Category Archives: Engineering Delivery

Creativity, Inc. & The Fuzzy Front End

iStock_000005724324MediumThe fuzzy early stages of any idea that offers the potential to create new value involves more art than science and is very difficult to achieve in business.  Ed Catmull’s book Creativity, Inc. sheds light on these early stages and provides incredible insight into how to lead a development organization’s fuzzy front end in the context of the lean engineering framework.

The fuzzy front end starts with the identification of an unmet customer need and ends with convergence on the optimum solution that a firm can repeatedly produce and sell profitably in new or competitive markets. Ideas that lead to market-creating innovation are of immense strategic importance in today’s competitive markets.  The fuzzy front end is messy, unpredictable, and highly uncertain. Start and end points are ambiguous. The process involves novelty, experimentation, complexity, creativity, and non-routine engineering work. Ed Catmull’s book provides a broad set of management tools and mindsets that every engineering leader needs to master to nurture ideas for new value creation to improve business performance in the fuzzy front end.

Protecting Ideas In The Fuzzy Front End

Catmull does a wonderful job describing the tension that exists in firms between what I call the delivery and innovation paradox. He uses the analogy of ‘The Hungry Beast and The Ugly Baby’ to describe how engineering leaders need to be mindful of the balance between day-to-day delivery and idea driven innovation. His point is the day-to-day delivery (The Hungry Beast) can quickly kill idea driven innovation (The Ugly Baby) because originality is fragile and the fully mature product resulting from the original idea do not just pop into the world as Catmull says ‘already striking, resonant, and meaningful’ to the market. New ideas need to be protected during the fuzzy front end to be developed and enable the convergence on the optimal solution (the best all around solution from among a variety of possible choices).

To create the right environment Catmull suggests several management actions:

  • Seek Balance (Continuously) – Management needs to give continuous attention to achieving strong counter balance in the face of the strong delivery desire for efficiency and consistency of workflow by: enabling give & take from parts of the business; not allowing one function to win at the expense of the whole company by seeing balance as the collective end objective; allow continuous healthy conflict; act on situations where balance has been lost; and ‘hold lightly to goals and firmly to intentions’ which permits adjustment as new information and learning comes to light.
  • Constructive Feedback Through An Advisory Team (Brain Trust) – New ideas don’t develop in a vacuum but rather need a constructive feedback mechanism to evolve, improve, and be tested as they develop through the fuzzy front end.  The Brain Trust at Pixar provided the constructive and iterative feedback system facilitated through candor, challenge,  independent and emotionally disengaged advice, all by people who ‘have been there and done that’ free from overpowering outside agendas. Catmull emphasized that to function effectively the Brain Trust had no authority avoiding negative influence on development team dynamics.
  • Trusting Culture – Management needs to continuously facilitate a culture that enables honesty and candor, accepts failure with no retribution, sees change as good, and pushes employees mindset beyond their comfort zone. Management also has to recognize that they can’t possibly have all the solutions to unforeseen problems trusting all employees to respond with solutions because they are closer to the problem and have the best information.

Engineering leaders faced with the need to continuously innovate in response to competitive pressures should read Creativity, Inc. to understand how they can manage the fuzzy front end. The book is rich with examples, methods, and advice. As Catmull observes ‘discovery means you don’t know the answer when you start’ which capture perfectly the essence of the fuzzy front end.







Engineering Productivity

Engineering leaders interested in improving their profitability need to understand how the Theory of Constraints can improve engineering productivity and perhaps most importantly under what business conditions. This post reviews the evolution of throughput methodologies from where they were first applied in the manufacturing environment to newer approaches evolving for engineering, new product development, and R&D where the business goals are improved profitability and new value creation for growth and competitiveness.

Theory of Constraints

Eli Goldratt’s book The Goal (1984) helped a generation of manufacturers understand the operational principles underlying the Toyota Production System and Lean Manufacturing.  Goldratt defined the goal as improved profits and clarified the operational rules for running a plant to be in order of priority throughput, inventory, and operational expense as opposed to pure cost cutting that lead to localized optimums and poor profitability results. He explained how the Theory of Constraints (TOCs) when applied through these operational rules can improve the profitability of a manufacturing operation with stable input demand. The TOC was first applied to manufacturing operations that can be characterized as a repeatable network of dependent events with processes that are subject to statistical fluctuations.  The TOC focusses on system constraints to improve throughput, inventory, and operational expenses in the total production system.  The key conditions that enable the TOC to achieve results in manufacturing are stable demand, moderate to high volumerepeatable processes, and a small range of products.

Unstable Production Environments

Eli Goldratt’s paper Standing on the Shoulders of Giants (published with The Goal) went on to clarify how certain production environments and conditions can become unstable leading to marginal improvement gains from applying TOC.   In this paper Goldratt described the Hitachi Tool Engineering case where the firm had limited success with lean manufacturing because of their unstable production environment conditions.

