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This article is a guest contribution from José Coignard, ActionableAgile customer, Professional Kanban Trainer, and Agile Coach in Europe’s largest financial institution. It was originally posted on his blog. Jump down to read more about José. Story points, c'est quoi? Un peu d'historique... Ce concept largement répandu et très mal répandu (à mon plus grand désarroi, vous le comprendrez si vous allez au bout de cet article) a été initialement créé par Ron Jeffries au sein de l'extreme programming (XP). Story points (ou points d'effort, de complexité en français) est une estimation relative d'un élément de travail. Le concept englobe des notions d'effort, de complexité, de temps, de risques, de niveau de compétences, de nombre de café requis pour terminer le sujet (oups je dérive...), etc. A la base c'était une estimation en temps pour implémenter une histoire utilisateur (les éléments qui étaient à connotation de valeurs pour un utilisateur final dans XP). Ron a rapidement décrit cela comme le temps idéal pour compléter une histoire à 2 personnes en pair programming, si le monde autour d'eux voulait bien leur ficher la paix. Seulement ce n'était jamais le cas et il y avait des risques, de la complexité cachée, etc. Il a donc introduit un facteur multiplicateur à cela (dans son expérience, c'était aux alentours de 3). Ce qui a fini par lui faire transformer cette estimation en temps idéal en une notion de points car les parties prenantes ne comprenaient pas qu'une journée idéale de travail puisse se transformer finalement en 3 jours réels. (Merci les parties prenantes qui ne comprennent pas que dans ce monde de travail intellectuel, on ne peut pas voir les choses de façon déterministe... Et que c'est un véritable danger de le faire!) Bref fin de la parenthèse, revenons à nos moutons... L'idée et l'utilisation de Ron (et de sa team à l'époque) de ces story points étaient tout simplement d'avoir des discussions au sein de l'équipe et de pouvoir juger si l'équipe était en train de ce challenger sur quelque chose de faisable ou pas sur l'itération. Donc Ron transforme cette notion en point... en story points! ("Nants ingonyama bagithi baba" ... Sur un célèbre air... Le lion est né!). Et c'est la que le drame commence. Quel drame? Que s'est-il passé? Sérieusement?! Vous osez me poser la question? Bon allez parce que c'est vous, je vous explique... Un célèbre framework apparaît et prend de plus en plus d'ampleur sur le marché: Scrum.  Il faut dire que le framework est plutôt séduisant et vient avec des potentiels bénéfices intéressants (que beaucoup prendront à mon sens comme des promesses). Pour une raison propre à l'être humain toujours très inventif, des pratiquants de Scrum commencent à utiliser les story points de Ron. Sauf que... l'utilisation de ces story points commencent à dévier de l'utilité initiale. Des personnes commencent à les utiliser pour faire des projections et des plannings au-delà d'une itération, pour comparer la performance de plusieurs équipes, pour pressuriser les équipes à toujours faire plus de story points par itération (cette fameuse vélocité)... Tenez, arrêtons-nous sur ce dernier point. Est-ce que dans la notion de story points de Ron, il y a une quelconque notion de valeur pour l'utilisateur final, pour le business? Bingo! Vous avez raison... AUCUNE! Alors pourquoi donc des managers, des parties prenantes, logiquement intéressés par le fait que le business se porte bien et de mieux en mieux, viendraient mettre la pression aux équipes pour qu'elles augmentent leur vélocité?! Alors? Alors? Oui j'entends timidement la réponse au fond de la salle... "Parce qu'ils n'ont rien compris à ce que c'était ! Et pensent que plus de vélocité = plus de valeur" Merci! Ça m'évite de le dire! Malheureusement, ce non-sens se propage, c'est une maladie hautement contagieuse et virale... Le monde est pris dans cette pandémie d'utilisation des story points, à mille lieux de l'idée originelle de Ron et de l'utilité qui l'avait amené à créer cela. Le pauvre... Car malheureusement, il est bien identifié comme le créateur des story points. Il en fera d'ailleurs un mea-culpa, que je vous donne ici en français: "J'aime dire que j'ai probablement été l'inventeur des story points, et si je l'ai fait, j'en suis désolé maintenant." Bon, bah, c'est bien beau tout ça, mais comment sortir de cela? Je suis content que vous posiez la question. Donc, ce que vous voulez savoir, c'est si vous ne vous challengez pas trop sur une itération, avec un objectif inatteignable avant même d'avoir démarré. De plus potentiellement avec une communication hasardeuse auprès de parties prenantes, qui prendront cet objectif comme une promesse à la fin de l'itération et qui vous attendront de pied ferme si vous ne l'honorez pas. Bonne nouvelle, c'est possible et sans les story points ! Et je dirai même que j'ai mieux et plus prédictible que ce que vous pouviez avoir avec des story points (sous certaines conditions). Bon soyons honnête, ce que je vous donne après ce n'est pas moi qui l'ai inventé, mais les personnes qui sont derrière la stratégie Kanban (la vraie) comme Daniel S. Vacanti par exemple (Mais comme je sais qu'il tient beaucoup à rendre hommage aux personnes qui étaient avec lui, je vous donne ma traduction de son bouquin où il raconte l'histoire et rend cet hommage : https://drive.google.com/file/d/1QJu4FQdBG1iFwzn4H4wTtPT0DuMPfweM/view?usp=sharing) Par contre, j'ai pas mal utilisé cela, dans différentes équipes et je peux vous assurer que ça fonctionne très, très bien ! (et je ne suis pas le seul à le confirmer). C'est parti et je vais essayer de vous synthétiser cela en moins de 200 pages... Ce que vous voulez, c'est une estimation plutôt fiable, la plus fiable possible, quand bien même nous soyons dans un monde d'incertitude. Pouvons-nous faire ces 10 items correspondants à notre objectif, ou seulement 8, ou plutôt 15 ? Et puis aussi on le verra un peu plus tard, afin de communiquer, répondre à des questions du type "Quand est-ce que vous pensez pouvoir compléter cette version?" Et si vous comptiez simplement le nombre d'éléments? D'histoires? Et si vous regardiez historiquement ce que vous avez réussi à faire? Bah non, ça ne marchera pas, les éléments ne sont pas de la même taille! Exactement! on a des sujets plus gros que d'autres et c'est bien pour ça qu'on estime en story points! Oki ... Oki... Calmez-vous ! Laissez-moi compléter... Je ne suis pas complètement en désaccord avec vous. Mais! Pensez-vous que vos estimations en story point soient justes? Bah oui, quand même, on est rodé! Attends, c'est vrai qu' "estimation"... Le mot en lui-même comporte une part de doute, une estimation peut-être fausse. Oui, exactement! Je dirai même que vous devez partir du principe qu'elle est fausse (bon parfois, mais plutôt rarement, vous tomberez juste.) Pourquoi? Parce que vous n'êtes pas dans un monde déterministe. Tenez! À quand remonte votre dernière découverte de quelque chose que vous n'aviez pas imaginé vous tomber dessus en réalisant une story? Je parierai que ça fait 1 jour, pas plus! C'est normal... Dès que vous allez commencer le travail sur le sujet, vous allez obtenir des informations qui vous feront entrer dans la réalité, la vraie réalité des choses et puis potentiellement, il y aura un changement intéressant à mettre en œuvre pour mieux satisfaire l'utilisateur. Il va se passer tout un tas de choses imprévues, vous allez avoir l'univers contre vous pour que ça ne se passe pas comme vous l'imaginiez. Ouais, bon d'accord, ce n'est pas faux! Vous n'avez pas compris quelque chose? (Désolé pour les non-fans de Kamelot ;-)) Donc, comment faire en ne comptant que les éléments, qui ne font pas la même "taille"? Alors, ils doivent faire, non pas la même taille, mais la bonne taille. J'entends par là que vous devez convenir en équipe d'une taille, en nombre de jours (ça servira pour autre chose d'être dans cette unité.), qui sera un point de comparaison et un