{"id":642,"date":"2020-02-12T17:01:12","date_gmt":"2020-02-12T17:01:12","guid":{"rendered":"https:\/\/aioutcome.ai\/?p=642"},"modified":"2021-02-09T17:31:13","modified_gmt":"2021-02-09T17:31:13","slug":"ai-outcome-analytics-help-reduce-cost-and-improve-tenant-satisfaction-2","status":"publish","type":"post","link":"https:\/\/aioutcome.ai\/en\/ai-outcome-analytics-help-reduce-cost-and-improve-tenant-satisfaction-2\/","title":{"rendered":"AI Outcome analytics help reduce cost and improve tenant satisfaction"},"content":{"rendered":"\n

This case was created to show that even if you think you have limited data, it is possible to find valuable insights and reduce the cost for a service. Data analysis is gaining momentum in creating value for real estate owners and service providers (SP).  Now if Ai Outcome can create value from limited data sets, imagine what artificial intelligence could do when BMS and larger sets of data are used. McKinsey<\/a> (June 2018) estimates up to 45% savings in project cost for infrastructure investors.<\/strong><\/p>\n\n\n\n

Canadian Building Unlimited (CBU)<\/strong><\/h2>\n\n\n\n

CBU is a large real estate owner and manager. The\ncompany grew from a few offices and commercial building to one of the preeminent\nplayers in North America with buildings in Canada, USA, Europe and Asia. Over\nthe years, their asset base grew from $250 M to $25 B through a series of acquisitions\nand organic growth.<\/p>\n\n\n\n

CBU decided to put under a long-term contract\ntheir “CBU 1 Plaza”, <\/em>a\n1.5 million square feet plus office\/retail complex recently acquired in\ndowntown Toronto. After an RFP process CBU chose Engineering Services\nTechnology (EST) because CBU valued EST\u2019s claim that it could save over 5% on\noperating costs per year excluding energy and increase tenant satisfaction. CBU\nawarded EST the full operational responsibility for “CBU 1 Plaza”<\/em>\nat a fixed cost 10-year Facility Management (FM) contract with detailed\nperformance objectives, plus four 5-year options, for a potential 30 years\ncontract.  <\/p>\n\n\n\n

EST\u2019s challenge was to meet all financial and\nperformance requirements and generate a gross margin of 15% in the first year with\nan expected average of 20% over the first 5 years.  EST management knew there would be a ramp up in\nthe first year for the CBU project. <\/p>\n\n\n\n

After 12 months, EST“CBU 1 Plaza”<\/em>\nproject team met EST executives for their performance review.  The meeting was rough as EST’s results were\nbelow target with a gross margin of 12%. EST suspected the local team did not\nunderstand the dynamics of the building well enough and would have difficulty\nramping up gross margin to the 20% average over 5 years.<\/p>\n\n\n\n

EST recognized that to push the envelope further they needed to go beyond normal operations and analyze data to find more insights on the dynamics of the CBU operations.<\/strong><\/p>\n\n\n\n

EST hires Ai Outcome (AiO) to uncover operations insights<\/strong><\/h2>\n\n\n\n

Some EST executives had heard about a start-up\ncompany performing sophisticated data analytics in the field of infrastructures\nand more specifically in buildings. After an introductory meeting a mandate was\nawarded to AiO to find ways to improve operational margins using the available\ndata.<\/p>\n\n\n\n

After cleaning and validating the CMMS (Computerized\nMaintenance Management System) data retrieved, AiO was unfortunately faced with\na limited set of usable data covering only one full year of operations. For\nexample, no Building Management System (BMS) was available as the system\nautomatically flushes 2-week-old data as new data are produced, and unfortunately\nCBU did not keep any history of the BMS data. No cost data were made available\neither.<\/p>\n\n\n\n

AiO nevertheless proceeded with the analysis using their experience both in data science and infrastructure O&M. AiO investigated a number of avenues as they correlated some data, to finally get to some interesting insights that would help EST achieve their goals. <\/p>\n\n\n\n

\n
\n

 First: optimizing Opex<\/strong><\/h3>\n\n\n\n
\"\"<\/figure>\n<\/div>\n\n\n\n
\n

AiO wanted to find a way ETS could improve\nthe mix of operational staff to be deployed in order to optimize Opex while\nensuring timely completion of all work orders. \nThey analysed the Mean Time To Resolution (MTTR) data for various\ntechnical team\/services.<\/p>\n\n\n\n

AiO recommended\nto local CBU managers talk with the technical teams on mechanical and\nelectrical issues, using some of the graphical representations. They came up\nwith some conclusions for EST executives:<\/p>\n\n\n\n

Electrical:\nInvestigate what accounted for the difficult months of January and August,\ne.g., an unusual number of incidents which required external support, delaying\nresolution time.  Could providing\nadditional training and tools to the local electrical team improve service and\nreduce subcontractor costs?<\/p>\n\n\n\n

Plumbing: Response\ntime is short, and performance is stable. Could the plumbing team be reduced by\n1 person and relocated him to another building?<\/p>\n\n\n\n

Mechanical: AiO\nused NLP(Natural Language Processing) techniques to read the notes left by the\ntechnicians on the work orders, AiO found that the absence \/ late delivery of\nsome parts resulted in abnormally long delays for the mechanical team.\n\nJanitorial \/ General Maintenance: Investigate\nwith subcontractor the potential re-balancing of hours between Janitorial and general\nmaintenance in order to optimize MTTR and reduce overtime\n\n\n\n<\/p>\n<\/div>\n<\/div>\n\n\n\n

