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Closed Loop Operations Management June 3, 2008

Posted by Brian Sohmers in bpm, Business, collaboration, enterprise 2.0.
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Aberdeen Group recently released a white paper called “Technology Strategies for Closed Loop Inventory Management“. This paper explains how inventory has and will continue to be the lifeblood of supply-chains and as such, needs to be properly managed.

Inventory drives revenue and efficiency for companies by reducing capital (with few inventories in stock) and simultaneously increasing customer service levels. – Aberdeen Group.

Those companies following the principle of closed loop inventory management can:

  1. Determine safety stock targets
  2. Replenish inventory into distribution buffers
  3. View end-to-end inventory
  4. Respond quickly to market events
  5. Segment inventory based on customer service requirements

Closed Loop Operations Management

Decades ago, closed loop quality management was in vogue and most companies today have achieved this through total quality management programs. It’s certainly refreshing to see closed loop inventory management being discussed which is a must for a high-tech company to compete in today’s global market place, but the focus on just quality and inventory falls short. What sets best in class companies apart from the competition is closed loop operations management. Closed loop operations management encompasses inventory, quality, production, accounting, and other value added activities that help bring products to market.

Closed Loop Systems

Before exploring the attributes and benefits of closed loop operations management, let’s quickly review what a closed loop system is. In an open-loop system, there is no feedback. Inputs are calculated based on desired outcome only. An example of open loop management in high-tech operations is setting inventory levels, production schedules, and supply chain plans based on just sales forecasts and orders. A closed-loop system, on the other hand, is one that is controlled based on both desired outcomes and feedback from the system. Applying this principle to the former example, would mean that inventory levels, production schedules, and supply chain plans are determined not just by sales forecast and orders, but also feedback from ongoing operations. 

Having a closed loop system provides the following advantages over an open loop system:

  1. Disturbance adjustment (such as actual yields and cycle-time)
  2. Guaranteed performance even with model uncertainties (No supply-chain planning model matches the real supply-chain perfectly)
  3. Reduced sensitivity to communication errors (developing the plan is one thing, but errors can develop when communicating it, especially to trading partners)
  4. Improved reference tracking to plan


So the different between open and closed loop operations management is the use of feedback from ongoing operations. One of the problems operations folks face today is they spend much time, effort, and money on planning to optimize operations by minimizing inventory, decreasing cycle time and lead time, and improving on-time delivery, but they lack visibility and execution ability to achieve their targets. As a result, buffers (inventories, freeze periods, longer lead times) are routinely accepted to insulate plans from disruptions, leaving decisions to be local in time and function, often based on historic performance at best.

To close the loop between planning and execution companies need to establish feedback through visibility to monitor for issues and trading partner compliance to instructions,trade laws, and environmental laws, systematic root-cause analysis, and decision supportfor proactive and concerted responses. This ensures plans actually happen rather than just replanning to adjust to reality. This is an ongoing process where visibility is established to continuously monitor and enable management by exception. Once alerted to an issue, the impact is assessed, a root-cause analysis is performed to help narrow down possible corrective actions, and different “what-if” scenarios are modeled to plan a response. The closed loop operations management process also leads to continuous learning and process improvements. It’s not enough to plan on having a lean supply chain with short lead times, you need to achieve it through closed loop operations management.

Real-Time Visibility

Gaining visibility into the complete picture is essential for closed loop operations management, but this is easier said then done. There are a variety of reasons that make it difficult to achieve high quality visibility. Before we discuss the challenges, let’s take a look at how to measure the quality of visibility in a closed loop system. There are two main factors that determine quality:

  1. Resolution – how much feedback or visibility a companies has into operations including outsourced manufacturing
  2. Latency – how quickly or real-time this information is gathered and available for KPI tracking and exception management

The drive for high quality visibility with high resolution and low latency feedback is simple. If you don’t have a complete picture of your current operations or the picture you have isn’t current, the feedback in your closed loop system becomes less useful decreasing your control over operations. An example to illustrate this in high-tech operations is a sudden increase in cycle time for a particular outsourced manufacturing process. If your organization doesn’t have visibility to this or doesn’t become aware of this increase for days or weeks, your ability to make the appropriate adjustments to reduce its affect on downstream operations and ultimately to the end customers is significantly diminished. With real-time visibility, the problem is acknowledged right away enabling an immediate response, minimizing the impact downstream. It’s not enough to have visibility, you need to have complete, real-time visibility into your operations including outsourced manufacturing.