point limite pour les éléments que vous allez travailler. Pour être vraiment dans l'idée de voir les choses de façon non-déterministe (si si c'est important et donc je vais le dire 1000 fois dans l'article), vous allez associer à cette timebox une probabilité de rester dedans (ou si vous voulez le voir dans l'autre sens une probabilité de la dépasser). Ça donne par exemple : "Taille de nos éléments de pas plus de 10 jours à 85% du temps". Dans la stratégie Kanban c'est ce qu'on appelle le niveau de service attendu (SLE). Avec ce SLE vous allez donc pouvoir définir vos éléments avec une bonne taille, ie une taille qui entre dans ce SLE. Attention, le sujet n'est pas de tomber juste, juste. C'est "pas plus de" 10 jours... Une timebox... Si c'est fini avant. Top ! Des utilisateurs pourront potentiellement bénéficier de quelque chose qui leur sera utile plus tôt. Donc au lieu de faire des estimations en story points, points d'effort ou complexité comme on les a renommés en France via des techniques de planning poker sur suite de Fibonnacci ou autre unité aidant à la relativité de l'estimation... Vous allez jouer à un planning poker un poil transformé, avec seulement 4 cartes : - 1 carte, j'en sais fichtre rien! - 1 carte, rentre assurément dans notre SLE - 1 carte, pas moyen que ça tienne dans le SLE - 1 carte, je n'ai pas compris le sujet, on peut réexpliquer? Voici différents scénarii possibles : Plein de "j'en sais fichtre rien!"... Il faut alors très certainement rediscuter pour mieux comprendre ce qu'il en retourne... Et au bout d'un moment si ça ne s'éclaircit pas par les discussions, il faut se lancer et faire. Le seul moyen de savoir concrètement si c'est dur ou pas et si ça dépassera le SLE ou pas. Plein de "je n'ai pas compris le sujet, on peut réexpliquer?" Idem, il faut rediscuter, éclaircir... Et potentiellement se lancer. "C'est en marchant qu'on apprend à marcher !" Que des "rentre assurément dans notre SLE", bon bah allez passons au sujet suivant Pas mal ou plein de "pas moyen que ça tienne dans le SLE"... Il faut rediscuter et trouver des moyens de découper le sujet pour arriver à sortir l'élément le plus important qui entre dans le SLE et remettre le reste à des discussions futures. (ou immédiates si c'est très important pour aussi s'assurer que ça entre dans le SLE) Un mixte de tout ça... Il faut rediscuter, tout le monde n'a pas compris la même chose, il y a peut-être des idées intéressantes de découpage, etc. Oki! Une fois que circulent dans notre workflow et que se réalisent dans nos itérations des éléments de bonne taille, on va pouvoir prendre le débit (nombre d'éléments qui se terminent par jour) et utiliser cela pour faire une projection probabiliste de ce qu'il est possible de faire dans une itération (dans la timebox de l'itération). Un excellent moyen de faire cette projection probabiliste et d'utiliser une simulation de Monte-Carlo en prenant en entrée ce débit par jour. Ceci vous donnera quelque chose de beaucoup plus précis que ce que vous faisiez avant et en plus un choix de voir ce que ça donne avec 50% de probabilités, 70%, 85%, 95%... Quel niveau de risque êtes-vous prêt à prendre? Ça sera aussi des discussions que vous pourrez avoir avec cette démarche. Exemple de simulation de Monte-Carlo ci-dessous : Sur cet exemple, il y a 85% de chances que l'équipe arrive à réaliser 6 éléments ou plus dans l'itération à venir (de 3 semaines), 7 ou plus à 70% de chance, 4 ou plus à 95% de chances. Un autre avantage de cette démarche, c'est de pouvoir donner une meilleure réponse à "Quand cela sera-t-il terminé?" Avez-vous déjà réussi à tomber juste dans votre prédiction de date de release en vous basant sur les story points (ou la vélocité)? Si c'est le cas, bravo ! Vous devriez jouer au loto ;-) Je présume, en étant sûr de ne pas me tromper, que comme moi, vous n'avez jamais vu cela ou par miracle une fois et pas beaucoup plus. Attention, quand je dis ça, c'est bien évidemment en étant honnête et sans avoir trituré le périmètre, ou bafoué la qualité ou les deux, pour faire entrer absolument la release à la date convenue et communiquée. Eh bien, vous savez pourquoi? Parce qu'il n'y aucune corrélation entre les story points et la durée que vous allez prendre pour terminer les éléments. Enfin aucune corrélation... un degré de corrélation très, très faible (0,2 ... 0,3 allez max 0,4). Donc est-ce que cela fait sens de réaliser des projections sur des dates, sur la base de story points, qui ont une corrélation très faible avec la durée que ces éléments prennent en réalité pour être réalisés? Pas vraiment, ou même aucunement! Tenez c'est cadeau, un exemple de non-lien entre story points et temps de cycle (durée pour qu'un élément traverse le workflow). Exemple réel, le même schéma s'est présenté plus d'une dizaine de fois à moi. Un collègue ProKanban Trainer a fait l'exercice sur plus de 100 équipes, toujours pareil... Et toujours avez un taux de corrélation ne dépassant pas les 0,4 et plutôt autour de 0,2. Ce graphique vous présente en axe X (horizontal) l'estimation en story points, en axe Y (vertical) le temps de cycle pour terminer l'élément. Vous voyez donc que des éléments avec beaucoup de points se terminent bien plus rapidement que d'autres avec moins de points estimés. Inversement, des éléments avec peu de points estimés se terminent bien moins rapidement que des éléments avec beaucoup de points estimés. Réponse à la question concernant un seul élément Si la question ne porte que sur un seul élément, alors dépendant de là où se trouve l'élément dans le flux de travail, vous pourrez répondre grâce à votre temps de cycle historique. Exemple : L'élément n'est pas encore démarré. Votre temps de cycle historique est de 12 jours ou moins pour 85% des éléments. Vous pourriez alors répondre "si nous démarrons aujourd'hui vous avez 85% de chances que nous fournissions cet élément dans les 12 jours à venir" (Au fait, je parle en jour calendaire... Pourquoi ? Ça fera certainement l'objet d'un autre article) Exemple 2 : L'élément est démarré. Le workflow est "Affinage > Dev > Recette > Terminé" et l'élément est dans l'état "Dev" avec un âge dans le flux de 5 jours (ie il a déjà passé 5 jours entre "affinage" et "dev"). Vous pourriez alors répondre "nous avons déjà travaillé 5 jours sur le sujet, il y a maintenant moins de 85% de chances que nous finissions dans les 7 jours suivants" (Oui, car la probabilité de finir un élément déjà démarré dans la timebox du SLE décroît dès le premier jour... Ça fera certainement également l'objet d'un autre article pour entrer dans ce détail, pour le moment sachez juste que c'est moins de 85% de chances en simplifiant les choses). Réponse à la question concernant plusieurs éléments (pour une release par exemple) Dans le cas où la projection probabiliste porterait sur plusieurs éléments, il faudra prendre le débit en élément par jour pour pouvoir répondre. En réalité, c'est la même technique que pour vous lorsque vous regardez combien d'éléments, vous pouvez prendre dans votre itération, juste qu'au lieu de le voir en nombre d'éléments dans une timebox donnée, on regarde où nous emmène le fait de faire un certain nombre d'éléments. Donc débit journalier et simulation de Monte-Carlo. Attention à bien prendre la bonne période pour le débit historique. Vous devez prendre une période de débit historique qui corresponde (à priori) au mieux à l'avenir que vous projetez. Enfin, n'hésitez pas dans la réponse à la question de proposer plusieurs niveaux de risque. Vous pourriez très bien répondre : "Nous avons 70% de chances de terminer cette release pour le 15 février, 85% de terminer le 24 février et 95% de terminer le 1er mars". Cela plaira à votre interlocuteur sans aucun doute et vous pourrez convenir du niveau de risque acceptable pour lui et ainsi continuer à monitorer cette projection dès lors que vous avez de nouvelles données de débit historique. Avec la simulation de Monte-Carlo ci-dessus, pour une release comportant 40 éléments à compléter la