By Nathan Donaldson Show 14 June 2016 Tags: Agile , Development Do you work in an organisation struggling to do more with less? We come across many projects where organisations have tossed in additional resources to meet a looming deadline, pinching pennies from elsewhere and creating stress for all, without solving the underlying problem. Reducing your batch size is a key way to deliver faster, cheaper and better. How to reduce batch size in Agile software development Get five tools for reducing batch size, and take a quiz to find out how effective you currently are at keeping your batches small. Working with small batch sizes (a batch is a unit of work that passes from one stage to the next stage in a process) has tremendous impact. It improves flow and lets us deliver quickly and reach project completion earlier. In software development terms, a traditional process means a team will define all of the project’s requirements first, complete all of the design next and then finish all of the coding before testing. In contrast, working in small batch sizes means completing, for example, 10% of the design, the development and the testing before moving on to the next 10% of the product’s features. Why is batch size important?There are very good reasons why batch size is important. First up, when we work with small batch sizes, each batch makes it through the full lifecycle quicker than a larger batch does. We get better at doing things we do very often, so when we reduce batch size, we make each step in the process significantly more efficient. Smaller batch sizes also mean you’ll deliver faster and reach project completion earlier. Since work on a feature isn’t complete until it is successfully running in production and generating feedback from customers, large batch sizes delay that essential feedback. Batch size can be one of the most difficult things to optimise but it is economically crucial. Numerous studies have proven that larger batch sizes lead to longer cycle and delivery times – and a longer wait to find out if you’ve delivered value to your customer.  Faster, cheaper, betterThere’s a bunch of beneficial outcomes that come from working with much smaller batch sizes than traditional processes recommend. Some of these benefits are:
All this makes for better economics. Donald G. Reinertsen’s diagram uses testing as an example, and shows the direct links between a smaller batch size (which results in smaller changes, fewer open bugs and faster cycle times) and improved economics. Proof reducing your batch size = greater outputWe’ve even got mathematical proof that you should reduce your batch size! First published in 1954 and proven in 1961, Little’s Law has been used across many industries. At its heart, it deals with queuing systems, which is what coding-oriented projects also have to deal with. Little’s Law means that if you reduce your cycle time by the power of 10, you increase your capacity/production by a power of 10. Tips for reducing your batch sizeWhat is an ideal batch size, and how do I reduce my current batch size? Reinsertsen recommends reducing your batch size by 50%. You can’t do much damage in this range, and the damage is reversible. Observe the effects, keep reducing, and stop reducing when total cost stops improving Batch sizing is very much a horses for courses endeavour. Some large projects might favour a 30 day sprint, but for most of the projects we’re involved in, we’ve found the sweet spot is two weeks. If you’re using Agile, you should be working with small batches already. (If you’re trying to implement Agile but using the same batch size as a traditional project – that is, 100% – Agile will not work!) However, it’s important to remember these guidelines when you’re setting up your next project:
The last word goes to Reinertsen and his video: The practical science of batch size. It has all you need to know about batch size – how it works, why it works and what to do next. How to reduce batch size in Agile software development Get five tools for reducing batch size, and take a quiz to find out how effective you currently are at keeping your batches small. Further readingReducing batch size to manage risk: Story splitting case study Why small projects succeed and big ones don’t Prioritise user stories and produce more value sooner The difference between prioritising and ordering Dave Snowden on how to achieve change in complex systems through small nudges
LEARNING FROM TOYOTA: Cars are very complex machines, involving many parts, a complex assembly process, electronics, mechanics, safety and many, many steps until you get one ready for market. In many aspects it’s not so different from ‘building’ complex fingerlings or shrimp post-larvae… including complex biology, complex environment, many production steps and processes, the requirement to control continuously for imperfections (a.k.a. deformities) in order to obtain a fully ‘functional’ product that can perform efficiently at grow-out. Many production challenges in hatcheries are similar to those in complex industries and Toyota tackled these challenges with what is today known as the Toyota Production System (TPS). The TPS became the inspiration for a number of management theories and methodologies and Eric Ries’ book, “The Lean Startup” is one good example (that I have referred to in at least a couple of articles previously). One interesting aspect of the TPS is that it proposes that working in small batches is more efficient than working with large batches. Note that this came from an industry where mass production was seen as the key to business success and most companies were investing in machinery and plants to produce ever-larger batches of automobiles! The results are known: Toyota is one of the most successful car makers in the world and most others have now adopted TPS or small variations of it. Hatcheries produce fish fry by the millions and shrimp PLs by the billions; are small batches an option? Quality versus Quantity It goes like this (using an example quoted by Eric Ries): you have to prepare and post your latest product brochure in envelopes for your hundreds of customers and you have two options to go about it:
Most of us would have no doubt deciding that the first option would be fastest. Repetitive tasks become faster and overall we gain much time by working in large batches. Experience, however, reveals this is not always so! Suppose you find out, towards the end of the large batch process that the seals in the envelopes are too dry and won’t seal? All the work labeling and inserting the brochures will have to be repeated using a fresh pack of envelopes… Or the brochures turn out to be too big for the envelopes… again you have to repeat the folding step or get new, larger envelopes, and repeat all the labeling… Toyota understood that the more complex the production process the more you benefit from working with small batches. Detecting quality problems early by using small batches has a significant impact on the quantity of market-ready final product you deliver. Everyone working in a fish or shellfish hatchery knows how important it is to detect quality issues as early as possible. In fact, we all know of the waste linked to discarding hundreds of thousands of deformed fry or physiologically compromised juvenile fish or PLs that were affected by a poorly controlled step early in the production process. It is this waste that small batches try to address, but how should we apply this to industrial hatchery production? Balancing act Typically hatcheries have a number of tanks of different sizes for each production area. The size of tanks defines the minimum production batch size. Companies, however, ‘optimize’ productivity by stocking a number of tanks at a time and treating the lot as a production batch. In the bass and bream sector a batch can typically represent 2 to 6 million fry. Companies then have teams of hatchery technicians that follow this and all other batches in production. Deciding on the batch size and on the number of teams to follow batches in production is what will determine the effective batch size. This is because the batch size really depends on the attention the larvae or fry receive from the production team. When a company stocks ten tanks, each delivering 300,000 fry (so, a batch of 3,000,000 fry in production) every two weeks and has one team of three technicians that follows these tanks, then after 4-5 weeks this team will effectively be following around 9M fry, that is, three batches of 3M fry each, running in production at the same period. Necessarily there is diluted attention to each tank and to each batch and problems that affect one will most likely affect the full batch. There are some ways around this that will reduce the effective batch size; one is to increase the number of teams (personnel) in the hatchery allowing each team to follow a smaller batch more carefully and detecting any quality or development issues earlier. In the extreme we have a team of 1-2 technicians to follow each tank although this would add an extra cost to production that most likely cannot be justified by productivity gains (staff represents an important share of hatchery production costs, typically above 25%). As usual, the optimal solution will be in the middle, in a balance between the numbers of tanks (actual batch size) each team follows and the number of teams necessary to follow the batches needed to achieve the hatchery’s annual production targets. Team responsibility One way to increase attention focus of teams to batches and reduce effective batch size would be to allocate the follow-up of each batch (that can be one or more tanks) to a specific team of hatchery technicians; a bit like doctors are allocated to patients in a hospital. For each stage of production, usually defined by the area (larval, weaning, nursery, etc.) where the tanks are located, each team of technicians has the full management and responsibility for one or a few batches. When the stage ends and the larvae or fry are transferred to the next stage then the team will report on the numbers and quality indexes of the batch. An organization of this type will definitely lead to more attention paid to each batch and to earlier detection of any quality issues. In the TPS, teams are responsible for each stage of production and when a problem is detected they take two important actions, both applicable to hatchery production:
It should be clear that in order to apply the above techniques the teams must know how the batches have progressed in production and for this to be so the effective batch size should be small enough for the team to follow. The best strategy? Hatchery production, measured in terms of fry or PL produced per worker, is traditionally highly leveraged and most companies aim to increase this leverage further. However, each hatchery should look at the overall performance and especially the waste in terms of discarded batches (and all the work and nutrition and energy they cost) and at opportunities for development that depend on quality improvements (reduction in deformity rates, increase in survival, reduction in cycle times, etc.). If it is judged that there is too much waste and/or a significant opportunity for productivity improvements then smaller batch sizes, even if counter-intuitive, may be the solution for you to achieve your goals. — Diogo Thomaz Diogo Thomaz, PhD, MBA, is a Technical and Business Consultant for the aquaculture industry, based in Athens, Greece. After 15 years as R&D project manager and other industry positions he now leads Aquanetix (www.aquanetix.co.uk), a data management and reporting service for the global aquaculture industry. He also heads RealSales Ltd (www.realsales.eu) a sales consultancy company that helps businesses expand their opportunities in export markets. He can be contacted by email on Captions Figure 1 – Hatchery managers frequently opt for the large batch approach; this, however, is not necessarily the best solution as detecting quality and performance issues too late often represents a large cost for hatcheries. One option to reduce batch size and increase staff motivation is to split large batches in smaller management units and allocate specific technicians to each of the smaller batches. Figure 2 – Minimum batch size in larval rearing of marine fish is dependent on the size of the larval tanks and on the density of larvae stocked; the two hatcheries above opted for very different strategies; on the left larval tanks are large, close to 20m3 in volume whereas those on the right are around 5m3. However the hatchery on the right works with very high larval density and stocks over one million eggs per tank where the hatchery on the left stocks less than 500,000 eggs per tank. Opting for a higher density strategy, with less tanks, may allow more attention per tank by technical staff and improve production efficiency. |
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