The three general conditions Goldratt identified that lead to unstable production environments are:

  1. Unstable Demand Per Product
  2. Unstable Overall Load On The Entire Production System
  3. Short Product Life

The first two unstable production environments fall within the means of a manufacturing company to manage because the production system can still be characterized as a network of dependent events with processes that are subject to statistical fluctuations.  Full productivity gains are not achieved because of how the production system throughput reacts to the unstable input demand due to dynamic mix of products, too many different products, or how dynamically the input demand of different types of products results in unstable overall load on the system. Goldratt explains how a time-based application of supply chain approach of TOC in a method called Drum-Buffer-Rope system can achieve improved performance for the first two conditions. Goldratt observed that low touch time production environments (Touch Time <<< Lead Time) provide enough margin to still exploit TOC benefits.

The third unstable production environment, short product life, emerged in the 1980s from the increased pace of technological change on manufacturing operations. The turn time (lead time) performance of engineering, product development, and R&D became a factor for product companies bringing attention to knowledge worker productivity.  Goldratt observed that product development systems do not exhibit processes that are ‘network of dependent events with processes that are subject to statistical fluctuations’. Each new product develop effort tend to have a unique network of dependent events with high variability which is consistent with a a project environment. Goldratt also observed that the project environments also exhibits time compression where touch time approaches lead time (lead time ~ 2 to 3 times touch time) of the project which degrades project environment throughput.

Unstable Project Environments

To solve the unstable project environment problem Eli Goldratt went on to develop the Critical Chain method in the 1990s.  The Critical Chain method adapts the TOC to unstable project environments with a particular emphasis on engineering development projects. In much the same format as The Goal his book Critical Chain (1997) explains how the Critical Chain method achieves improved project performance over Critical Path methods. The goal of Critical Chain method is to improve the flow (throughput) in project environments for stable and unstable project demand.  The mental jump from manufacturing production environments to project environments is helped when one considers that most project environments are multi-project environments.  Throughput in a project environment is understood to be the flow of projects (and their activities) of various degree of: sizes, durations, complexity, uncertainty and novelty.

The Critical Chain method seeks to maximize project environment throughput by managing feeding buffers and capacity buffers within the project and drum buffers and capacity buffers between projects.  The Critical Chain methods use of buffers (time & resource) to improve productivity by reducing Work In Process (Design in Process), manage bottleneck resources, not allowing multi-tasking of resources, staggering projects along the constraints, prioritizing projects, and resolve resource conflicts on the system level.

An interesting aspect of Goldratt’s Critical Chain method was how to consider behavioural issues in multi-project engineering environments. The Critical Chain method addresses:

  1. Tendency for engineers to ‘pad their estimates’ to give local safety margins that degrade the efficiency of the project environment by use lumped buffers (rather than activity-by-activity risk buffers) and focussing less on individual activity time performance.
  2. Overcome the tendency to think locally (within the project or a work area) by encouraging global thinking by avoiding multitasking.
  3. Manage ‘student syndrome’, the tendency for humans with time buffers to start their tasks later and waste safety margins.
  4. Manage ‘Parkinson’s law’, the tendency not to finish tasks ahead of time even they have a chance to by removing activity padding.
  5. Minimize the individual project owners pressure to execute first (local optimization at the expense of the global performance) by adopting a priority system.

An excellent review of the Critical Chain method can be found in a 2005 paper by Lechler, Ronan, and Stohr with some useful simplifications that make the method more practical.

Product Development Flow

Donald Reinertsen developed a parallel set of work to Goldratt that explored and clarified much of the underlying principles of lean product development from the perspective of achieving faster time-to-market in the project production environment. Reinertsen’s books Developing Products in Half The Time (1991) co-authored with Preston Smith, Managing the Design Factory (1997), and The Principles of Product Development Flow (2009) explored an economic model for design, queues in product development work, management systems, managing risk, lean engineering principles, and performance metrics more appropriate for the paradigm shift from the traditional utilization based management paradigm to a throughput management paradigm for engineering, product development, and R&D.

Reinertsen also defines Design in Process (DIP) in the project production environment since inventory is measured in terms of information in the knowledge work space. The abstract nature of information inventory and visualizing how it flows through a knowledge based work environment has probably been the single largest factor holding back the broader adoption of lean product development.

Reinertsen clarifies how the project production environment differs from the manufacturing production environment with repeatable network of dependent events with processes that are subject to statistical fluctuations to one with high variability (uncertainty, learning, experimentation), non-repetitive (every project network is different, sometimes completely), and non-homogeneous task durations (most tasks slightly different each time). Reinertsen’s most recent book Principles of Product Development Flow in particular explores the themes of cadence, synchronization, flow control, WIP constraints, batch size, exploiting variability, queue size, fast feedback, and decentralized control to maximized throughput.  Although these works provide a vast array of tools it is difficult to see the big picture framework suitable for practical implementation.

Lean Product Development

Ronald Mascitelli, Timothy Schipper and Mark Swets went onto develop fully integrated lean product development frameworks that operationalized the principles for engineering leaders who are responsible for new product development.  Most importantly they describe how to fully implement a multi-project production environment based on the all the preceding methods but appropriate for actual business environment.

