Posts Tagged ‘Enterprise resource planning (ERP)’

More than half surveyed chose RapidResponse over SAP and custom solutions

Published July 5th, 2013 by Melissa Clow 0 Comments

As we described in our first blog of the series, we recently completed a customer survey project with TechValidate and are very pleased with the over 150 survey responses and the many stories we can share. And so, for our Friday posts, we have been featuring the different customer results on how they are using RapidResponse in their supply chain and the benefits they are realizing.

In this series, we will explore the following topics:

Voice of the customer part 1: Supply Chain Flexibility

Voice of the customer part 2: Supply Chain Visibility

Voice of the customer part 3: Supply Chain Planning

Voice of the customer part 4: What-if Analysis

Voice of the customer Part 5: Response Management

Voice of the customer part 6: Alternative Technologies

Voice of the customer part 7: Competitive Advantage

If you are eager to check out all the results, simply go to our TechValidate page. If you wish to use or share any of the content we’ve published to-date, click on the asset you wish to use and then select the download button to save. You can also choose the share button to distribute through various social media channels.

We asked our customers, what alternative technology did you use or evaluate when your were looking for a supply chain management solution? And, how does it compare to competitive solutions? From our results, it is clear that investing in a patchwork approach for integrated demand and supply chain management is a gamble, at best, and the industry as a whole is questioning the merit of ERP supply chain suites. With a growing focus on sales and operations planning (S&OP) as a means to ensuring agility, alignment and adaptability, the limitations of ERP systems and their associated modules are becoming very apparent.

more than half of survey organizations chose rapidresponse over sap and customer solutions

better planning capabilities

quick supply chain visibility

displaced SAP APO

better response times

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Posted in Control tower, Demand management, Inventory management, Response Management, Sales and operations planning (S&OP), Supply chain collaboration, Supply chain management


Another Link In The Chain: Using Project Management to Drive the Supply Chain

Published June 24th, 2013 by Dan Nowicki 1 Comment

At Kinaxis, I help our customers develop solutions to supply chain and project management challenges using RapidResponse. You may remember in my last blog entitled, “Another Link in the Chain: Connecting Project Management to the Supply Chain”, I discussed how changes in material, supply, demand, and resources can significantly impact project schedules. In this blog I want to continue that conversation.

Before integrated project management (IPM) came along, direct links between the supply chain and project management didn’t exist.  Such relationships led to project management features where supply and demand can directly link to and impact project tasks.In one of our first IPM implementations a question was raised resulting in another “link” in the supply chain.  In this situation, the construction industry was our focus. The client managed most elements of the project from project management software.

The ability to directly link the supply chain and project tasks was a great step forward.  However, after adding such links, independent demand still had to be manually updated and re-linked to tasks when a related project schedule or material requirement changed.  This naturally led to brainstorming on how such linking could be simpler.

Another Link In The Chain: Using Project Management to Drive the Supply ChainThe brainstorming raised an interesting question, “If we automatically link independent demand to a project task, why not also use automation to utilize project task data to generate, modify or delete independent demand?”

Ultimately, the result of the brainstorming killed two birds with one stone through the use of RapidResponse.  Now, two previously manual tasks (entering, maintaining independent demand and linking independent demand to the project) are automated.

Project Management Currently

With RapidResponse’s integrated project management (IPM), individual project tasks can be directly linked to supply chain independent demand or orders.  When material availability doesn’t support a project task, the discrepancy is immediately flagged.  Standard configuration options allows one to automatically re-schedule tasks based on the availability of independent demand and the orders they are linked to.  In either case, RapidResponse IPM provides visibility into the supply chain’s impact on a project, in order to identify and respond to conflicts.

Another Project Management Approach

When setting up IPM for use at a site, one could also use RapidResponse as the primary source to create project specific forecasts or actual independent demand.  The opportunity exists to populate independent demand orders and forecasts based on material requirements entered into a RapidResponse project plan using RapidResponse.  The quantity and timing of these independent demands would be defined and automatically adjusted based on the project tasks they are linked to.

When

When would it be most advantageous to drive forecasted and actual customer requirements directly from the project management tool?
Such a strategy is excellent for large, long projects that take months or years to complete and have one of a kind deliverables.  Examples of such projects exist in:

  • Construction
  • Ship building
  • High-tech projects for one-of-a-kind machines
  • Design and development phases of high-tech products before shifting to repetitive manufacturing

Often, in such projects, the key is to have the material on site at the time needed (i.e. build the foundation, clear land, prepare dry dock, utilize specialized materials in design experiments etc).  Managing the materials required for such projects can be managed within an ERP system, but this is often not the optimal tool, given that it was built primarily with repetitive manufacturing in mind.  Even more important, the ERP system is not linked into a project management tool.