simulation nous donne : 70% de chances de compléter cela pour le 2 Mai 2024, 85% de chance pour le 13 Mai 2024, 95% de chance pour le 21 Mai 2024. Comment convaincre de lâcher les story points et de basculer en principe de bonne taille? Je dirai avec mon expérience qu'il n'y a pas de recette miracle. Mais une qui a souvent fonctionné pour moi est de montrer la différence entre les deux approches de projections (même sans être dans une logique de bonne taille). Vous avez certainement déjà une ou plusieurs release historique. Vous connaissez donc factuellement votre date de livraison. Regardez ce que vous aviez prédit avec une projection par la vélocité (Et encore une fois, soyez honnête, prenez la première projection là où vous n'aviez pas encore trituré le périmètre, la qualité... Pour faire entrer la release au chausse pieds à la date voulue). Maintenant, prenez le débit historique par jour des éléments, faites une simulation de Monte-Carlo avec cet historique en vous remettant à la date de démarrage et avec le nombre d'éléments que vous aviez imaginé au début de cette release. Je mettrai ma main au feu, que vous avez un meilleur résultat, plus proche de la réalité avec la simulation de Monte-Carlo. Et attention, c'était sans faire une approche de bonne taille pour vos éléments. Vous allez augmenter la prédictibilité en faisant cela et en vous tenant à contrôler l'âge de vos éléments dans votre flux par rapport à ce SLE que vous choisirez. Conclusion Bon eh bien, ça n'aura pas fait un bouquin de 200 pages, mais un bien long article quand même. Si vous êtes arrivés jusque-là (en ayant tout lu), bravo! J'espère que cela vous aidera à mettre de côté les story points ou, en tout cas, la très mauvaise utilisation que beaucoup (trop) de monde en font. Je vous invite, vraiment et très sérieusement, à vous pencher sur la stratégie Kanban dans sa globalité si vous voulez vraiment réussir à vous passer des story points et avoir tous les bénéfices de cette stratégie pour optimiser votre efficacité, votre efficience et votre prédictibilité. Si vous mettez en place uniquement ce que j'ai décrit dans cet article, vous obtiendrez certainement des résultats intéressants, mais vous aurez moins de chances que cela se produise que si vous mettez en place les 3 pratiques clés Kanban appuyées des 4 métriques de flux essentiels. En tous cas, vous allez être limité à un moment sur le niveau de prédictibilité que vous allez pouvoir atteindre. Donc si vous voulez creuser tout cela, je ne peux que vous recommander de venir avec moi en formation sur la stratégie Kanban, de lire mes autres articles, de me suivre sur LinkedIn (https://www.linkedin.com/in/jose-coignard/) , de lire les ressources gratuites qui se trouvent sur https://prokanban.org, de venir vous abonner au Slack de ProKanban.org https://join.slack.com/t/prokanban/shared_invite/zt-2a4ofpd9g-7PvTd5RiV5h17tCmUdVxuA Sources : L'histoire des story points racontée par Ron lui même : https://ronjeffries.com/articles/019-01ff/story-points/Index.html About José Coignard, Guest Writer José Coignard is a French Professional Kanban Trainer and Agile Coach in Europe’s largest financial institution. As a user and advocate of ActionableAgile Analytics, he is pursuing a quest to acculturate and develop the Kanban strategy in his company and beyond for French and French-speaking people.

This customer story was originally published on March 19, 2021. We asked Ben Parry, Partner & Integration Delivery Lead at John Lewis, about his mission to reduce integration delivery lead time by 25% and how the improved metrics and reporting from ActionableAgile Analytics help make that possible. Here’s what he had to say! ActionableAgile Analytics connects my team more to outcomes and helps us to respond to trends over time. Outside my team, it’s now possible to have a common language around charts and metrics. The scope for cross-team learning is increasing. What was going on in your business that made you look for flow metrics and then eventually purchase ActionableAgile Analytics? The last five years have seen rapid growth in both our food and GM websites. Do more with less! Be disruptive! Be efficient! This push brought a focus on flow, and many people have been introduced to ActionableAgile Analytics to visualize it. I became involved as I was aware of the opportunity to act on insight from data in non-digital contexts. I believed that waiting time could be reduced/eliminated on one of the strategic projects that I was involved with. I wondered if planning could be driven more by data; one big project was reissuing plans every six weeks - was there a lighter-weight way to forecast delivery? I’d taken customer feedback that a ‘consistent sense of urgency’ would be appreciated - there seemed to be months in refinement but days for build. Soon after, I started a Lean Six Sigma Green Belt project to see if I could improve my team’s delivery. There were suspicions we were slow; could we become more efficient? To track progress over time, I invested time in getting the most from ActionableAgile Analytics. What did success look like for you at that time? Success was making waste visible and having better conversations on how to reduce them. We needed to work out which wastes were worth tackling first. I used ActionableAgile Analytics and SigmaXL to look at trends in queuing and activity cycle times. The initial goal was to concentrate on a key metric and increase awareness of that within my team. This was lead time - a customer outcome, not an activity output. The second was to communicate this upwards to my sponsor, which helped me complete my Green Belt Project. How has ActionableAgile helped to achieve that success? My approach has definitely evolved over time. I’m having better conversations about aging work. I intervene on the top ten oldest tickets or the oldest in a status. Last year, I could also justify recruitment decisions based on the arrival and departure rates on Cumulative Flow Diagrams (CFDs). Which features or benefits do you like best about ActionableAgile Analytics? Speed. The time between loading a board from JIRA and inspecting the related CFD is less than 5 minutes. There is no data refinement required - data is simply pulled from JIRA. I like zooming into a CFD and hiding statuses, etc. - something I can’t do with the native Jira CFD. ActionableAgile Analytics is great for detective work! What was the most valuable thing using ActionableAgile Analytics has brought, and why? My team is now more connected to outcomes (lead time to deliver value to system test) and our delivery trends over time. Outside my team, it’s now possible to have a common language around charts and metrics. The scope for cross-team learning is increasing. What results (qualitative or quantitative) have you seen because of using ActionableAgile Analytics? I’ve inspected how the digital agile teams manage flow and conducted an experiment to see what I could make of their data blindfolded. In doing that, I developed a repeatable process where I could build a report to look at WIP, lead time, throughput, and age in 90 minutes and then playback to the team in 30 mins. Reception to this has been positive, and I’ve had good conversations understanding why my perception of work through data may differ from the reality on the ground. E.g., a dip in throughput - “Yes, that’s where we lost a developer!” I champion the use of flow metrics and ActionableAgile Analytics as lead of our Flow Optimisation Community (>100 members). What’s next in your journey? I’m keen to look at flow beyond and within individual teams, perhaps tracking Epics across teams. At the organisation level, I’ve also been consulted on both: whether we have the right mix of work (Feature v Compliance work) as well as how to get a consistent way to train on metrics so Partners can ‘self-serve’. The worsening economy makes the hunt for waste increasingly urgent. It’s great to have the insight from ActionableAgile Analytics in my back pocket.