The bar for visibility into operations has been raised in today’s demanding business environment. Two main market drivers responsible for raising the bar are the increase in the speed of business and globalization. Faster product lifecycles and increased customer fulfillment demands are changing the speed at which business is conducted. This in turn, increases the cost of any latency in responding to demand or supply-chain changes. Responding to a quickly changing environment requires real-time information. Secondly, the move to globally dispersed business models, where over 50% of the information needed to efficiently run operations resides outside your four walls, makes it increasingly challenging to achieve high resolution visibility. Hi-tech companies can no longer afford to take a hit on business agility when they outsource their manufacturing. All participants in the value chain need complete visibility for faster, better decisions.

Intelligent Operations Management

To address these needs and close the loop in operations requires a new category of solutions. Intelligent Operations Management has been recognized by leading analysts as a new and unique category of enterprise software that aligns business objectives with operational systems. Using real-time information from all parts of your extended enterprise, IOM completes existing infrastructure allowing better access to information, more effective collaboration between business units, and better operational decision-making. By closing the loop with Intelligent Operations Management, companies are able to control costs, improve service levels, decrease lead times, and address pricing pressures. Those companies who follow the principles of closed loop operations management enabled through an Intelligent Operations Management solution are best equipped to compete in today’s global business environment.


Industry Recognition Received for Intelligent Operations Management February 22, 2008

Posted by Jeff in Business, collaboration, enterprise 2.0.
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We’ve reported on our conversations on the concepts, derivation, and terminology regarding Intelligent Operations Management in several earlier postings, including “Collaborative Decision Environments“, “Notes on Enterprise Software“, “Shorter Time to Volume is the New Goal“, and “Defining the Category: Intelligent Operations Management“.

In addition to the discussions with Bob Parker of Manufacturing Insights mentioned in many of the above postings, Serus has also made presentations to Gartner Group, AMR, Aberdeen, ChainLink, Ventana, and CIMData.

One of the first external validations of Serus’ concept of Intelligent Operations Management has now been written and released by Manufacturing Insights.  Their recent white paper, describes all layers and concepts of IOM, and provides examples of its use.

The term Intelligent Operations Management can be broken down its three terms:

  • “Operations” defines the scope of our solutions.  We address all parts of operations, including forecasting, planning, work in progress tracking, basically all the way from the manufacturing product specification (including ECO’s) and order placement through the fulfillment activities, WIP at your outsourced organizations, and finally interface with your financial systems for invoicing and reconciliation.  Not surprisingly, most of our sales are to the VP of Operations, though we have gained traction recently with the VP’s of Finance, and are starting to gain traction with the sales functions in our customers, these being on each end of manufacturing in the full lifecycle.
    We use this word because there are few systems that directly address the challenges within Operations.  Hence we provide unique tools that are based on real-world experience in operations, rather than trying to coble something together with a spreadsheet.
  • “Management” defines what we enable our customers to do within that scope.  Our definition of management means first solving visibility challenges so that you understand the situation, in terms of inventory levels, backlog, etc., but most importantly allowing control of the situation, by making decisions regarding actions, orders, and instructions that are fed back to the organizations in the supply chain.
    This concept of management is most clearly thought of in terms of “feedback loops” which are a combination of visibility and control with the idea that corrective action is being taken, and new insights or information is being gained each time through the loop.
    Our product has dashboards that allow you to access information at the visibility, or raw content level.  A related concept is that the content has come from multiple sources, and hence has had to be collected and cleansed, meaning resolving errors or inconsistencies, such as inconsistent naming and organization.
  • “Intelligent” defines the next level above Management, which adds the ability to define business goals and operational constraints within with the system’s operation.  A typical example of a goal is to “keep service levels above 98%”, or “Reduce stockouts to no more than 5%”, or “reduce inventory”, etc.  Adding goals into the decision making allows the system to suggest solutions or possible decisions that are biased toward the goal, again enabling the feedback loops to run more efficiently.  Another goal may be to carry out or advance a business process, as business processes act as the foundation for many feedback loops.
    Examples of operational goals within which decisions are carried out occur in often in finance, where for example a trading organization makes trading decisions but within a corporate goal of maximizing profit, and other goals that limit credit risk or cash utilization.

A classic example of a goal driven planning system is a supply chain planner, which has an engine that generates plans given a set of operational goals and thresholds.  However, many supply chain engines run overnight, and the plans that they produce are often out of date by 10 or 11AM the next morning, due to some unexpected change.  For this reason. Serus focuses on rapid tactical replanning, such as handling a supplier outage through a local replan, with a goal being to assure that this outage and plan does not affect other production activities.

Two important terms that don’t appear in the IOM term per se, are “collaboration” and “ecosystem”.

