supply chain conference someone from one of the leading CPG companies said that their monthly demand Coefficient-of-Variation (CoV = std. dev./mean) for many of their high revenue items is over 1.5, largely because of the effects of seasonality coupled with promotions. He said if anyone in Operations thinks that either seasonality or promotion planning is going to decrease they are delusional. CoV should not be confused with forecast accuracy, but higher CoV usually results in lower forecast accuracy. And a CoV of 1.5 is very high. But a CoV of 3 is not unusual for NPI. So even if we have a perfectly accurate model of our supply chain, how optimal will be the supply plan? This was the problem I was trying to address in my research.
I was still forced to use an objective function, despite all the drawbacks this implies. I wish I had known then what I know now about accounting because I would have tried to use something like Return-on-Assets (ROA) as the objective function. But the issue with something like ROA is that it is affected by the cost accounting method used. (By the way, I am certain that there is at least one Finance or Accounting person out there whose toes are curling while reading my simplistic explanation.) The objective function for a fixed cost allocation method will give very different results from a variable cost method. But at least it would be consistent with the company’s financial accounting methods, and understood by Finance. Instead, most objective functions are only understandable by the team that wrote the original optimization and therefore is not changed – because it takes too long to change – as market conditions and/or company objectives changes. Actually, I question whether the objective functions developed by Operations people ever matched the company’s financial objectives.
I have come to adopt Warren Buffet's (amongst others) mantra, namely that it is better to be approximately right than precisely wrong. I believe much more in being directionally correct.
So how can we use this to make everyday decision in the supply chain? One of the most common forms of analyzing the future value of an investment is Discounted Cash Flow (DCF). Of course any decision we make in supply chain planning is an investment decision, even though our decisions are not usually viewed from this perspective. The reality is that in planning we are choosing to invest
â€¢ cash in materials we buy,
â€¢ productive capacity in the converting the materials we buy into items we sell,
â€¢ people in managing the supply chain, and
â€¢ sales and promotion funds in getting the product into customers hands.
DCF is usually used to analyze capital investments, not operational investments. All our decisions should be evaluated using DCF. But DCF has a fundamental flaw, namely that it is the analysis of the most probable outcome, and I have just spent a page explaining that the market is highly variable and uncertain. So how do we understand the most probable outcome?
I have come to see Real Options Analysis as a better form of analysis than optimization, because it incorporate the concepts of uncertainty and likelihood. Here are some links to videos on YouTube: Link 1, Link 2. But a fundamental flaw in Real Options Analysis is that it assumes that you know the probability of an outcome before the fact, for example how well a product will be adopted in the market. If we did know outcomes before the fact we would all be rich. More importantly, the first video outlines the need for continuous adjustment of a plan – whether long term, medium term, or short term — as we gain more information. In the first video, the speaker refers to this as Monte Carlo simulation, which is really just a way of automating what-if analysis. In fact, the third video points to a decision tree, which is essentially a what-if analysis of scenarios and outcomes. This is where human judgment or experience comes in. There is still — more correctly there should still be – a place for human judgment in our decision making processes precisely because of the uncertainty.
Scenario analysis, or what-if analysis, is a key capability in which people can perform an analysis of the likely outcome of a set of choices or decisions. Human judgment can be applied to evaluating the likelihood of achieving the outcome, in other words the risks associated with an outcome. But without the ability to evaluate different options quickly across multiple metrics — both financial and operational — it is difficult for a team to arrive at a conclusion of the best way forward that balances opportunity with risk.
Lastly, I just want to go back to a key point about Real Options Analysis, namely the continual reevaluation of the options as more market information is received. If we bring the concepts together they add up to Know Sooner; Act Faster.
I attended the S&OP Innovation Summit put on this week in Seattle by IEGroup. There were many fascinating presentations and great conversations.
A comment in one of the presentations reminded me of a very important fact about supply chain risk management. We often focus on “the big event” when we talk about risk. But often there is as much risk in the cumulative effect of the many small events that take place every minute of every hour of every day; A customer moves an order in. A part is scrapped. A supply order is late. These are the types of small events that companies deal with every day. If managed well, these events will have little impact on the bottom line and can even become opportunities. However, if these events are not handled well, over time, small impacts can add up to a very large impact.
To combat the death of a thousand cuts outcome, companies need to develop a culture of fast response. What is a culture of fast response? What does it mean? Put simply, it's about giving those on the front line the tools, knowledge and information to make the right decision. The front line in the supply chain are those planners, buyers, schedulers and analysts that make the many small decisions every day that impact your business. If they make the right decision, your customers are happy, money is saved and your business is profitable. If the wrong decision is made…well there is one of the thousand small cuts.
These front line decision makers need information, tools and knowledge.
To make the right decision, you need information. Note I didn’t say data; data is noise, information is that noise arranged, summarized and highlighted so that the important pieces are presented to the decision maker in a manner that can be used to drive a decision. The information needs to be complete. One of the challenges we face is that information often exists in different systems that don’t communicate with one another. Different sites, even those running the same ERP system often can’t share data. Front line decision makers need visibility to this information in a single system regardless of the source.
Front line decision makers make many decisions every day. They don't have time to scan mountains of data to find the real problems. In other words, these decision makers need to manage by exception when they are alerted to them. Alerts needs to be based not on the event, but on the impact of the event. For example, if a supply order is going to be late but it doesn't actually impact an order, it isn't worth acting on. However, if the alert is triggered because that late supply order is going to cause a customer order to be late, that is something the decision maker is going to want to action.
Once the decision maker has the information, they need tools. In the case of the front line decision maker, the tools we are talking about are the ability to understand "what-if". What-if I take that order?…..This supply is late....So then, what-if I use this other source? To be able to do effective what-if, you need three key things;
The frontline decision maker needs to have strong supply chain knowledge and skills. The best tools and the best information are useless if it is put in the wrong hands. Think of a woodworking shop with top of the line tools; with a trained woodworker, that shop can create beautiful cabinets. Put someone in there that has never used a saw and you'll get firewood. Even with the best tools and the best information, if a front line decision maker doesn't understand how supply chain works they can't make the right decision. This is about getting the right people in the job and technology can't really help with that.
So, hats off to the front line decision makers. If empowered with the tools and information they need...and if they have the knowledge, they can make the right decisions for the company and drive to a positive bottom line. If not, the company could suffer from a slow and painful death.... from a thousand cuts.
How do you empower your front line decision makers? How do they make the day to day decisions? Comment back and let us know.
Gartner held their 2012 Supply Chain Executive Conference in Palm Desert, California a few weeks ago. We were lucky enough to host our “ LATE LATE SUPPLY CHAIN SHOW” and record the panel discussion to share with our readers.
We had a special host for the program, renowned supply chain thought leaderRoddy Martin,Sr. VP Global Supply Chain at Competitive Capabilities Int. The expert panelists participating included, Shellie Molina, VP Global Supply Chain at First Solar, Robert Reinckens, Sr. Partner at Business 1st and Trevor Miles, VP Thought Leadership at Kinaxis.
During the session the panel discussed the emerging trend of supply chain management control towers. In particular, focusing on concepts, components and consequences of investing in these control towers to ensure complete visibility and orchestration across all demand and supply initiatives.
Hear first hand howFirst Solar’s Shellie Molina discusses how the visibility across their extended supply chain is crucial to the ability to effectively plan, monitor and respond to continuous changes across key processes in our supply chain.
Check out the video below.