Advantages

Organizations can consolidate project supply planning into a single source of record.  By generating independent demand from the project plan, there is no longer any need for the project management tool to operate in parallel with (as opposed to being integrated with) the entry of forecasted and actual independent demand entered in the ERP system.  This reduces/eliminates effort to manage, maintain, and audit forecasted and actual independent demand in the ERP System vs. the project management tool.

Teams can reduce or eliminate the need to build, manage, maintain special part numbers, BoM’s and routing to drive project material needs.  Without the existence of these specialized parts in an ERP system, a significant amount of cross-department coordination and management of the Engineering and Manufacturing groups who often own part, BoM and routing data could disappear.  This could be especially important if project demands on these departments are minor (and often 2nd priority) relative to the standard production work that may be their primary priority to support.

How

Convert IPM material requirements into independent demand by building commands in RapidResponse, IPM can become the data entry and maintenance point to enter material forecasts or orders required to support a project.

Such a strategy is facilitated by creating RapidResponse command(s) to:

  • Translate IPM task material requirements into independent demand orders or forecast orders
  • Link the newly generated independent demand order’s to the appropriate tasks

An important design consideration for any company taking such an approach is to determine if project based independent demand created in RapidResponse will be translated into independent demand in the host ERP system. Depending on customer needs it may not even be necessary to use independent demand in the host ERP system.  In such a case, independent demand satisfaction can be modeled in RapidResponse based on material receipt and consumption logic.   If, on the other hand, project independent demand is created/maintained within the host ERP system AND RapidResponse, a strategy must be developed defining how it will be they be maintained to be in sync across both systems.

Organizations can use tasks in a project with material requirements as one would a production order, Routing/BoM in a traditional ERP configuration to drive quantity/timing of independent demand.

Just as re-scheduling a production order or planned order can result in shifts in the timing of the material that is needed so do shifts in the timing of a Project Plan’s tasks.  In repetitive production environments scheduling of operations needed to build product is facilitated through a production order, routing and BoM.  In a RapidResponse project based environment, these variables are analogs to a project, task, and task material requirement (for those tasks with assigned material requirements).

Repetitive Mfg. RapidResponse IPM
Production Order Project
Routing Project Tasks
BoM Lines Task Material Requirements

A production order has a routing where each operation represents certain production operations that can either have specific material requirements linked to it.  In a RapidResponse, the project is analogous to a production order.  Individual tasks, within the project, serve a similar purpose of operations in a routing, albeit with a much broader variety of activities which must occur.  In RapidResponse, material requirements are linked with specific tasks in a project plan just as material requirements from a BoM for a production order can be linked to any step in a routing (though often in many industries all material in a production order are linked and kitted in step 1 due to the relatively short life cycle of a production order).

Implementation considerations

  • Volatility in a project plan could lead to fluctuating material requirement schedules that could be difficult and frustrating to react to by purchasing and within the production MES system.  This could be mitigated by a number of strategies.  One possibility would be to selectively deciding if/when particular tasks will result in forecasted or actual demands.  The commands to create independent demand could be conditionally based on such things as the timing of the resulting independent demand or the status of the task.
  • New business processes/responsibilities may need to be defined since the trigger to buy/build material for a project shifts to data entry made directly into the project plan.
  • Others are dependent on the unique requirements of the adopter.

In conclusion, integrating project management to the supply chain can reduce the number of manual project management and supply chain steps required for maintaining and meeting project timelines and budgets.

As I mentioned in my last blog, if you are interested in reading more about this topic, we published a white paper on this subject. The paper describes an integrated approach to project management which enables an organization to model all their projects and their entire supply chain together in one environment. I recommend checking it out to understand the business case for linking project management to the supply chain and to understand the specific capabilities that should be included.

 

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Posted in Control tower, Demand management, Response Management, Sales and operations planning (S&OP), Supply chain collaboration, Supply chain management


The Effective Supply Chain Frontier – Fact or Fiction?

Published May 27th, 2013 by Trevor Miles @milesahead 1 Comment

Lora Cecere over at Supply Chain Insights has been writing for some time about Conquering the Supply Chain Effective Frontier. Lora characterizes this as a focus on long term resiliency of the supply chain, not just short term cost efficiency. She is right.