“Collaboration” defines the nature of the interactions between the different members of the supply chain.  It holds that no decision can be made in isolation, but instead have a context or state that defines them.  Typical examples are one of agreeing on an inventory stocking level by first communicating several different what-ifs or scenarios.  Or proposing different dates or product specifications between one organization and other, until agreement is reached.  Information can be changed, shared, retracted, committed, etc.

“Ecosystem” defines the set of participants within the collaboration, which can be all members from suppliers to customers, but also extends the definition of the information used in the decision process to include content from public and private sources, as well as content that is feedback, evaluations, and lessons learned within the ecosystem, in the same way that eBay or Amazon provide content about the members, books, and products that is created within the site augmenting the public information such as book descriptions.  In our case, ecosystems provide benefit when performing collaborative actions and decisions toward meeting a goal.

Content Collaboration Update February 15, 2008

Posted by Jeff in collaboration, enterprise 2.0.
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 The February 2008 issue of IEEE Computer has two important articles that follow up on the topic of using wikis and social networking software for collaboration.  Since our previous posting on Content Collaboration Software was one of the most popular postings of 2007, I’m bringing these articles to your attention.

The article “Wikis: ‘From Each According to His own Knowledge’” by Dan O’Leary of USC describes the history of wikis and typical usage today.  The first wiki was implemented in 1994 by Ward Cunningham.  Wikis offer the following advantages:

  • Structure
  • Consensus
  • Collective Wisdom
  • User Engagement
  • Accuracy
  • Delegation of Control
  • User Management

They have the following limitations:

  • Lack of authority
  • No referees
  • “Too many cooks in the kitchen”
  • Bias
  • Information insecurity
  • Scope creep
  • Decreased contributions – “slow death”
  • Legal problems with content
  • Vandalism

There are a number of potential applications of AI in the area of wikis.

The article “Social Networking” by Alfred C. Weaver and Benjamin B. Morrison of the University of Virginia describes how the mass adoption of social-networking websites points to an evolution in human social interaction.  It has discussion of MySpace, Facebook, Wikipedia and YouTube.

Online Communities and the Business Ecosystem November 26, 2007

Posted by Jeff in Business, collaboration, enterprise 2.0.
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After writing a number of postings on technology and business processes, we noticed that the readership stats for collaborative technology and collaborative business processes are now about equal.  To us, this provides a confirmation that it is time to focus on the business ecosystem that is created by using these two concepts together.  We discussed this a few times back in Spring 2007 in our posting on “Enterprise 2.0“.

What is a business ecosystem?  The following definition comes from Ray Wang of Forrester:

These ecosystems increasingly specialized and rely on the intellectual property (IP) innovation networks of Partners, Suppliers, Financiers, Inventors, Transformers and Brokers.  As software vendors and systems integrators expand into new markets, they will form solutions-centric ecosystems to enable exclusive, complementary, and “co-opetive” relationships.


Collaborative Decision Environments are an Upcoming Trend October 17, 2007

Posted by Jeff in Business, collaboration, Technology.
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Last month, we mentioned a meeting with Bob Parker of Manufacturing Insights in which a number of trends in Enterprise Software were mentioned.  Bob recently published a perspective document in which he grouped a number of important concepts under the term “Collaborative Decision Environments”.  Many of the concepts were initially featured in an interview with Bob that appeared in Supply Demand Chain Exec Magazine earlier this year on predictions for 2007.

Examples that he gave included Teradata, SAS, Business Objects, and Oracle.  All of these firms are fielding products for collaboration and decision-making.  Within such a product, one can review and analyze data, and share aspects of the conclusions.

This was the first time that we have seen the concept applied directly in manufacturing.  Many of the prior examples were drawn from emergency situation handling, such as handling responses to natural disasters.

Here are some of the important characteristics of a CDE:

  • Provide shared access to a baseline content set.
  • Provide means to propose changes to the content.
  • Allow users to navigate through a sequence of changes, and commit or retract them within scenarios.
  • Define problems to be addressed, or goals to be accomplished and have them be used to set the context of the decision.
  • Provide semantic resolution of terms within disparate data sources.  In the manufacturing operations world, this would be a harmonized view of key content such as suppliers, products, assets, customers, and employees.
  • Provide analytic functions that allow evaluation of current or proposed data in terms of the problems or goals. This capability should include the ability to perform analytics that are retrospective (what happened), perspective (what is happening), or predictive (what will happen).
  • Provide a social network or ecosystem, within with participants can rank the relevance or ranking of comments, changes, and contributors, again in terms of the goals.  This enables the network or ecosystem to determine where expertise is located, and to expand to include additional experts or knowledge.