Ronald Mascitelli’s Mastering Lean Product Development (2011) is perhaps the best integrated framework for the engineering, product development, and R&D leader to establish a throughput managed multi-project production environment. Mascitelli’s framework is an event-driven process incorporating practical lean methods to achieve the goals of improved profitability and new value creation for growth and competitiveness.

Timothy Schipper and Mark Swets published Innovative Lean Development (2010) to describe an equally powerful integrated framework that leverages fast learning cycles and rapid prototyping for project production environments with high uncertainty.

Agile Scrum

In the digital information age as products have become software driven and in many cases entirely software based the agile scrum methodologies have operationalized software product development emerging in early 2000s. The abstract nature of software development defied reliable engineering management methodologies before the emergence of agile scrum. With agile scrum software productivity is more manageable, efficient, and effective. Software driven products require the integration of the agile scrum methodologies within the project production environment framework just described.

The Lean Start-Up

Up to this point in the post we have looked at how established companies with existing demand can exploit the TOC for improving throughput, inventory, and operational expenses to improve profitability in knowledge work. Finally Eric Ries operationalized new-to-the world lean product development (particularly digital offerings) for start-up founders in his book The Lean Start-Up (2011). This is the extreme unstable demand case.  Ries describes how to measure productivity as validated learning for fast iteration and customer insight to find the scalable business model before cash runs out.  Application of lean principles such as small batch size in the form of minimum viable product, build-measure-learn loop for fast feedback, metrics, and adaptability to find product/market fit. Ries observes that The Lean Start-Up is also applicable within existing companies for use by intrapreneurs who may be creating new value with new-to-the-world products because this is also the extreme unstable demand case.

Productivity Methodology Selection Based On Business Environment

Selecting the right methodology to drive business productivity requires leaders to understand their business environment and the stability of their demand environment. The diagram below helps to characterize application & business environments.

Engineering Productivity TOC

The diagram illustrates that in both manufacturing and engineering that the nature of the work can fall into a range of demand conditions.

A key lesson from this review is that leaders should seek to throttle/smoothen (WIP Constrain) the input demand conditions if productivity improvements results are to be achieved.  All the available methodologies are based on the concept of flow and maximizing throughput and managing inventory (physical or information), and operational expense to achieve business the goals of improved profitability and new value creation for growth and competitiveness. As Goldratt emphasized time and again effectiveness of these methods depend the key underlying condition of stable input demand or constraining the process input demand to ensure stable flow. As demand conditions become unstable lean engineering methods have been developed by Goldratt, Reinertsen, Mascitelli, Schipper, and Swets. Ries has described how new cash flow streams can be created in a lean fashion in the extreme case where demand does not yet exist.

Finally a common theme throughout these works is the fact that cost accounting methods and data tools are ill suited to measure throughput, inventory, and operational expenses to achieve business the goals of improved profitability and new value creation for growth and competitiveness. Goldratt explores this issue at length in The Goal why a blind focus on cost reduction leads to bad performance.  This problem has continued as throughput and TOC methods have evolved in the information age as pointed out by Reinertsen of the invisibility of DIP because of how R&D expenses are recognized at the time the money is spent. Information inventory and intangible assets remain as a problem for cost accounting and business performance management. This will be a topic of future posts.

Managing Engineering WIP

Engineering work in process (WIP) is notoriously difficult to manage because unlike manufacturing WIP you can’t see it. Engineering WIP is information in various forms along the engineering value stream. Value is added to each ‘engineering work item’ at each successive stage up to customer delivery (either internal or external customers). The key goals are managing effectiveness and efficiency of engineering work. Typically management sees input requests and outputs that are often late and overrun but have trouble seeing the causes in-process. Management could take corrective action if they could see engineering in-process WIP more clearly.

Visualizing Engineering WIP

To visualize engineering WIP firms need to establish a simple system to identify ‘in-process engineering work items’ appropriate for each stage of their engineering value stream. These work items act as a proxies for physical items that would be visible in a manufacturing process for instance. Engineering work often varies in size, complexity, and novelty so a simple (ie. small-medium-big, low-medium-high, proven-modified-novel) rating system should also be used to differentiate the scope & nature of each work item without getting too confusing.

A visual WIP flow board should then be established to see where each work item rests on the engineering value stream as it flows along the engineering value stream as illustrated below:

Engineering WIP Flow Visualization

Post-it notes or total work item counts by size, complexity, or novelty identifiers are marked on a white board and updated weekly or at the operating cadence selected for the firm. Managers should distinguish between engineering work items that are ‘active‘ or ‘waiting in queue’. This distinction is critical to see queues, evaluate throughput, and identify capacity bottlenecks in the engineering value stream. The visual WIP flow board allows managers to see engineering WIP in-process and understand the dynamics of their resource allocation performance.