Supply Chain Frontier

Lora writes that

As shown in the Supply Chain Effective Frontier framework it needs to recognize the impact of corporate trade-offs, business investment strategies, supply chain trade-offs and the degree of complexity in business policies. These together form the system definition of the Supply Chain Effective Frontier. … While students of economics might caution that this is the efficient frontier, we have consciously chosen not to define this as the “Efficient Frontier.” Companies have traditionally defined the most efficient supply chain as the most effective supply chain with the lowest cost per unit. It is our belief that the most effective supply chain is not always the most efficient.

In other words the primary focus of the supply chain function should be the conscious trade-offs between customer service and cost-to-serve. These trade-offs can only be made horizontally, across multiple functions and even trading partners. This becomes more obvious when discussing the trade-offs needed by, for example, a pharmaceutical manufacturer when trying to determine how to respond to a tender. This requires the evaluation of long term profitability of the tender considering the costs associated with non-conformance, both immediate and long term. These need to be balanced against satisfying non-tender business, which is both a lot more uncertain and more profitable over the life span of the tender. How does accepting the offer impact capacity needs? Will the capacity be required beyond the life span of the tender? Will inventory need to be placed closer to the market?  How will planning introductions of the same into new markets be impacted?  These are not decisions that can be made in isolation by Demand Planning, or Marketing, or Sales, or Inventory Management, or Manufacturing, or Purchasing. These are decisions that have to be made horizontally, across these vertical functions, and often in conjunction with Contract Manufacturing Organizations (CMOs) and Third-Part Operators (TPOs).

The tender process is only one example of the increasing need to focus on horizontal process enablement in order to make the conscious, risk adjusted trade-offs Lora is writing about.  It is time that the ‘war room’ concept – a co-located cross-function team which is often used to deal with crisis – became standard operating practice. We have to look beyond the ‘divide and conquer’ concepts dating from the 1980s, which broke up planning into multiple function and time horizons, such as in the diagram below. Many of us may feel uncomfortable with this idea because it challenges our pre-conceived notions, how we were taught to view supply chain as a practice. Change can be difficult, even when it is good for you.

Source: http://www.partnersforexcellence.com/toptenlist.htm

Change starts by recognizing that an approach that puts horizontal process effectiveness first is far superior to an approach that only focuses on functional excellence. It starts by recognizing that harnessing and harmonizing human ingenuity and imagination across functions trumps functional optimization every time. It starts with the recognition that the ‘divide and conquer’ approach that guided the development of all legacy APS solutions is no longer enabling, but limiting progress. Yesterday’s approach fails todays dilemma.

All the APS suites were developed in this ‘divide-and-conquer’ paradigm with individual applications being developed for Demand Planning, Inventory Planning, Distribution Planning, Capacity Panning, Master Production Planning, …  To be fair, technology in the 80’s and 90’s was not able to support the data scale, speed, and analytics required to connect horizontal business processes in real-time. In fact, the only partially successful attempt to do so was ERP, but with a batch oriented computational engine that took hours, often days, to run. Technology limitations are no longer an excuse with the advent of the internet, social, and in-memory computing.

And it isn’t all the fault of the vendors. There is a great article just published in Modern Materials Handling titled “JDA Focus Challenges Attendees to Think Outside the Silos” which comments on the first conference of the joint JDA/Red Prairie operation.  According to Modern Materials Handling Tom Kozenski of JDA states that

“Optimization is a funny word,” he says. “You can optimize a WMS. You can optimize a TMS. But if you optimize a platform that sits above them, it might be one plus one equals three. We love to pitch the platform approach [such as in JDA eight], but the customer buys by silo. They think the platform is interesting, but only as long as it solves the problem in the silo. For many of them, siloed behaviors, thinking and budgets are hard to get away from.”

While I praise JDA for raising this issue of silo buying, solving it from a solution perspective will take much more than simply integrating the functionally focused applications through an integration framework. This approach will do little to address Lora’s Effective Frontier of providing horizontal conscious trade-offs, which is the fundamental reason for connecting these processes in the first place.  Connecting the data without connecting the people still promotes a siloed decision making approach focused at functional expertise. Unfortunately, in many cases the people do not want to be connected, which is the point being made by JDA about siloed behaviors, thinking, and budgets in the user community. Only strong executive leadership will drive an organization to conquer the supply chain Effective Frontier with a strong emphasis on horizontal processes supported by functional excellence.

