Reddedilmelerin olumsuz yanı

Son egzersizde, prosedürü yeniden başlatmadığımız veya adayları reddetmediğimiz vakalara bakmıştık. Bu durumlarda, açık pozisyonların biraz daha hızlı doldurulduğu görülüyor. Peki, aday teklifimizi reddettiğinde etkisi ne oluyor?

filter_precedence(), önce gelen ve sonra gelen aktiviteleri belirterek aktiviteler arasındaki belirli sıralara göre vakaları seçmek için kullanılabilir.

Kullanımı şöyledir:

dataset %>%
  filter_precedence(
    antecedents = "önce gelen aktivite",
    consequents = "sonra gelen aktivite",
    precedence_type = "doğrudan mı yoksa sonunda mı takip ediyor?",
    filter_method = "hepsi mi yoksa hiçbiri mi?"
  )

Bu egzersiz

R ile İş Süreci Analitiği

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

"Receive Response" etkinliğinin ardından "Review Non Acceptance" etkinliğinin doğrudan geldiği durumların asla olmadığı vakaları filtrele. Sonucu not_refused içinde sakla. Bunu yapmak için precedence_type değerini "directly_follows" ve filter_method değerini "none" olarak ayarla.
not_refused içindeki vakaların hâlâ yüksek bir çevrim süresi var. not_refused içinden 300 günden uzun süren vakaları seçelim. Bunları worst_cases içinde sakla.
worst_cases olay günlüğündeki en sık görülen farklı izlerin yüzde 80’ine bakmak için trace_explorer() kullan.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Filter by precedence with activites not directly following
not_refused <- vacancies %>% 
  ___(
    antecedents = ___, 
    consequents = ___, 
    precedence_type = ___, 
    filter_method = ___
  ) 

# Filter for throughput time greater than 300
worst_cases <- ___ %>% 
  filter_throughput_time(interval = ___, units = ___)


# Explore the traces with 80% coverage
___

Kodu Düzenle ve Çalıştır

Bu egzersiz

R ile İş Süreci Analitiği

kursunun bir parçasıdır

IntermediárioNível de habilidade

4.8+

Kursa Ücretsiz Başlayın

The amount of event data has grown enormously during the last decades. A considerable amount of this data is recorded within the context of various business process. In this chapter, you will discover a methodology for analyzing process data, consisting of three stages: extraction, processing and analysis. You will have our first encounter with the specific elements of process data which are required for analysis, and have a first deep dive into the world of activities and traces, which will allow you to reveal of first glimpse of the process.

Exercise 1: Introduction to process analytics Exercise 2: Identify process elements Exercise 3: Process Analysis Workflow Exercise 4: Activities as cornerstones of processes Exercise 5: Retrieve basic process information Exercise 6: Explore activities Exercise 7: The happy path Exercise 8: Create process maps Exercise 9: Components of process data Exercise 10: Event data identifiers Exercise 11: Constructing event data objects Exercise 12: Inspecting event data objects

A process can be looked at from different angles: the control-flow, the performance and the organizational background. In this chapter, you will make a deep dive into each of these perspectives. The control-flow refers to the different ways in which the process can be executed, and thus, how it is structured. Considering performance, we are both interested in discovering how long things take, as well as when they take place. Finally, the organizational perspective looks at the actors in the process.

Exercise 1: Organizational analysis Exercise 2: Specialization of resources Exercise 3: Detecting roles and specializations Exercise 4: Involvement of resources Exercise 5: Structuredness Exercise 6: Process variants Exercise 7: Rework Exercise 8: Precedence matrix Exercise 9: Performance analysis Exercise 10: Eating habits Exercise 11: Eating habits - Dotted chart Exercise 12: Eat, sleep, repeat Exercise 13: Linking perspectives Exercise 14: The right person on the job I Exercise 15: The right person on the job II Exercise 16: The right person on the job faster

Event data rarely comes in a form which is ready to analyze. Therefore, you often require a set of tools to get the data in the right shape, before we can answer our research question. At the end of this chapter, you will be familiar with three common preprocessing tasks: filtering data, aggregating events and enriching data.

Exercise 1: Vaka filtreleme Exercise 2: Sık kullanılan yol Exercise 3: Redlerin olumsuz yanı Exercise 4: Reddedilmelerin olumsuz yanı

Geçerli Egzersiz

Exercise 5: Olayları filtreleme Exercise 6: Kısır reddetme döngüsü Exercise 7: Yüksek maaşlı Ar-Ge adaylarıyla çalışma Exercise 8: Olayları bir araya getirme Exercise 9: Reddetmeler reddetmedir Exercise 10: Uzaklaştırma Exercise 11: Manzaranın tadını çıkar Exercise 12: Olayları zenginleştirme Exercise 13: Her şeyin bir maliyeti var Exercise 14: Maliyet ve aciliyet

In this final chapter we will use everything we have learned so far to do and end-to-end analysis of an order-to-cash process. Firstly, we will transform data from various sources to an event log. Secondly, we will have a helicopter view of the process, exploring the dimensions of the data and the different activities, stages and flows in the process. Finally, we will combine preprocessing and analysis tools to formulate an answer to several research questions.

Exercise 1: Preparing the event data Exercise 2: Transforming Sales Data (1/3)Exercise 3: Transforming Sales Data (2/3)Exercise 4: Transforming Sales Data (3/3)Exercise 5: Putting the process together Exercise 6: Creating the event log Exercise 7: Getting to know the process Exercise 8: Examining Traces Exercise 9: Zooming Out Exercise 10: Roles and Rules Exercise 11: Resource Roles Exercise 12: The 4-Eye Principle Exercise 13: Control-flow deviations Exercise 14: Fast Production, Fast Delivery Exercise 15: Throughput time analysis Exercise 16: Course Recap