Queues in Engineering Work Flow

A key advantage of the visual WIP flow board is the ability to see where queues form in engineering as illustrated below:

Engineering WIP Constraints

All engineering managers have experienced the effects of queues but often can’t pin-point them with sufficient visibility in any given week to take effective action. Queues begin to form in any stage as capacity utilization increases beyond about 75-80% based on queuing theory (see Reinertsen for an in-depth explanation of why this is so). For example the production support stage in this example. Queues introduce congestion in the engineering work flow that causes delays that can lead to schedule overruns and idle engineers waiting for data in downstream stages.  Queues in earlier stages are more dangerous for engineering delivery performance.

The queue size – capacity utilization curve also helps to illustrate why maximizing capacity utilization in engineering is bad for schedule performance because in-process queue formation will choke flow.  Most firms today are multi-project organized by matrix so choked flow and congestion can have serious negative consequences from complex inter-project connections. The visual WIP flow board helps management to make the paradigm shift from the traditional ‘maximize utilization‘ paradigm to ‘maximize throughput’ paradigm of lean engineering. Probably the single biggest benefit of the visual WIP flow board is being able to see queues forming before they reach choke flow in order to take timely action. This is also why the visual WIP flow board should be updated weekly at a minimum to capture the dynamics of work flow in engineering.

Engineering WIP Capacity Constraints

To maintain streamlined flow , maximize engineering work efficiency, and avoid congestion/choked flow the amount of WIP in each stage of the engineering value stream should be actively managed. This is accomplished by applying WIP capacity constraints at each stage of the engineering value stream. For example the capacity constraints are defined in the shaded middle row of the visual WIP flow board for each stage of the engineering value stream.

WIP capacity constraints enable engineering managers to control the throughput in engineering. The visual WIP flow board helps to visualize the flow on a day-to-day basis enabling more effective control actions.  When combined with small batch sizes WIP capacity constraints are powerful management control methods. Engineering managers can adjust and set the optimum WIP capacity constraints as they gain experience with the visual tool in practice.

Engineering Throughput Management Levers

How can managers use the visual WIP flow board in practice to take corrective-action? Several management control options or responsive levers are now possible with visual WIP flow board:

  • Work Acceptance Discipline – This is the most critical lever because WIP constraints at the initial stages can ‘throttle‘ the work flow in down stream processes. If too much work is accepted in a short period of time the work the visual WIP flow board illustrates how streamlined flow would become rapidly choked and slow progress along the value stream with waves of underutilization at later stages. By applying ‘go/hold discipline’ management can set engineering up for success along the engineering value stream.
  • Resource Allocation – The visual WIP flow board also helps to see how engineering resources should be allocated to deliver value at each stage. The visual tool inherently allows multi-projects but rather than giving a project view gives a resource view to clearly see capacity constraints for faster resolutions.
  • Resource Organization – The visual WIP flow board provides insight into how engineering resources should be organized to maintain streamlined flow.  The value stream model provides an alternate view of the functional-project ownership problem that many organizations get caught in. Firms may have several business lines that possess separate value streams so managers can identify bottlenecks and allocate resource capacity more effectively to each value stream rather than function or too many projects.
  • Allocating Spare Capacity – Engineering resources that are underutilized will be immediately clear from the visual WIP flow board enabling engineering managers to reallocate their time for short periods to work down queues in other stages. Developing flexible resources is key. Some specialist engineers may be more difficult to move but the visual board helps staff to see how their flexibility will help contribute to the firm business performance but also make their work day less stressful.
  • Targeting Outsourcing / Subcontractors – Stages with perpetual queues are opportunities to target outsourcing or subcontractor efforts. The added overhead and lead time for outsourcing is not appropriate for short term queues and will not be responsive enough to maintain streamlined flow.
  • Engineering Process Improvements – Stages with perpetual queues are also ideal opportunities to target engineering process improvements.
  • Adjusting To Seasonal / Cyclical Variation – By tracking and recording engineering throughput data collected with the visual WIP flow board on a weekly tempo over an annual period management can build better insight into seasonal and cyclical variation. This insight can be useful to resource capacity actions, work acceptance decisions, and outsourcing decisions. All firms experience some form of seasonal work volume effects so the visual WIP flow board can be used to manage short term surges at peak work volumes or scheduling long term development activities during slow periods.
  • Efficient Buffers For Variance & Uncertainty – High task duration variance caused by uncertainty during the ‘fuzzy front end’ of the engineering value stream is key operational difference from manufacturing value streams. Although active risk management and risk reduction methods can be applied to reduce the variation eliminating it outright is not possible nor desirable because it inhibits innovation a key source of value creation. The visual WIP flow board enables engineering management to intentionally build-in capacity buffers to increase throughput.
  • Business Line Value Streams – As firm’s grow and support multiple business lines separate visual WIP flow boards can be established to support resource allocation across the multiple business lines to enable prioritization, de-confliction, and possibly even exploiting differing seasonal cycles for efficiency.

Combined together the visual WIP flow board and active WIP management can dramatically improve engineering efficiency and effectiveness. The improved control realized by such a method can dramatically improve project on-time and on-budget outcomes.