It isn’t just the so-called East-West, or horizontal, integration that has been lacking in processes and technology, it is also the North-South integration between financial and operational plans. Executives have long experienced the side-effects that result from trusting a thin S&OP process disconnected from its deeper operational implications, namely misalignment between objectives and achievements.  Leaders are now expecting full and deep alignment between their highest level plans, and their lowest level operational constraints. Far too many companies believe that they cannot operationalize the S&OP plan, let alone the Annual Operating Plan (AOP), because it can only be developed at a corporate level in aggregate form and in chunky monthly buckets, at best at a Business Unit level. The net effect is an AOP that is, at best, a very loose statement of intent rather than an alignment between intent and feasibility.

I’d like to see all of us in supply chain management – executives, practitioners, analysts, commentators, and vendors – lift our heads to focus on the art of the possible. We have the opportunity to rewrite how supply chain management is conceived, enabled, and performed by focusing on the Effective Frontier.

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Posted in Control tower, Demand management, Inventory management, Response Management, Sales and operations planning (S&OP), Supply chain collaboration, Supply chain management, Supply chain risk management


SupplyChainBrain Video Series Part 8: Celestica’s Supply Chain Collaboration Center

Published May 16th, 2013 by Melissa Clow 0 Comments

SupplyChainBrain attended our annual Kinexions user conference.

At our event they completed a number of video interviews with some customers, analysts, and Kinaxis executives. These videos are loaded with great information and we would like to share it with our readers.
Each week, we have been sharing the clips. The final video in our series is Celestica!

Celestica’s Supply Chain Collaboration Center

Celestica’s collaborative initiative comprises three elements – inventory visibility, much closer relations with suppliers, and optimized inventory management, says Erwin Hermans, vice president of supply chain solutions at the contract manufacturer. [Run Time (Min.): 12:22]

 

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Posted in Control tower, Demand management, Sales and operations planning (S&OP), Supply chain collaboration, Supply chain management, Supply chain risk management


Webcast: Frank Scavo of Constellation Research Presents: “7 Recommendations for Gaining Positive ROI and Strategic Benefits from SCM Technology”

Published May 2nd, 2013 by Melissa Clow 0 Comments

Frank ScavoI’m excited to tell you about our upcoming webcast with Frank Scavo of Constellation Research entitled, “Seven Recommendations for Gaining Positive ROI and Strategic Benefits from SCM Technology”.

Register now for this May 7th webcast exploring investment trends and solution needs for midsize organizations.
Event Details:
Research indicates small and midsize organizations are investing in SCM technology at a slightly higher rate than are large organizations, signifying that SCM solutions are moving down market. With pervasive industry trends such as globalization and outsourcing, midsize organizations are facing similar planning challenges as larger enterprises, and thus are experiencing ever-increasing SCM and S&OP solution needs. Supply chain management systems can deliver concrete results and measurable financial benefits; however, it is important to recognize the challenges and plan accordingly.

Reserve your spot for this complimentary webcast.

Presenter
Frank Scavo, CFPIM
Vice President and Principal Analyst, Constellation Research

Frank Scavo has over 20 years of experience in IT strategy, IT management metrics, enterprise applications and business process improvement in a broad range of industries, including process and discrete manufacturing, medical devices, pharmaceuticals, consumer products, high-tech, wholesale and retail distribution and information services. He is especially skilled at aligning business and IT strategy, developing the business case for new systems and facilitating the selection of enterprise systems, such as ERP, CRM and supply chain management. He is also an expert in benchmarking IT spending and staffing levels for end-user IT organizations.

 

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Posted in Control tower, Demand management, Inventory management, Response Management, Sales and operations planning (S&OP), Supply chain collaboration, Supply chain management, Supply chain risk management


Yet Another Excel Blooper. When will we learn?

Published April 24th, 2013 by John Westerveld 2 Comments

excelIf it wasn’t so depressing, I’d laugh. Last week, we discovered that one of the leading studies that has been driving the EU Austerity policy is flawed. Why? An Error in an Excel spreadsheet.

The Eurozone Crisis really hit its stride in 2010 when several European countries (lead by Greece, Ireland and Portugal) found themselves unable to repay or refinance their government held debt. As a result, this prompted EU countries to implement austerity measures (higher taxes and lower expenses) targeted at cutting government deficits and hopefully reducing debt. The study that drove the austerity decision linked high debt to negative impact on growth. Researchers have finally been able to replicate this study and found issues. One is that the causation may be backwards; that in fact, it could be that slow growth drives higher debt. The other thing they discovered was an error that anyone who has used excel has probably made themselves; a formula that calculates the average for a range of numbers didn’t include the entire range. This error and some tweaks to the assumptions (the years included in the study) actually fundamentally changes the results.