Not covered on this list are some of the communications technologies, such as having instant message or video collaboration.  We see those as being important as well, but they stem from infrastructure technologies outside of computational decision-making process that we are focused on.

It hasn’t appeared in the material from analysts yet, but it was clear from our discussion with Bob that Serus is also providing an example of a Collaborative Decision Environment.  This is mentioned in our posting on our Decision Support Infrastructure Architecture from earlier this month.  Over the next month, we will be clarifying more of the terms defined here and improving the alignment.

Notes on Enterprise Software Architecture – Part III October 12, 2007

Posted by Jeff in bpm, Business, collaboration, enterprise 2.0, Technology.
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In our previous postings we looked at definitions, then at the structure of Enterprise Systems Architecture, and Enterprise Application Architecture.  In this posting, we go beyond the typical definitions, and look at some of the challenges that we have been addressing at Serus, and what their impact has been on our architecture.  We also discuss the definition and impact of Enterprise 2.0 technologies.

Serus is focused on the evolution of enterprise software architecture toward operations management.  This category of software deals with supporting the ongoing decision-making of schedulers, planners, manufacturing operations staff and managers, etc.  (more…)

Decision Support Information Architecture October 8, 2007

Posted by Jeff in collaboration, enterprise 2.0, Technology.
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We view the Serus core information architecture as a “federated content hub for decision support”.  It has the following layers:

Decision Support Information Architecture

This model of content and operations on content appears ideally suited to solutions that are based on integrated information. It combines several aspects of reporting and business intelligence with the business process driven concepts.

There are several important layers:

Content Management: this focuses on the raw representation of fetched or received content.

Decision Management: this deals with deriving new information, or setting aside information or changes to values as part of scenarios.

Goal-Driven Analysis: this deals with creating search routines that will consider different options, toward improving an objective function score.  The objective function might be lowest cost, might be shortest delay, etc.

Presentation: this deals with the screen-building and screen navigation that allows the user to access information at each of the different levels below, from row data to particular scenarios.

Notes on Enterprise Software Architecture – Part II October 5, 2007

Posted by Jeff in bpm, collaboration, enterprise 2.0, Technology.
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In our previous posting, we reviewed some basic definitions, and divided the problem down into Enterprise Systems Architecture and Enterprise Application Architecture.  In this post, we deal with the latter.

Enterprise Application Architecture deals with the structure inside the application.  It covers aspects such as how the application connects with data bases and other data sources, how the business logic is organized, and how the presentation logic is organized.  As is typically the case, with increased decoupling of these layers of the design, the flexibility and maintainability of the application is greatly increased.

It is within EAA that the concepts of software organization known as “Design Patterns” apply.  This refers to a set of canonically-reused structures within software that have been identified and refined.  The first literature on this concept came out in the mid-1990’s. (more…)

Serus Presented at Enterprise 2.0 Mashups Summit October 1, 2007

Posted by Jeff in collaboration, enterprise 2.0, Event Reporting, Technology.
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Last Friday’s Enterprise 2.0 Mashups Summit was a very informative and interesting event.  There were presentations by most of the major players in the mashup technology or mashup infrastructure space, as well as presentations that focused on the API’s of the major information providers such as Google.

Near the end of the day, Serus gave a presentation titled “Enterprise Mashups for Outsourced Manufacturing: mashing your shipments and processes”.  This was appropriate placement, because we were the only presenter covering a usage case study.  After that was a panel in which the speakers compared thoughts on acceptance and effectiveness of mashups and content integration.

The theme that I noticed throughout the day was the challenge of trying to clarify the difference between a true “mashup”, and a “composite application”. In general, the conclusion was that a “mashup” is a content integration constructed by an end user using some tools, while “composite applications” are applications that include content integration and mashups, along with related concepts, but may not be as end-user oriented, and in fact may be built by the IT organization. (more…)

Defining the Software Product Category: Intelligent Operations Management September 18, 2007

Posted by Jeff in Business, collaboration, Technology.
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At Serus, we are building systems and tools for operations managers, and a key element in providing a tool for operations is to understand the information management needs of the user community.  By “information management needs”, we mean: what information is required, what information is produced, what information is shared with others, and what is the lifecycle of a change to the information.

Let’s consider a typical example:  one operations manager is looking at a schedule for a production of a product with hundreds of parts in its bill of materials.  Several of those parts are arriving according to another delivery schedule.  The manager would like to change a date in the primary schedule, but this requires a change in the subordinate one.  The other schedule is “owned” by someone else in the supply chain, perhaps across an organizational boundary. (more…)