Strategically the visual WIP flow board can also help management to make wise resource investments, partnering decisions, and strategic pivots to fuel growth. Experience with the visual WIP flow board provides management a better feel of how to size and allocate new engineering resources if the firm is growing rapidly. The mix of experience and inexperience can be targeted to stages that present the biggest bottlenecks to growth. Alopex Management Consulting can assist you to implement more effective engineering WIP management for improved business outcomes.

6 Hidden Demands On Engineer’s Time

Firms never have enough engineering resources to meet business demands yet projects often overrun in cost and schedule.  Today’s engineering work environment is a complex and chaotic mix of market change, multiple projects, and development risk/uncertainty.

Development projects, the bread and butter of most engineering organizations, require robust project and risk management protocols to achieve cost, schedule, and quality goals. Engineering resources are often pulled in different directions with discretionary requests for their time contrary to what business leaders set as priorities and their strategic intent. The gap between reality and perception can be large leading to misunderstanding, misdirected blame, reduced morale, and degraded business performance.

To understand engineering delivery throughput it is essential that business leaders understand the total demand that the firm is placing on their engineers beyond project commitments caused by six hidden demands on engineer’s time.

Total Engineering Workload

A non-supervisory engineer’s normal available work time for direct engineering project work is about 82-86% of total possible work hours or approximately 47 weeks when holidays, vacation, and normal business demanded time (performance reviews, town halls, functional group meetings, training) are taken into account. There are slight differences for country, role, company situation, and their level (determining vacation) but this baseline range is fairly consistent world wide. Overtime and weekends are normally held in reserve for short term capacity surges to address unforeseen issues and is over and above this total possible work time.

The reality for most engineering organizations is a total engineering workload for a non-supervisory engineer is comprised of eight types of activities:

  • Multi-Project Direct Work Tasks & Activities;
  • Development Project Risk / Uncertainty Mitigation & Treatment;
  • Discretionary Business Activities;
  • Multi-Project Inefficiencies;
  • Un-Forecast Back-Door / Walk-In Activities;
  • Process Inefficiencies (Business & Engineering);
  • Rework; and
  • Daily Distractions.

Project schedules are often based on the first two types of activities or the work that one would expect would make-up the majority an engineer’s productive time. This approach can lead to under resourced engineering organizations. The problem is that the six hidden demands can create 15-25% additional work. Only 60-70% of engineering capacity may be going to project work as a result causing schedule and cost overruns as well as excessive overtime to keep up. This work volume disconnect drives the engineering capacity perception / reality gap. To address the gap business leaders and engineering management/staff need to determine the extent to which the six hidden demands are impacting their engineering capacity plan.

Discretionary Business Activities

Discretionary business activities are undertaken for good reasons because they lead to growth or improvement in business performance. Engineers often must support business sustaining activities because of their product/technical knowledge, experience, customer knowledge, and academic credentials.  Bids and proposal support alone can on average demand 3-5% engineering productive capacity. Other discretionary business activities aimed at improving business performance may include enhanced training, continuous improvement, or reorganizations which can also consume several percent engineering productive capacity.

The firm’s industry environment, market cycle, and the degree of market force driven change can also have a significant short or medium term impact on the volume of this discretionary engineering workload. Assumptions about discretionary business activity volumes based on years with little change leads to significant engineering capacity demand under estimation.

Capacity availability problems also often arise from discretionary business activities if they are not timed or integrated well with direct project schedule commitments. Discretionary business activities should be synchronized during project demand valleys avoiding seasonal project demand peaks if present in annual work cycle.

Multi-Project Inefficiencies

Most businesses today are multi-project environments because the extremes of dedicated functional or dedicated project teams are inefficient or impossible to implement with scarce specialist expertise. The number, size mix, complexity mix, and novelty mix all combine dynamically to create a constantly shifting work plan with winning / losing project managers. Multi-project inefficiencies are always bad.

Priority conflicts are a leading source of project schedule delays in multi-project environments. Organizations that operate lean or with labour shortages are particularly susceptible to priority conflict. Furthermore a misalignment between project priority and project manager assertiveness can often put individual engineers into difficult situations that impacts morale and job satisfaction that if left unchecked can degrade the operational effectiveness of the engineering organization and impact overall business performance.

Inefficiencies can also be caused simply by the sheer number of individual projects.  The more projects that individual engineers support the more project related overhead activities they are required to keep up with. Project related overhead activities include stand-up or status meetings, project email information, email responses from them, schedule updates/EACs/ETCs, and customer meetings. This activity can require several hours of work per week to sustain multiplied by the number of projects and depending on the stage of the project can be quite intense. This workload is often budgeted under project management as opposed to engineering so is drain on engineering capacity even though project budgets are fine and the net result is a schedule overrun.

To solve this issue business leadership need to implement engineering WIP constraints by limiting the number and mix of projects in process and establish a company wide priority system. Leadership must resist the temptation to load engineering staff up to 82-86% which causes schedule overruns, cost overruns, and near constant overtime leading to fatigue and increased error rate.