This is one of many examples that bring home the risk inherent in Excel models. They can be incredibly complex and errors tend to hide in the underlying formulas hidden to casual inspection. Forbes calls Excel “The most Dangerous Software on The Planet” yet companies still make many decisions every day based on Excel models.

Supply Chain is no different. How many times have you pulled data from your ERP system and modeled it in Excel? Companies, even those with costly, complex ERP suites very often turn to Excel for complex decisions. Why?

  1. They want to simulate something, but don’t want to run it in their ERP system because it takes too long.
  2. They want to simulate something, but don’t want to risk making changes to their production data until they know for sure that the change is a positive one.
  3. The data is in their ERP system, but they can’t get the data visualizations they need from their ERP system without an expensive, time consuming customization project.
  4. Some data is in one system, and other data is in the other system (might even be the same software but a different instance), but both sets of data are needed to make a complete decision.

As a supply chain professional, what would you need to stop using Excel for supply chain decisions? What if you had a tool that allowed you to:

  • Create scenarios instantly. Those scenarios are private until you want to share. You can change anything you want and those changes won’t impact production until you want them to.
  • Have current data from multiple systems, yet have it all connected as if it were a single environment. One site is Oracle, another is SAP? No problem – the analytics from each system are replicated.
  • View numerous standard reports, graphs, dashboards and scorecards. The reporting model is similar to Excel (Rows and columns) so users are comfortable with navigating. Reports leverage deep supply chain and business analytics. Workbook design can be limited to a select few so business logic can be locked down.
  • Get analytic results instantly. All data and analytics reside in memory so full ERP calculations occur in seconds, not hours or (gulp) days.
  • Share results and have everyone on the team see the exact same version. No worries about people using different versions.

If you had such a tool why would you still use Excel?

Have you had any spreadsheet fiascos? If so, comment back and let us know!

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Posted in Demand management, Response Management, Sales and operations planning (S&OP), Supply chain collaboration, Supply chain management, Supply chain risk management


Truth, Lies, and Statistical Modeling in Supply Chains – Part 3

Published April 5th, 2013 by Trevor Miles @milesahead 4 Comments

As discussed in Part 1 and Part 2 of the “Truth, Lies, and Statistical Modeling in Supply Chains series, systems rarely exhibit variability that follows a Normal distribution, even though very often we base our inventory policies on the assumption that both demand and supply lead times follow a Normal distribution.

In this blog, I want to address the issue of the effect variability has on capacity needs. This is where Queuing Theory comes in handy, though let me start by admitting that Queuing Theory can only take us a little way down the road of understanding some fundamental principles and is next to useless to evaluate and manage something as complex as a supply chain. In fact, the mathematics for anything more complex than even a single server and single arrival is daunting, and even then we need to make some pretty radical assumptions. But, nevertheless, there are some really valuable ideas that come out of this analysis.

As I mentioned way back when in a blog titled “I am adamant that an accurate forecast does not reduce demand volatility”, my first degree was in Chemical Engineering, a rigorous and very mathematical course that does not admit to any randomness in the chemical processes.  As a consequence, when I took a course in graduate school in which the lecturer asked us on the first day how long the queue/line would be if she took about 1 minute to check our IDs and people arrived to the class at approximately 1 person per minute, my immediate reply was that there wouldn’t be a queue because the moment she was finished checking one ID the next person would arrive.
Statistical Modeling
Wrong. Because of those words ‘about’ and ‘approximately’.  So let’s dive in and see what this means.

To make the mathematics at least workable we need to make 2 major simplifying assumptions, namely that people arrive at class completely randomly – no friends walking to class together – and that it takes next to no time to check most IDs but occasionally it takes a long time to check an ID. In other words, the time it takes to check the IDs follows an Exponential distribution with an average service time of 1 minute. So, an Exponential distribution has a similar shape to a LogNormal distribution with a high Coefficient of Variation (CoV). In fact, the time between people arriving to class will also follow an Exponential distribution.