If you're using a Cloud version of ActionableAgile Analytics, you've probably seen frequent updates throughout the year. I'm happy to share that we have another significant update scheduled for later this month, which will mark the completion of our major 5.0 release. For Jira Data Center users looking forward to it, rest assured - version 5.0 will be ready for download around the end of June. ActionableAgile Analytics 5.0 introduces a range of new features and improvements designed to enhance your user experience and make analyzing your data easier than ever. Keep reading to find out more. What's New in ActionableAgile Analytics 5.0 Re-imagined Dashboard We've made the dashboard your starting point for all data sets. It offers a comprehensive view of key metrics, making it easier for you to track progress and make informed decisions quickly. Our eventual goal is to enhance the dashboard further by adding more details and allowing you to customize what appears there in the future. Certain elements from the previous dashboard have been carried over to the new version, such as tracking work in progress (WIP), cycle time expectations, and simulation results for how many items can likely be completed within 30 days. We've taken it a step further by including information not only for the 85th percentile but also for additional percentiles like 50th, 70th, and 95th. To address user confusion around the previous stability insights, we've introduced a Pace chart that analyzes work started versus completed each month along a combined WIP and Age chart. This chart presents three interconnected metrics on a single graph - showing, for any date, the number of items in progress, the total combined work item age (a valuable risk indicator), and the average age of individual work items. If you’re familiar with Little’s Law, you’ll see that monitoring these charts will help you establish and maintain your system’s stability! New Help Center and Newsfeed Communicating with users within the app itself is hands-down the best way to share vital information. Therefore, we’ve introduced a brand-new Help Center accessible from the question mark icon located in the bottom-right corner of the app. You can find easy links to our support portal, community site, roadmap, and more here. The Help Center features a newsfeed where we share updates on new features or other essential information. A red dot on the help center icon indicates unread newsfeed updates. We're thrilled to start this journey of discovering how we can connect with you, our valued users. Whether you are a seasoned professional or new to ActionableAgile Analytics, these tools are designed to assist you in maximizing the value you seek from the application. Easily Collapsible Sidebar Let’s be real here - diving into the app settings never really made sense just to collapse or expand the sidebar. We're stepping up our game with a sidebar that you can conveniently hide and show whenever you want, allowing you to have more screen space for what truly matters—your data. Intuitive Workflow Stages Control Long-time users of our app know that the first checked stage in the workflow stages control marks where work is considered started, and the last checked stage marks where work is first considered finished. But that's not good enough for us! We want users to intuitively know how the app works. So, we've updated the control to add visual signals so you can see how the stages are classified based on your selections. Painless Data Selection for Charts Have you had a chance to check out our new and improved date control, where you can select a specific portion of your data to analyze? In addition to dragging to select a subset of your data, you can instead use the inputs located just above. This reduces the pain users have previously experienced when trying to pick a specific date range. And don't forget, once created, you can still easily drag, drop, or even resize your selection. Enhanced Chart and Simulation UI The Flow Efficiency Chart and Monte Carlo Simulations (How Many and When) have undergone significant improvements! The updated design makes it easier for everyone to grasp the information at a glance. The purpose of these charts is now more evident, with essential details no longer tucked away in the sidebar. This marks just the start of our mission to simplify new user onboarding. We are committed to further enhancing these and the rest of our charts as we move forward. Zoom Capability Would you like to examine a specific section of your chart more closely? You can now zoom in without affecting any of the calculations activated when modifying the selected data for the chart. These small enhancements can greatly improve your analysis of complex data sets! Improved Source Data Readability We've also focused on making it easier to view the source data that ActionableAgile Analytics uses to render the charts and run the simulations. Now, we have frozen header rows and columns, which allow sorting so you can easily find what you’re looking for. And Much, Much More... These are just a few highlights of what's new in ActionableAgile Analytics 5.0. It is impossible to list every single difference as we've literally overhauled every feature and touched every line of code to bring you a more powerful and user-friendly experience. Why the Big Update? You might be curious about the reasoning behind our decision to implement such significant changes. The answer is straightforward—transitioning from legacy code empowers us to: Comprehensively grasp every facet of our codebase. Leverage modern, supported libraries such as Highcharts, facilitating the swift addition of new features and charts. However, these updates do come with a small task for our users. You'll need to reconfigure the settings in the chart controls for each data set you visit, but only once per data set. But, this change will allow us to have configurations that are compatible with features we'd like to add, such as deep-linking to a specific, full-configured chart for a data set. Join Our Live Webinar To help you get acquainted with all the new features and improvements, we're excited to announce that we're hosting a live webinar on June 25th at 15:30 (GMT+2). During this session, we'll walk you through the changes and answer any questions you might have. Please don't miss this opportunity to learn more about ActionableAgile Analytics 5.0 and get your questions answered. If you can't make it, please don’t worry. We’ll have the recording available following the event. RSVP now! Stay tuned! Are you excited to dive in? You'll be pleased to know that these updates are just around the corner, likely by the end of June—or maybe even sooner, depending on where you use ActionableAgile Analytics. We'll ensure all users see an announcement pop-up in the app once we've launched version 5.0. We're eager to hear what you think! Head on over to the 55 Degrees community and let us know!