Un-Forecast Back Door / Walk-In Requests

Often business leaders and even engineering managers are unaware of many requests coming through the back door to their staff. Individual engineers want to do their best, satisfy external/internal customers but are left confused and unclear of priorities.   Back door and walk-in requests can lead to project schedule delays. Typical examples of back door  and walk-in requests for engineering advice include engineering support to supply chain, engineering support to production, and customer support requests. Another type of back door and walk-in requests on engineers are management questions or management emergencies. Although good intentioned engineers are compelled to respond or they fear being labelled as non-team players, non-responsive, or difficult employees.

Leadership should test whether these back door and walk-in requests are essential for the business and if so should budget to meet this demand. A separate support group could be established if the volume is large enough to separate support work from project work potentially rotating junior staff through the group for experience. Often engineering support groups are completely overhead but when times are difficult the groups are down-sized and the work load moves back to the project support engineers leading to a re-emergence of schedule and cost overruns.

Process Inefficiencies

Waste and inefficient processes add to total engineering workload. Process inefficiencies can relate to either business or engineering process waste. Focusing on engineering process inefficiencies, forms of engineering waste include:

  • Over production of information;
  • Over processing of information (over design);
  • Miscommunication of information;
  • Stockpiling of information;
  • Generating defective information;
  • Waiting for information; and
  • Unnecessary movement in working to the process.

An excellent discussion on engineering waste can be found in Waste in Lean Product Development by Josef Oehmen and Eric Rebentisch. Lean engineering methods provide solutions to eliminating waste in engineering processes.  Simply redesigning business or engineering processes can free up enough engineering capacity to mitigate the other hidden workload – sometimes in the 10-15% range. Leadership should ensure that a volume of engineering capacity is budgeted for continuous improvement on an annual basis.

Engineering Rework

Engineering rework can also be a large drain on engineering resource capacity sometimes exceeding 5%. Engineering rework is difficult to see in the same way that engineering work in process is difficult to see because engineering work involves information rather than physical parts. Engineering rework is any effort spent in correcting information resulting from engineering mistakes, errors, or change required to manufacture, build, or support a product. Often though the actual presence or need for engineering rework can be subjective where right or wrong are not clear cut but accountability for solving engineering rework rests with engineering staff and engineering management. The degree of product novelty and complexity can drive higher rates of rework. Common defect categories include:

  • Technical accuracy;
  • Technical argument structure & soundness;
  • Engineering approach;
  • Deliverable completeness;
  • Regulatory compliance;
  • Deliverable format, typos, or grammatical errors (least important)

Rework is a form of waste and must be eliminated. Organizations should establish engineering error tracking systems as part of an engineering quality management system to determine trends, volumes, and support root-cause analysis continuous improvement activities to reduce rework. Rework should be measured and addressed in conjunction with engineering process improvement.

Daily Distractions

Finally, the daily distractions that impact everyone in a work environment go largely unnoticed in most firms also drain valuable engineering resource capacity. Daily distractions include: phone calls; emails; office noise; excessive meetings; and social walk-in interruptions.  Even just 15min/day of distractions can add up to 60 hours per person. A good method to address daily distractions is to have quiet times, often in the morning when staff are most productive, where emailing and phone calls are not permitted.

Annual Engineering Capacity Forecasting

In today’s complex work environment business leaders and engineering managers need to  understand the total engineering workload, how it behaves dynamically, and how it can degrade the operating efficiency of their finite and constrained engineering capacity to correctly forecast the needed capacity.  The six hidden demands on engineer’s time can have a significant impact on engineering delivery throughput.

Problem areas are opportunities to free up capacity to use on more productive work and reduce project schedule and cost overruns. The annual business operating plan is the time to take a deep look at total engineering workload, underlying work volume assumptions, and plan improvement work to prepare a realistic engineering workload forecast. Periodic time studies may be worthwhile to uncover the degree to which the six hidden demands on engineer’s time are causing project schedule and cost overruns.

Solving Engineering Capacity Problems

Firms never have enough engineering capacity to meet their needs. Down-sizing, hiring freezes, narrow specialist expertise, labour shortages, and increasingly demographics are significant constraints on engineering capacity. Engineering work is also constrained to highly qualified staff with unique knowledge, experience, and competencies.   Yet the demands on engineering are continually growing, changing, and becoming increasingly complex.  Capacity shortfalls impact customer satisfaction, company reputation, and goal accomplishment potentially leading to negative fall-out and financial consequences.

If they can’t recruit, managers typically respond to capacity problems with three solutions: working overtime, prioritizing, and outsourcing. These solutions have their limitations.  Lean engineering is a fourth solution that managers can use to expand their capacity by providing a systematic approach to make more efficient use of their existing talent.  Engineering specialists can benefit from lean engineering because it allows them to do more of what they want to do – engineering – which in the long run fuels their career development. Significant results on the order of 5-10% improvements in efficiency can be achieved in the first year of applying lean engineering which for a 100 person engineering organization means freeing up 5-10 scarce resources to meet business needs. Committing to a lean engineering program in the long run can deliver an important competitive advantage to the business and support increased growth but it will take some effort.