In the diagram we can see all the elements and characteristics of this very simple Queuing System where:

  • λ = rate of arrivals, which is 1 per minute in the example above
  • µ = service rate, or time to check IDs, which is 1 per minute in the example above

We can then derive equations to calculate all the other resultant characteristics of Wq, Lq, W, and L. This is where assuming an Exponential distribution comes into play because assuming a distribution with a more complicated probability density function makes it really difficult – some would say impossible – to derive the equations for Wq, Lq, W, and L.  In the case where both arrivals and service follow Exponential distributions these are:

  • L = λ / (µ – λ)
  • Lq = λ2 / µ(µ – λ)
  • W = 1 / (µ – λ)
  • Wq = λ / µ(µ – λ)

In the example above, µ = 1 and λ = 1 therefore (µ – λ) = 0, meaning that all the characteristics above will be infinite.  In other words, the queue will grow infinitely, as will the waiting time in the queue. This was pretty counter intuitive to me and it took me a long time, and many hours in the computer lab, to accept that this is correct.

Let’s look at the problem from a different perspective. How quickly do we need to be able to check the IDs to ensure that there are no more than 10 people in the queue? Well, we have to formulate the question slightly differently, namely that the probability that there are “k” or more people either in the queue or having their ID checked is:

P(n>k) = (λ / µ)k

So, the probability that there are fewer than “k” people in the queue is:

Pq(n<k) = 1 – (λ / µ)k+1

To be 95% sure that there are 10 or fewer people in the queue, we need an average service time of about 45.7 seconds, meaning that close to 25% of our capacity is used to buffer against service variability. The average waiting time in the queue would be about 146 seconds. To be 99% sure that there are 10 or fewer people in the queue, we need a service time of about 40 seconds, meaning close to 33% of our capacity is used as a buffer. The average waiting time in the queue, in this case, would be about 76 seconds.

In other words, an enormous amount of extra capacity is required to absorb demand and supply variability while keeping the queue size small and the queuing time low.

But this is a very simplistic case with a single queue, a single process, and a single server. Imagine now that checking IDs is a 3 step process carried out by the same person, and each step follows an Exponential distribution. In this case to get an averaging queuing time of 1 minute or less, we need an average service time of about 41 seconds; And to get a queuing time of 30 seconds or less, we need an average service time of about 34 seconds, or very close to 50% capacity utilization.

Of course, most of the time our service rates are not as variable as is modeled by an Exponential distribution, but, on the other hand, the structure of a supply chain or manufacturing process is a lot more complex than a single server system with a single queue.  And the sources of variability are a lot more varied than can be captured in a single service time because of factors such as change-overs, failures, yield, and learning. It is not difficult to see that variability will reduce the effectiveness of capacity very quickly, especially when companies are pressed to reduce working capital by reducing inventory. This shifts the investment from inventory (OpEx) to capacity (CapEx).

The same argument can be made on the demand side where there are so many factors that influence demand, many of which are not considered in a forecast based upon historical shipments. This is why I spent so much time in Part 1 proving that in many, many cases demand and supply quantities and lead times do not follow a Normal distribution. Part of the reason I emphasized the LogNormal distribution in the first blog of this series is that with a high CoV it approximates an Exponential distribution very well.

Given the debilitating effect of variability on available capacity, it is very easy to see why, whether based on Queuing Theory or through experience, so much of Lean and Six Sigma is focused on reducing variability.

If only it were so easy to predict and eliminate variability.

More blogs in this series:
Truth, Lies, and Statistical Modeling in Supply Chain – Part 1
Truth, Lies, and Statistical Modeling in Supply Chain – Part 2

 

Posted in Control tower, Demand management, Inventory management, Response Management, Sales and operations planning (S&OP), Supply chain collaboration, Supply chain management


SupplyChainBrain Video Series Part 7: Agilent’s Vertically Integrated Supply Chain

Published March 20th, 2013 by Melissa Clow 0 Comments

In October, SupplyChainBrain attended our annual Kinexions user conference.

At our event they completed a number of video interviews with some customers, analysts, and Kinaxis executives. These videos are loaded with great information and we would like to share it with our readers.
Each week for the past few weeks, we have been highlighting clips in the series. Next up, Agilent!

Agilent’s Vertically Integrated Supply Chain

Yoke Sun Lieu, head of supply chain engineering at the Electronics Measurement Business Group of Agilent Technologies, talks about the supply chain challenges of a high-mix. Low-volume business and describes Agilent’s two-level supplier collaboration model.

[Run Time (Min.): 5:50]

 

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Posted in Demand management, Response Management, Supply chain collaboration, Supply chain management, Supply chain risk management