We are thrilled to announce a fresh update to ActionableAgile™️ Analytics for Jira Cloud that's all about enhancing teamwork and boosting efficiency. Introducing the Dataset Sharing feature—our most requested enhancement yet. We listened to your feedback, and we delivered. This feature is designed to streamline your workflow, save time, and ensure everyone is on the same page with shared datasets. The Power of Dataset Sharing Imagine this: no more repetitive tasks of every team member creating identical datasets. With Dataset Sharing, you can create a dataset once and share it with anyone in Jira. Yes, it’s true—sharing really is caring in the world of data. Unlimited Sharing We’re not just talking about the ability to share a few datasets. With this update, you can share an unlimited number of datasets. Think of all the time you'll save and the collective sigh of relief as everyone stays aligned with the same set of data. Before and After Dataset Sharing Before Dataset Sharing: If everyone on a team wanted to view a dataset unless someone shared their screen, each member would have to create the dataset individually. This process was not only time-consuming but also led to inconsistencies in how the data was set up and interpreted. After Dataset Sharing: Now, one person can create the dataset and share it with the entire team. If you need help managing it or want others to edit it, you can add them as Admins. There are two levels of permissions: Admins: Can view, share, edit, and delete the dataset. Viewers: Can view the dataset as configured and use available item filters. This structure ensures that only authorized individuals can make changes, maintaining the integrity of your data. Benefits of Dataset Sharing Saving Time Creating a dataset once and sharing it with your team saves a significant amount of time. Multiply the setup time by the number of people on your team, and you can see the massive efficiency gains. Consistency When everyone had to set up datasets individually, variations often led to different interpretations of the data. With Dataset Sharing, everyone views the same dataset, ensuring consistency and accuracy in data analysis. Streamlined Collaboration Dataset Sharing fosters better collaboration by allowing team members to work from a single source of data. Admins can manage and update datasets, while viewers can interact with the data without altering it. This streamlined approach promotes a more cohesive and productive way of work. Enabling Dataset Sharing in Jira Cloud If you can't access the sharing feature, your Jira admin likely hasn't upgraded it or has disabled it. Contact your Jira admin and ask them to upgrade or enable the feature. Learn More and Get Involved To explore this feature in detail, including setup instructions, visit our Documentation Page. We value your feedback and are always eager to hear from you. If you have a feature request, visit our Product Roadmap Page and let us know what you need. You can also discover the latest updates and upcoming plans on the product roadmap page. For general questions about Dataset Sharing, head to our Community Page. If you encounter any issues, our Support Site is here to help. Sharing truly is caring in the world of data. With Dataset Sharing, we’ve made teamwork more efficient and collaboration more seamless. We hope you enjoy this feature and the value it brings to your workflows. Have a great time sharing, and feel free to let us know what other features you'd like to see in ActionableAgile™️ Analytics! You asked, and we delivered. Happy sharing! 🌟

Atlassian announced today that 55 Degrees has received Atlassian Partner of the Year 2023: Cloud Transformation Apps for their outstanding contribution and achievements during the calendar year 2023. This includes exceptional efforts in developing new business, thought leadership, and products and services that complement Atlassian. 55 Degrees was one of 28 partner recipients honored in the annual Atlassian Partner of the Year awards for their continuous efforts and exceptional customer work. We are proud of 55Degree’s achievements in 2023 and are thrilled to recognize them as the Atlassian Partner of the Year 2023: Cloud Transformation Apps," stated Keran McKenzie, Head of Ecosystem at Atlassian. "The apps offered in our Marketplace play a vital role in our customers' success. We are excited to highlight partners who have shown outstanding dedication by providing innovative app solutions and services to our customers throughout 2023.” This achievement is even more special because we are here at Atlassian Team’24 to celebrate this success together! At 55 Degrees, we create apps that help you understand how you work and how to improve your experience while getting work done. 🚀 In the coming years, our customers can expect even more innovative solutions, enhanced user experiences, and continued support to help them succeed in their work journey. We want to thank Atlassian for this incredible award and our customers for their trust and support. Your feedback and collaboration drive us to improve and innovate continually. Last but not least, a big shoutout to our amazing team! This achievement wouldn't have been possible without your hard work, dedication, and passion for excellence. Here's to even more prosperous times ahead as we continue to push the boundaries of innovation and excellence in cloud transformation apps. 🎉 Thank you to everyone who has been a part of this incredible journey with us. The best is yet to come! 🚀

We're excited to announce that 55 Degrees will again join the vibrant Atlassian community at Atlassian Team’24, happening in Las Vegas from April 30th to May 3. Team '24 is a fantastic event that brings together the Atlassian community, providing a platform to connect, collaborate, and drive advancements in how people work together, leveraging deeper human insights and breakthrough technology. At 55 Degrees, we create innovative apps to help you understand your workflows and streamline your work experience. Atlassian Team'24 is the perfect platform for us to connect with the Atlassian community and explore the future of teamwork alongside inspiring minds. What to expect when you visit our booth Get ready for a jam-packed experience at our booth! We've got something for everyone: Products Conversation: Dive into a conversation with our team about our apps and how they can transform your work. Discover how to streamline your workflow and enhance productivity with our tailored solutions. Swag Giveaways: Be sure to snag some 55 Degrees swag, including t-shirts, sleep masks, and the coolest stickers you've ever seen – each representing a member of our team. You won't find these stickers anywhere else! Rock Paper Scissors Competition: Join the excitement of the biggest competition at Team’24 – our rock paper scissors tournament! Compete against fellow attendees for a chance to win a surprise gift and bragging rights. Special Guest Appearance: We're thrilled to announce that Author and Agile Coach Daniel Vacanti will join us at our booth. Get ready to dive deep into discussions about agile methodologies, predictability, and the transformative power of ActionableAgile for businesses and work cultures. Don't miss this opportunity to gain valuable insights from an industry expert! Introducing our Team Our amazing team will be at the booth, ready to chat, answer your product-related questions, and connect with you. For Atlassian Solution Partners, Our Partner Success Manager will be there to discuss your specific needs, and of course, we'll have plenty of those epic stickers to share. We can't wait to connect with fellow Atlassian enthusiasts, share our passion for enhancing work experiences, and explore how we can collaborate to drive advancements in how people work together. Stop by Booth #12 at Atlassian Team’24 – we'll see you there!

At the heart of Malmö, Sweden, where innovation meets inspiration, 55 Degrees once again took center stage at the Øredev Developer Conference from November 8th to 10th, 2023. As a testament to our commitment to revolutionizing team workflows, our booth became a hub of meaningful conversations, exciting giveaways, and friendly competitions. Let’s take a journey through the highlights of this extraordinary event. Empowering Workflows Through Conversations Our booth became the focal point for participants seeking revolutionary solutions to elevate their organizational workflows. With passion, our team conveyed how our software products can boost collaboration and productivity for teams and organizations. The exchange of ideas and experiences ignited a shared enthusiasm for innovation. To inject some fun into our workflow discussions, we set up a fortune wheel at our booth. Attendees spun the wheel, and the excitement was contagious! Lucky participants won cool swag like beanies, baseball caps, backpacks, and exclusive tickets to our highly anticipated rock-paper-scissors competition the next day. Team Spirit in Stickers: Putting Faces to 55 Degrees To make personal connections at the conference, we shared stickers representing each member of the 55 Degrees team. It was heartwarming to see the joy on everyone’s faces as they collected these tokens of team spirit. Connecting faces to our brand resonated deeply with our commitment to building genuine customer relationships. Rock, Paper, Scissors: A Grand Finale to Remember The pinnacle of our participation unfolded the next day with the Rock, Paper, Scissors competition. The crowd gathered, anticipation filling the air, as attendees battled for the grand prize—an Airpods Pro. This spirited competition highlighted 55 Degrees’s dedication to building a lively community and served as a memorable and entertaining conclusion to the conference. This year marked our second appearance at the Øredev Developer Conference, and it was undeniably one of our favorite moments of the year. The connections forged, the excitement shared, and the lessons learned are invaluable to our ongoing journey. To everyone who stopped by our booth, engaged in conversations, spun the fortune wheel, collected stickers, and participated in the rock paper scissors showdown, thank you! Your enthusiasm and energy fueled the success of our journey at Øredev 2023. Until next year, thank you for the memories! Capturing the moments that sparked meaningful conversations, exciting giveaways, and friendly competitions.