Limitations of Overtime, Prioritization, & Outsourcing

What are the limitations of overtime, prioritization, and outsourcing?

Overtime enables short-term capacity surges but can hurt productivity in the medium term from the effects of fatigue and reduced morale. Unfortunately many firms have come to rely on almost continual overtime since the financial crisis.  Overtime can hit overheads hard if not chargeable. If it is chargeable excessive billing can signal inefficiency directly to customers leading to cost overruns that impact the reputation of the firm. Overtime should be held in reserve to mitigate the risk of sudden surprises due to the uncertainty inherent in most engineering and new product development work.

Prioritization allows high priority committed work to be completed but delaying lower priority committed work hurts customer satisfaction that can result in lost revenue in the longer term when customers decide to go elsewhere. Depending on where high, medium, or low priority lines are drawn delaying too much work volume can have a significant impact on the business. Priority decisions should be applied where slack exists between multiple projects off the critical path on any project.

Outsourcing can be effective but can be difficult to manage. Engineering service supplier work needs to be tightly aligned with internal engineering processes, structured to meet project needs, requires extra management overhead, and can be very costly if requirements change often or are not correctly defined up-front.  Outsourcing can lead to hollowing out of a firms core competencies and can create future competitors.  Outsourcing can be effective in the long run but requires extensive investment in the relationship and setting up an effective work allocation/performance management system. Outsourcing engineering work is usually most effective for algorithmic tasks that can be more easily defined, packaged, and monitored with clear end deliverables.

These three solutions help solve capacity problems but are often not enough and can be overused so managers need to look at finding efficiencies.  Unfortunately the process of finding efficiencies often involves an ad hoc approach with mixed, un-repeatable, or un-scalable results.  Managers need a systematic approach to engineering delivery efficiency which is why managers need to take a serious look at lean engineering.

Lean Engineering

Lean engineering is about doing more with less.  Lean engineering is the application of Lean Thinking to engineering and new product development work.  Lean was popularized by James Womack and Daniel Jones in the early 90s with their book on Lean Thinking based on the Toyota Production System. Although lean is best know in the context of lean manufacturing, lean engineering methods have evolved as a systematic approach to gain efficiencies in engineering while taking into account the unique influences of uncertainty, complexity, variety, and creativity in engineering and new product development work. Direct application of lean manufacturing methods to engineering does not work because of these subtle differences from production work that is algorithmic, predictable, and repetitive. In fact drawing parallels with how lean is applied in manufacturing to engineering work in my experience leads to disappointment. Instead lean engineering needs to be understood by starting from the same basic principles of lean but then adopting an information based perspective.

Lean is based on the central principle of delivering only what the customer (internal and external) needs and eliminating all forms of waste.  Quoting Womack & Jones lean ‘is the process of reducing effort, time, space, cost, and mistakes while producing more nearly what the customer wants’ when the customer needs it.  Lean thinking is ‘a way to specify value, line up value creating actions in the best sequence, conduct activities without interruption whenever someone requests them, perform more and more efficiently’. Value creating actions are visualized as continuous work processes or value streams. While lean manufacturing focusses on the flow of physical items through a production environment, lean engineering diverges from the physical model because ‘work items‘ flowing through engineering and absorbing value derived from an engineer’s time are information based.  Engineering work items are primarily virtual in the digital age thus difficult to see in the typical engineering work environment. In fact lean engineering is really about how most knowledge based professions will need to compete in the information age going forward. Software professions are already do this with Agile scrum lean methods.

Lean engineering seeks to improve how engineering value is specified, how value creating actions are lined up in the best sequence, how to conduct engineering activities without delay whenever someone requests them, and perform engineering activities more efficiently. Lean engineering involves a change in mindset. Lean engineering requires managers and staff to ‘question the status quo’ and their prevailing assumptions in how engineering work should be performed to free up capacity in the firm. In this respect lean engineering does involve the complexities of change but the results in terms of capacity efficiencies are compelling.

Waste in Engineering Burns Capacity

Waste in engineering effort is anything that does not contribute to what the customer needs or delays when the customer’s need is satisfied. Not surprisingly examples of waste in engineering include waiting for data, unnecessary tasks, multitasking, hand overs, reinventing, engineering IS tool incompatibilities, over-engineering, rework, distractions – the list is very long.

Waste in engineering can take many forms but the impact is the same – waste burns engineering capacity unnecessarily.  So the underlying logic of lean engineering is that by eliminating waste the capacity of existing engineering talent can be freed up to meet the business needs including making more time for business success enablers such as talent career development and innovation to create new value for the business.

Lean Engineering Methods

There are two varieties of lean methods prevalent in engineering worth noting: lean engineering; and engineering for lean. Making this distinction is important because they represent two very different forms of value capture. The focus of lean engineering is the fast and efficient delivery of engineering work (thus freeing up engineering capacity) while the focus of engineering for lean is to enable lean manufacturing.   Both are important but engineering for lean often gets more attention across the business because 80-90% of a product’s costs are ‘baked in’ during design which greatly restricts buyers and production leaders from achieving cost savings. Lean engineering should be of interest to delivery managers (ie. project managers, project engineers, functional managers, depending on organization structure of the firm) who need engineering capacity to meet their commitments.

