# ERP Hypercare Staffing and Ticket Decay Calculator > Source: https://netray.co/tools/erp-hypercare-staffing-and-ticket-decay-calculator > Publisher: Netray (netray.co), AI and integration engineering for ERP and manufacturing systems. > Section: Free Tools ## Short answer Forecast hypercare tickets, how fast they fall off, and how many analysts you need in week one versus the end of hypercare. ## Category erp-operations ## Audience - ERP project manager - Support lead - IT manager ## Tool **Kind:** calculator **Currency:** $ **Inputs:** #### Active ERP users at go-live **Key:** end_users **Type:** number **Default:** 450 **Min:** 1 **Step:** 10 **Unit:** users #### Week 1 tickets per user **Key:** w1_rate **Type:** number **Default:** 0.5 **Min:** 0 **Step:** 0.05 **Unit:** tickets **Help:** Commonly between about 0.3 and 1.0 in the first week; use your own pilot data if you have it. #### Weekly ticket decline **Key:** decay **Type:** slider **Default:** 25 **Min:** 5 **Max:** 60 **Step:** 1 **Unit:** % **Help:** How much ticket volume falls each week compared with the prior week. #### Average handling time per ticket **Key:** handle_hours **Type:** number **Default:** 1.5 **Min:** 0.1 **Step:** 0.1 **Unit:** hours **Help:** Includes triage, analysis, fix or workaround and user follow-up. #### Productive hours per analyst per week **Key:** prod_hours **Type:** number **Default:** 28 **Min:** 1 **Step:** 1 **Unit:** hours **Help:** Excludes meetings and admin. #### Hypercare duration **Key:** weeks **Type:** slider **Default:** 8 **Min:** 2 **Max:** 16 **Step:** 1 **Unit:** weeks #### Loaded cost per support hour **Key:** rate **Type:** number **Default:** 75 **Min:** 0 **Step:** 5 **Unit:** $/hour **Outputs:** #### Weekly retention of volume **Key:** r **Expr:** 1 - decay / 100 **Format:** decimal #### Week 1 tickets **Key:** w1_tickets **Expr:** end_users * w1_rate **Format:** integer #### Total hypercare tickets **Key:** total_tickets **Expr:** w1_tickets * (1 - pow(r, weeks)) / (1 - r) **Format:** integer **Description:** Geometric series over the hypercare weeks. #### Total handling effort **Key:** total_hours **Expr:** total_tickets * handle_hours **Format:** hours #### Analysts needed in week 1 **Key:** peak_analysts **Expr:** ceil(w1_tickets * handle_hours / prod_hours) **Format:** integer **Primary:** true #### Tickets in the final week **Key:** last_week **Expr:** w1_tickets * pow(r, weeks - 1) **Format:** integer #### Hypercare handling cost **Key:** cost **Expr:** total_hours * rate **Format:** currency **Primary:** true **Chart:** #### Total handling hours vs hypercare duration **Type:** line **X:** weeks **From:** 2 **To:** 16 **Steps:** 15 **Series:** - total_hours **Disclaimer:** Tickets per user and decay vary widely by process complexity and training quality. Replace defaults with data from your pilot or mock cutover. ## Content **Intro:** Hypercare is staffed on guesses more often than it should be. This calculator treats ticket volume as a decaying curve: a first-week burst proportional to users, falling by a set percentage each week. It then turns volume into analysts needed, total effort and cost. **Sections:** #### How to use it **Body:** Start with active users, not named licences. Week one tickets per user depends on training quality and process change; a like-for-like upgrade sits near the low end, a full process redesign near the high end. Estimate the weekly decline from pilot sites or past go-lives. Enter handling time and productive hours per analyst, set the hypercare length, and read off the analysts required in week one, the final week volume and the total cost. **Bullets:** - Count active users, not total licences - Use pilot data for tickets per user - Keep handling time realistic, including follow-up - Plan a ramp-down schedule from the weekly figures #### The math **Body:** Let r = 1 - decline %. Week n tickets = week 1 tickets x r to the power n - 1. The total over N weeks is week 1 tickets x (1 - r to the power N) / (1 - r), the sum of a geometric series. Effort = tickets x handling hours. Analysts in week one = ceiling of week 1 tickets x handling hours / productive hours per analyst. The chart shows total effort as hypercare lengthens; the curve flattens as later weeks add few tickets. #### Using the result **Body:** Staff for week one, then release people on a schedule driven by the weekly volume, not the calendar. A common pattern is to keep functional consultants for the peak, shift to a smaller core team after the first month-end close, and exit hypercare when volume and severity fall under defined thresholds. If the final week volume is still high, hypercare should be extended rather than cut at the planned date. #### Common mistakes **Body:** Planning hypercare as a fixed calendar period ignores the first month-end, quarter-end or peak shipping week, when tickets jump again. Another error is staffing with project consultants who also have a next assignment starting. Finally, counting only formal tickets undercounts walk-ups and chat requests that consume the same hours. **Bullets:** - Ignoring the first month-end close - Consultants double booked on the next project - Missing walk-up and chat requests - No exit criteria for ending hypercare **Cta:** If you want ticket categories, repeat issues and fix suggestions drawn from your service desk and ERP logs, Netray can build an on-prem assistant for the support team. **Faq:** #### How long should hypercare last? **Answer:** Many programs run four to twelve weeks, and usually through the first month-end close. Base the end date on exit criteria such as ticket volume under a threshold and no open severity 1 or 2 issues, not only on the calendar. #### What decline rate should I use? **Answer:** Use pilot or past go-live data first. If you have none, 20 to 35 percent per week is a reasonable starting range, then test lower values to see how much extra effort a slow decline would cause. #### Does the model include second-line support? **Answer:** The handling time should cover all levels of support combined. For separate tiers, run the calculator once per tier with its own handling time and productive hours, then add the results. #### Why does effort flatten as hypercare gets longer? **Answer:** Each extra week adds a smaller share of tickets because volume keeps declining. This is why a long hypercare tail is cheaper per week than the start, but it still needs named owners. ## Search terms erp hypercare staffing, hypercare support calculator, post go-live support ticket volume, erp hypercare duration, hypercare team size, go live support staffing, hypercare ticket decay, how long should hypercare last --- Talk to an engineer: https://netray.co/contact?intent=erp_ai | Machine catalog: https://netray.co/agent/catalog.json