What is SOC 2, and why is it important? SOC 2, or Service Organization Controls 2, is a framework governed by the American Institute of Certified Public Accountants (AICPA). With a SOC 2 audit, an independent service auditor will review an organization’s policies, procedures, and evidence to determine if their controls are designed and operating effectively. A SOC 2 report communicates a company’s commitment to data security and the protection of customer information. Improving your security posture SOC 2 compliance exemplifies an organization’s commitment to customer trust and is a major milestone toward improving their overall security posture. With increasing cybersecurity threats and data breaches, it is paramount that organizations prioritize information security and the protection of their systems and data. By undergoing a SOC 2 audit, our controls and processes were validated by a third party who attests to the functioning of the controls relevant to our application. Why we pursued SOC 2 now? At 55 Degrees, two of our company values are "Make things better" and "Put people first." An important part of living up to those values is our commitment to data privacy and security throughout all aspects of our organization. From recruiting and training to onboarding new tools to delivering new products and features, we don’t take a single step without ensuring we’ve taken all reasonable steps to protect your data and your privacy. SOC 2 compliance is integral in proving to customers, stakeholders, and interested parties that our organization lives up to those values and has effectively implemented security controls. At our company’s stage, we realized that it was an ideal time to pursue this as it is important to protect data and mitigate potential security risks early and on an ongoing basis. 55 Degrees’ journey to SOC 2 compliance Compliance Partners Without the right compliance partners to guide us on our journey, the task would have seemed insurmountable. There were some key partners involved in our SOC 2 compliance journey. We partnered with Vanta to help us automate the collection of our audit evidence and monitor our continued compliance. Vanta provides us with the strongest security foundation to protect our customer data. Our audit firm, Advantage Partners, was extremely helpful in creating a seamless audit experience. With their guidance and support, we were able to achieve SOC 2 compliance in a swift, efficient manner. Process While SOC 2 can be a big undertaking, our compliance partners streamlined the process. We leveraged Vanta to integrate our key systems and guide us in quickly implementing policies and procedures to become audit-ready. Vanta gave us the direction we needed to pursue our compliance journey in a much shorter timeline than we would have had without them. Advantage Partners then confirmed our audit readiness, and we kicked off our Type II audit. Advantage evaluated the controls we have in place for the audit and opined on their state. In a matter of weeks, after our audit window ended, Advantage Partners drafted and issued our report. Timeline One key takeaway is understanding that improving our security posture and achieving compliance is a monumental task. This can be made easier with the right compliance partners, but it will take dedicated focus and time from your organization. The readiness period can take the most time, but we were able to make compliance a priority to get audit-ready in just a couple of months. We also found it important to review the audit timeline with Advantage Partners, set an ideal audit date, and work backward to be ready in time. We started with the required 3-month audit window. However, now that controls are implemented, we plan to exhibit our focus and priority on security by maintaining audit readiness at all times so that subsequent SOC 2 audits will be even more seamless and cover longer audit periods. Lessons we learned Start the process early. It is easier to implement policies earlier rather than later, and doing so as early as possible helps you build a more secure organization from the start. Building secure procedures and infrastructure are key components of a successful security program. Focus on improving security posture, not checking boxes. Compliance is not one size fits all. Ensure you are spending time understanding the frameworks you’re working towards so you can know how to best comply for your organization. Your entire organization will be involved in improving security and adhering to procedures. Help them to understand how they impact your ability to stay compliant! Security is a continuous project that should be prioritized in an organization. Don’t snooze your alerts - aim to stay at 100% readiness in Vanta all the time! The right partners and tools are key. Finding a compliance management tool like Vanta to guide you through the process makes it so much easier to know what to do and to do it quickly. Not only does it make it easier to get started, but it makes it (nearly) painless to maintain your audit readiness! For us, leveling up our licenses in tools like Snyk Enterprise has really taken the pain out of managing certain aspects of our compliance and providing the necessary proof. Finally, partnering with an audit firm dedicated to your success makes the process much less scary! Make sure you find a firm that you feel comfortable with and that is comfortable in your compliance management system. You can read more about our trust stance at https://55degrees.se/trust. If you would like to request access to our SOC 2 report or see information about the controls currently in place and other publicly available documents, you can visit our Vanta Trust Center at https://trust.55degrees.se. If you have any other questions, please contact us!

In today’s rapidly evolving tech landscape, where Software as a Service (SaaS) companies continuously strive for innovation and excellence, it’s crucial to remain mindful of our responsibility to both our communities and the world at large. At 55 Degrees, we firmly believe that true success extends beyond profits and market share. We take pride in our enduring commitment to making a positive impact and giving back to society. Our dedication to putting people first has been one of our core values since the beginning, and we are constantly seeking ways to make a difference through initiatives such as the “1% Pledge”, where we donate 1% of team member time to charitable causes and 1% of our profit. Supporting the Barncancerfonden One cause that has been close to our hearts is the fight against childhood cancer. The words "Children" and "cancer" should never coexist. As an organization, we proudly support the Barncancerfonden in their fight to keep cancer away from children. The Children’s Cancer Foundation fights childhood cancer and ensures that affected children and their families receive the care and support they need. As Barnsupporter 2023, we are involved and contribute to this noble cause. Despite advancements in medical science, the fight against childhood cancer continues. By donating to organizations like the Barncancerfonden, we are helping individual families and the collective effort to find better treatments and, ultimately, a cure for childhood cancer. Every day, a child in Sweden faces the harsh reality of cancer. However, there is hope! Thanks to research and more effective treatment methods, we’ve made significant progress, with 85 percent survival rate today. However, the ultimate goal is to ensure that every child diagnosed with cancer not only survives but also leads a healthy and fulfilling life. To achieve this, we proudly support Barncancerfonden by being a Child Supporter 2023. We encourage others to join us in supporting this vital cause, and together, we can bring hope and healing to children battling cancer. Do as we do, support Barncancerfonden | Stöd barncancerforskning Together, we help keep cancer away from children!

This is post 3 of 9 in our Little's Law series. Having explained in an earlier post in the series that Little's Law (LL) comes in at least two flavors, it's time for another thought experiment. For this test, I'm going to ask you to fabricate some flow data over an arbitrarily long period of time. In order to keep the experiment as simple as possible, the requirements for our fabricated data are going to be quite specific, so please allow me to list them here: Your flow data must start with zero WIP. Trust me, the experiment works equally well if you start with non-zero WIP, but in order to eliminate the possibility of certain edge cases occurring, let's all start with zero WIP. For the whole period of time under consideration, the arrival rate of your data must be constant. For example, if the arrival rate for the first day is two items, then the arrival rate for the second day must be two items, as well as two items for the third day, etc., for the whole span of your dataset. Likewise, the departure rate (Throughput) for your data must be constant for the whole time period under consideration AND must be less than your arrival rate. This should make sense. If we start with zero WIP, it would be impossible to have a constant departure rate greater than your arrival rate--otherwise, your WIP would turn negative (which, of course, is impossible). So, for example, if the departure rate for the first day of your dataset is one item, then the departure rate for the second day must also be one item, as well as one item for the third day, and so on for the whole span of your dataset. Items must move through your process and complete in strict first-in-first-out (FIFO) order. Again, this need not be strictly necessary, but it makes conjuring your dataset easier. The length of time for