Lean engineering methods emerged in the 1990s from global competitive pressures first in the automotive industry followed very quickly by the aerospace industry.   Diffusion of automotive/aerospace lean engineering experience to other industries has been slow – awareness and lack of competitive intensity being leading reasons. Experience from the automotive and aerospace industries has demonstrated that firms can successfully implement lean engineering through value stream mapping, waste identification, process measurement, and some form of local continuous improvement methodology (usually based on DMAIC) to establish repeatable engineering processes and basic process standardization.  Firms should begin standardizing  algorithmic engineering processes first – such as detail drawing production, release, and the change process – then move can to processes involving more creative work when algorithmic processes demonstrate results.

The  basic framework then provides the opportunity for intermediate methods such as engineering work batch sizing, capacity or WIP constraints, sequence, cadence, and synchronization. These methods allow for flow to be managed between multiple engineering processes.  In the longer term more advanced lean engineering methods such as fast feedback, iteration, rapid prototyping, and simulation allow process standardization for those involving higher uncertainties where outcome is less certain – for example fuzzy front end processes supporting new products, new methods, and concept development.    Agile scrum and the Lean Start-up movement are examples of more advanced lean engineering methods being applied in extreme uncertainty situations.

Key Success Factors

In my experience successful implementation of lean engineering requires time, patience, and a methodical application of a series of solutions starting from the most basic to more advanced. Strong evidence suggests that best in class performers have committed to lean engineering for longer than five years.  In my experience improvements came steadily from the application of lean engineering over several years.   Lean engineering is a long-term investment – lean engineering is a journey.  Certainly as competitive intensity increases in an industry, like it did in automotive and aerospace, surviving firms need continuous improvement to stay in the game.

Lean engineering also requires nurturing a culture of lean within engineering and across the business.   Lean requires the application of a DMAIC methodology for continuous improvement.   Lean requires a performance measurement system for appropriate feedback and monitoring.  Lean engineering also needs to be integrated with the firm’s business processes: project management, cost accounting, and resource allocation systems.  This is achievable with the use of visual workflow methods that complement the existing systems without the need for costly investments in additional software.

Lean engineering is a solution for capacity problems that results in faster, more efficient, streamlined delivery with reduced rework. Unfortunately many firms wait until too late to begin the lean journey then don’t have enough time to respond to increased competition, increased demand, or labour shortage.  Please contact me at for help deciding if lean engineering is right for your firm or to successfully implement lean engineering methods, cultural change, and ensure results are sustainable in the long run.

The Nature of Engineering Delivery Flow

Anyone who has worked in a large, fast-paced, multi-project engineering organization understands that it can be very difficult to ensure that delivery is efficient and effective given the high degree of uncertainty involved in many engineering activities. Business profitability depends on getting this right and ‘good enough’ is ‘not good enough’.  Engineering leaders need better tools to manage engineering delivery flow.

Engineering Delivery Flow

Donald Reinertsen authored The Principles of Product Development Flow – Second Generation Lean Product Development exploring the nature of design work flow that is broadly applicable to all engineering delivery and knowledge work in general.  His work was also presented in Harvard Business Review in Six Myths of Product Development.

With the LEAN movement focused on manufacturing waste elimination little attention has been given to the delivery of knowledge work and engineering work in particular. LEAN is often applied in engineering when designing down-stream production efficiencies.  Designing for LEAN production is certainly important but does not provide much guidance to engineering leadership on how to deliver engineering work efficiently and effectively. Reinertsen’s book clarifies why LEAN, and the focus on minimizing variability in production processes in particular, does not fit engineering delivery because as Reinertsen points out ‘product development deals with high variability, nonrepetitive, and nonhomogenous flows’.  The success of LEAN in manufacturing is based on the predictable, repetitive, and homogenous nature of production work.   His book is the first comprehensive analysis of how to understand and achieve efficient and effective delivery of knowledge work based on sound science and a basis for collecting validating evidence.

Managing Engineering Flow

Reinertsen’s key points based on the inherent variability, non-repetitive, and non-homogenous nature of engineering activity are:

  • High utilization can lead to excessive queues in engineering delivery work load which have hidden costs for the business;
  • Engineering work batch size should be reduced (convincingly supported by Eric Ries in The Lean Startup);
  • The importance of tracking and managing engineering work queues;
  • How variability pooling & substitution can be used to manage variability in engineering work;
  • The importance of cadence and synchronization in engineering work flow control;
  • The importance of fast feedback loops (also convincingly supported by Eric Ries in The Lean Startup);
  • Taking a more balanced view in decentralized control of the engineering work delivery stream.

Reinertsen suggests that this is a new field with limited implementation experience.  The principles and approaches identified in these books provide a new set of management tools to manage engineering delivery.