your dataset is completely up to you, but make it realistic, say, the length of one or two Sprints, the length of one of your releases, or the like. Got it? (I'm hoping the reasons for the specificity of these requirements will become clear shortly.) You'll recall from this earlier post that to calculate flow metrics, all you need to have is the start date and end date of each item that moves through your system. Thus, the following (Figure 1) might be some example data that we might use for this experiment: Figure 1 - Sample data Please note (and please forgive) the use of American-style dates above. "3/1/2023" is 1 March 2023, not 3 January 2023. You'll further recall from that earlier post that it is rather straightforward to calculate flow metrics from our item date data: Figure 2 - Flow Metrics Calculated From Sample Data Figure 2 above shows the arrival rate, Throughput, Cycle Time, and WIP for every single day of the time period under consideration (again, using American-style dates). The astute reader will notice the mathematically correct nuance of how the averages were calculated, which I hope to address in a future post. Now that we have all of our flow metrics derived, we can do some LL calculations for comparisons. We left off last time by pointing out that we have two versions of LL to deal with. The first is L = λ * W Where L is the average queue length (WIP), λ is the average arrival rate, and W is the average wait time (Cycle Time). Plugging in numbers from Figure 2, we have L = 7, λ = 2, and W = 3.5, or 7 = 2 * 3.5 which, of course, is correct. However, in the case of the second form of LL: WIP = TH * CT where WIP is the average work in progress, TH is the average Throughput, and CT is the average Cycle Time; when we plug in numbers, we get WIP = 7, TH = 1, and CT = 3.5, or 7 = 1 * 3.5 which, of course, is NOT correct. So how is it that we can have two forms of a "law" where one is correct, and the other is incorrect? Any mathematical theorem you can think of comes with a set of assumptions that must be in place in order for the theorem to be valid. I can guarantee you that the problem isn't with LL. The problem is with us and our understanding of LL. Let me explain. Any mathematical theorem you can think of comes with a set of assumptions that must be in place in order for the theorem to be valid. Violate any one (or more) of the assumptions at any time (or times), and the results you get in practice will not match the theory. For example, the fundamental theorem of calculus requires that you are dealing with (amongst other things) real-valued, continuous functions. Unless you are a mathematics geek, you may not know what any of that means but violate any one of those assumptions, and most of what you learned in your Calc 101 class becomes meaningless. Because it is an equation, most people want to rush to plug numbers in [Little's Law] to see what comes out the other side--without really understanding what they are doing. Little's Law is no different. It's worse, even. Because it is an equation, most people want to rush to plug numbers in to see what comes out the other side--without really understanding what they are doing. The prevailing Lean-Agile literature perpetuates this myth by suggesting you can do just that. (I'm loathe to give any examples here lest I become part of the problem, but just search the interwebs on your own for Little's Law in Agile, and you will see what I mean). What's worse is that many of those "sources" will tell you that you can use Little's Law as a predictor for what will happen if you take specific action. In other words, Little's Law will tell you exactly what your Cycle Time will be if you cut your WIP in half (spoiler alert: it won't). Another way of saying the above is that most people see L = λW, and they want to treat it like E = mc^2^ or F = ma. That is to say, they want to plug two of the three parameters into the equations to see if they can predict what the third parameter will be in some future state of the system. So if our current WIP is 12 and our current Throughput is 2, then all we need to do to get our future Cycle Time down to 3 is to cut our WIP in half while keeping our Throughput at 2 at the same time. I'm sorry to say it doesn't work like that. At all. The work to dispel these myths will start with the next post in this series. It will be a bit of a slog, and the minutia might seem tedious, but my hope is that if you stick with it, you will gain a much deeper appreciation for the law. That detailed discussion of what assumptions need to be in place for LL to be valid and how those assumptions apply to your own process data begins next. Explore all entries in this series When an Equation Isn't Equal A (Very) Brief History of Little's Law The Two Faces of Little's Law (this article) One Law. Two Equations It's Always the Assumptions The Most Important Metric of Little's Law Isn't In the Equation How NOT to use Little's Law Other Myths About Little's Law Little's Law - Why You Should Care About Daniel Vacanti, Guest Writer Daniel Vacanti is the author of the highly-praised books "When will it be done?" and "Actionable Agile Metrics for Predictability" and the original mind behind the ActionableAgile™️ Analytics Tool. Recently, he co-founded ProKanban.org, an inclusive community where everyone can learn about Professional Kanban, and he co-authored their Kanban Guide. When he is not playing tennis in the Florida sunshine or whisky tasting in Scotland, Daniel can be found speaking on the international conference circuit, teaching classes, and creating amazing content for people like us.

This post is part 2 in our Little's Law series. You might think that the history of the relationship L = λ * W (Eq. 1) would start with the publication of Dr. Little's seminal paper in 1961 [reference #1]. The reality is that we must begin by going back a bit further. What the symbols in the above figure (Eq. 1) mean will be discussed a little later. Evidence points to queuing theorists applying (Eq. 1) in their work well before 1961--seemingly without ever providing a rigorous mathematical proof as to its validity. The earliest pre-1961 example that I could find (in a semi-exhaustive search) was a paper written in 1953 called "Priority Assignment in Waiting Line Problems" by Alan Cobham [reference #2]. Somewhat coincidentally (for those who know me), this paper applies (Eq. 1) to prove the dangers of prioritization schemes to the overall predictability of queuing systems. (As an interesting aside, a quote from that paper is, "any increase in the relative frequency of priority 1 units increases not only the expected delay for units of that priority level but for units of all other levels as well."--in other words, we knew about the dangers of classes of service at least as early as the 1950s!) It would seem that (Eq. 1) was not only acknowledged well in advance of 1953, but it was also widely accepted as true even then. We knew about the dangers of classes of service as early as the 1950s! For the purposes of our story, however, the most important person before 1961 to recognize the need for a more rigorous proof of (Eq. 1) was Philip M. Morse. In 1958, Morse had published an Operations Research (OR) textbook called "Queues, Inventories, and Maintenance." [reference #3] In that book, Morse provided heuristic proofs that (Eq. 1) holds for very specific queuing models but commented that it would be useful to have the relationship proved for the general case (i.e., for all queues, not just for specific, individual models). In Morse's words, "we have now shown that...the relation between the mean number [L] and mean delay [W] is via the factor λ, the arrival rate: L = λW, and we will find, in all the examples encountered in this chapter and the next, for a wide variety of service and arrival distributions, for one or for several channels, that this same relationship holds. Those readers who would like to experience for themselves the slipperiness of fundamental concepts in this field and the intractability of really general theorems might try their hand at showing under what circumstances this simple relationship between L and W does not hold." Somewhat serendipitously, circa 1960, Dr. John Little was teaching an OR course at Case Institute of Technology in Cleveland (now Case Western Reserve University) and was using Morse's textbook as part of the curriculum. During one class, Little had introduced (Eq. 1) and commented (as Morse had) that it seemed to be a very general relationship. According to Little himself, "After class, I was talking to a number of students, and one of them (Sid Hess) asked, 'How hard would it be to prove it in general?' On the spur of the moment, I obligingly said, 'I guess it shouldn't be too hard.' Famous last words. Sid replied, 'Then you should do it!'" [reference #4] Little took up the challenge, went away for the summer in 1961 to come up with a general proof for (Eq. 1), wrote up his findings in a paper, submitted the proof to the periodical Operations Review, and had his submission accepted on the first round. His paper has since become one of the most frequently referenced articles in Operations Review's history. [reference #5] As such, the relationship L = λ * W quickly became more commonly known as Little's Law (LL). The real beauty of Little's general proof--apart from not relying on any specific queuing model--was all of the other things you didn't need to know in order to apply the law. For instance, you didn't need to have any detailed knowledge about inter-arrival times, service times, number of servers, order of service, etc., that you normally needed for queuing theory. [This point will become of monumental importance when we talk about applying LL to Agile.] In the years after its first publication, LL found applications far beyond OR. One such application was in the area of Operations Management (OM). OM is a bit different than OR because OM is generally more focused on output rather than input. Consider the perspective of an operations manager in a factory. A factory manager's primary focus is output because the whole reason a factory exists is to produce "things" (factories don't exist to take in "things"). Because of this potentially differing perspective, in the OM world, LL is usually stated in terms of throughput (TH or departures) rather than arrivals; work in progress (WIP) rather than queue length; and cycle time (CT) rather than wait time [reference #6]: WIP = TH * CT (Eq. 2) It's fairly easy to see that (Eq. 1) and (Eq. 2) are equivalent; however, the change in focus from arrivals to departures will require a nontrivial amount of care that we will get into in a later post. The reason I mention (Eq. 2) is because this is the form of LL that the Agile community seems to have preferred, and so it is here that our brief history ends and the real story begins. So why should you be concerned about any of this? There are a couple of reasons, really. First, practitioners should acknowledge that any doubts about the legitimacy of the theory have been settled for 70 years or more. There is simply no question about the validity of LL or its place in the management of flow. Second, because most agile practitioners have only seen LL in the form of (Eq. 2) and not (Eq. 1), it is important for them to understand where (Eq. 2) really comes from. It's not just a matter of simply substituting variable names, and Robert is your father's brother. There is simply no question about the validity of Little's Law or its place in the management of flow. This brings us to the fact that we actually have two forms of Little's Law to consider: L = λ * W and WIP = TH * CT But which one do we use and when? I'm glad you asked because that will be the topic of the next post in this series... Explore all entries in this series When an Equation Isn't Equal A (Very) Brief History of Little's Law (this article) The Two Faces of Little's Law One Law. Two Equations It's Always the Assumptions The Most Important Metric of Little's Law Isn't In the Equation How NOT to use Little's Law Other Myths About Little's Law Little's Law - Why You Should Care About Daniel Vacanti, Guest Writer Daniel Vacanti is the author of the highly-praised books "When will it be done?" and "Actionable Agile Metrics for Predictability" and the original mind behind the ActionableAgile™️ Analytics Tool. Recently, he co-founded ProKanban.org, an inclusive community where everyone can learn about Professional Kanban, and he co-authored their Kanban Guide. When he is not playing tennis in the Florida sunshine or whisky tasting in Scotland, Daniel can be found speaking on the international conference circuit, teaching classes, and creating amazing content for people like us. References Little, J. D. C. A proof for the queuing formula: L = λ W. Operations Research. 9(3) 383–387, 1961. Alan Cobham, Journal of the Operations Research Society of America, Vol. 2, No. 1 (Feb. 1954), pp. 70-76 Morse, P. M. (1958) Queues, Inventories and Maintenance, Publications in Operations Research, No.1, John Wiley, New York. Little, J. D. C., S. C. Graves. 2008. Little's Law. D. Chhajed, T. J. Lowe, eds. Building Intuition: Insights from Basic Operations Management Models and Principles. Springer Science + Business Media LLC, New York. Whitt, W. 1991. A review of L = λW and extensions. Queueing Systems 9(3) 235–268. Hopp, W. J., M. L. Spearman. 2000. Factory Physics: Foundations of Manufacturing Management, 2nd ed. Irwin/McGraw-Hill, New York.

This is post 4 of 9 in our Little's Law series. In the previous post, we demonstrated how the two different forms of Little's Law (LL) can lead to two very different answers even when using the same dataset. How can one law lead to two answers? As was suggested, the applicability of any theory depends completely on one's understanding of the assumptions that need to be in place in order for that given theory to be valid. However, in the case of LL, we have two different equations that purport to express one single theory. Does having two equations require having two sets of assumptions (and potentially two types of applicability)? In a word, yes. Recall that the L = λW (this is the version based on arrival rate) came first, and in his 1961 proof, Little stated his assumptions for the formula to be correct: "if the three means are finite and the corresponding stochastic process strictly stationary, and, if the arrival process is metrically transitive with nonzero mean, then L = λW." There's a lot of mathematical gibberish in there that you don't need to know anyway because it turns out Little's initial assumptions were overly restrictive, as was demonstrated by subsequent authors (reference #1). All you really need to know is that--very generally speaking--LL is applicable to any process that is relatively stable over time [see note below]. For our earlier thought experiment, I took this notion of stability to an extreme in order to (hopefully) prove a point. In the example data I provided, you'll see that arrivals are infinitely stable in that they never change. In this ultra-stable world, you'll note that the arrivals form of LL works--quite literally--exactly the way that it should. That is to say, when you plug two numbers into the equation, you get the exact answer for the third. Things change dramatically, however, when we start talking about the WIP = TH * CT version of the law. Most people assume--quite erroneously--that this latter form of LL only requires the same assumptions as the arrivals version. However, Dr. Little is very clear that changing the perspective of the equation from arrivals to departures has a very specific impact on the assumptions that are required for the law to be valid. Let's use Little's own words for this discussion: "At a minimum, we must have conservation of flow. Thus, the average output or departure rate (TH) equals the average input or arrival rate (λ). Furthermore, we need to assume that all jobs that enter the shop will eventually be completed and will exit the shop; there are no jobs that get lost or never depart from the shop...we need the size of the WIP to be roughly the same at the beginning and end of the time interval so that there is neither significant growth nor decline in the size of the WIP, [and] we need some assurance that the average age or latency of the WIP is neither growing nor declining." (reference #2) "At a minimum, we must have conservation of flow." Allow me to put these in a bulleted list that will be easier for your reference later. For a system being observed for an arbitrarily long amount of time: Average arrival rate equals average departure rate All items that enter a workflow must exit WIP should neither be increasing nor decreasing Average age of WIP is neither increasing nor decreasing Consistent units must be used for all measures I added that last bullet point for clarity. It should make sense that if Cycle Time is measured in days, then Throughput cannot be measured in weeks. And don't even talk to me about story points. If you have a system that obeys all of these assumptions, then you have a system in which the TH form of Little's Law will apply. If you have a system that obeys all of these assumptions, then you have a system in which the TH form of Little's Law will apply. Wait, what's that you say? Your system doesn't follow these assumptions? I'm glad you pointed that out because that will be the topic of our next post. A note on stability Most people have an incorrect notion of what stability means. "Stable" does not necessarily mean "not changing." For example, Little explicitly states aspects of a system that L = λW is NOT dependent on and, therefore, may reasonably change over time: size of items, order of items worked on, number of servers, etc. That means situations like adding or removing team members over time may not be enough to consider to a process "unstable." However, to take an extreme example, it would be easy to see that all of the restrictions/changes imposed during the 2020 COVID pandemic would cause a system to be unstable. From a LL perspective, only when all 5 assumptions are met can a system reasonably be considered stable (assuming we are talking about the TH form of LL). References Whitt, W. 1991. A review of L = λW and extensions. Queueing Systems 9(3) 235–268. Little, J. D. C., S. C. Graves. 2008. Little's Law. D. Chhajed, T. J. Lowe, eds. Building Intuition: Insights from Basic Operations Management Models and Principles. Springer Science + Business Media LLC, New York. Explore all entries in this series When an Equation Isn't Equal A (Very) Brief History of Little's Law The Two Faces of Little's Law One Law. Two Equations (this article) It's Always the Assumptions The Most Important Metric of Little's Law Isn't In the Equation How NOT to use Little's Law Other Myths About Little's Law Little's Law - Why You Should Care About Daniel Vacanti, Guest Writer Daniel Vacanti is the author of the highly-praised books "When will it be done?" and "Actionable Agile Metrics for Predictability" and the original mind behind the ActionableAgile™️ Analytics Tool. Recently, he co-founded ProKanban.org, an inclusive community where everyone can learn about Professional Kanban, and he co-authored their Kanban Guide. When he is not playing tennis in the Florida sunshine or whisky tasting in Scotland, Daniel can be found speaking on the international conference circuit, teaching classes, and creating amazing content for people like us.