\n
\n

Improving occupant comfort<\/strong><\/h3>\n\n\n\n
\"\"<\/figure>\n<\/div>\n\n\n\n
\n

AiO found some abnormal MTTR on noise tenant calls. If MTTR is too high on noise occupant calls, this can create dissatisfaction with the tenants. <\/p>\n\n\n\n

AiO graphs\nshowed that 1 CBU Plaza experienced an abnormally high MTTR for Mechanical\nNoise issues in the Spring and Fall, corresponding to the annual start-ups of\nthe main A\/C and heating systems, respectively.<\/p>\n\n\n\n

The high\nMTTR was not due to an abnormally-high volume of work orders since these were only\naverage for the months of April and September.<\/p>\n\n\n\n

The root\ncause of this problem, again after AiO reads the notes on\nthe work orders<\/a>, was due to late delivery \/ shortage of HVAC parts and\nconsumables at these critical periods of the year. This also created quite a\nmajor loss of time with the mechanical team.<\/p>\n\n\n\n

The\nrecommendation was to review the relevant tasks in the CMMS and ensure all parts\nand material were ordered in advance of season changes and delivered on time to\navoid shortages and time inefficiencies for the mechanical team.<\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/strong><\/p>\n\n\n\n

\n
\n

Improving tenant satisfaction<\/strong><\/h3>\n\n\n\n
\"\"<\/figure>\n<\/div>\n\n\n\n
\n

After\nanalysing the noise complaints, AiO started looking at all available data\ntagged to tenants, AiO found an interesting correlation between service calls\nand formal complaints with some tenants.<\/p>\n\n\n\n

AiO observed that Ararat Assurance and\nMatterhorn Insurance had the highest ratio of Formal Complaints to Work Orders,\nwhile Everest Financial and Kilimanjaro Telecommunications had the highest\nabsolute volume of work orders.<\/p>\n\n\n\n

Then they asked EST to look at the\nsatisfaction survey and Net Promoter Score (NPS) results they had and found\nthat the four tenants identified gave low satisfaction scores in general and\nespecially on comfort and building Operation & Management (O&M) in\ngeneral.<\/p>\n\n\n\n

EST\nrecommended to their local team to improve communications with the identified\ntenants. They organized building mechanical room visits for tenants and\noccupants, which improved tenant understanding and business relationship. <\/p>\n\n\n\n

Finally, they suggested to educate tenants and occupants on how the building systems work.<\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/p>\n\n\n\n

\n
\n

Increase subcontractor performance<\/strong><\/h3>\n\n\n\n
\"\"<\/figure>\n<\/div>\n\n\n\n
\n

AiO verified if subcontractors were\nperforming as expected. They analysed the MTTR of the four major subcontractors:\nAcme Mech.\/Elec., JJ Janitorial, Oris Elevators and Yarda Security.<\/p>\n\n\n\n

AiO observed that Acme Mech\/El and Yarda experienced\nperformance anomalies in January and July, respectively. The monthly MTTR\nperformance was stable otherwise.<\/p>\n\n\n\n

EST shared those observations with their local CBU\nteam who compared these results to tenant surveys for those months. These\nshowed a higher level of dissatisfaction with them. The analysis with\nsubcontractor\u2019s managers proved that during those difficult months, there were\nsome employees on extended leave and their replacements were definitely not up\nto the task.<\/p>\n\n\n\n

AiO observed\nthat the resolution time performance of JJ Janitorial was up and down until\nmid-year and inferior to the desired level for a janitorial subcontractor in\nsuch a prestige building. <\/p>\n\n\n\n

Although the last third showed signs of improvement, EST suggested that the CBU team initiate discussions with JJ Janitorial to implement a continuous improvement program in order to increase their performance level as well as consistency.<\/p>\n<\/div>\n<\/div>\n\n\n\n

\n
\n

Digging deeper into MTTR<\/strong><\/h3>\n\n\n\n
\"\"<\/figure>\n<\/div>\n\n\n\n
\n

AiO was surprised the number of MTTR work\norders for mechanical was so high (refer to figure 1) and decided to explore\nthis further.<\/p>\n\n\n\n

AiO wanted\nto identify tenants with abnormally high MTTR of electro-mechanical equipment; try\nto trace these measures to specific sectors \/ systems of the building and\ntarget them for further technical investigation.<\/p>\n\n\n\n

Looking at\nthe correlated results, it became clear that Meru Investments, K2 Asset\nManagement and Eiger Funds have the highest ratio of electromechanical issues\nto total Work Orders.<\/p>\n\n\n\n

EST compared these results to Mechanical drawings and\nidentified that an HVAC system was common to the three tenant suites. EST recommended\nto the local team, after investigating the condition of the said system, to\nmodify the CBU CAPEX investment plan to prioritize the upgrade or replacement\nof the offending HVAC system.<\/p>\n<\/div>\n<\/div>\n\n\n\n

After the analysis, the game plan<\/strong><\/h2>\n\n\n\n

EST were satisfied …and happily surprised with\nAiO\u2019s findings and admitted they would not have come close to those conclusions\nwithout data analytics.   <\/p>\n\n\n\n

EST could now see clear benefits:<\/p>\n\n\n\n

  1. Electrical: Savings with external\nsub-contractors.<\/li>
  2. Plumbing: Potential to reduce by 1 plumber.<\/li>
  3. Mechanical: Make the team more productive\nby providing them earlier with the appropriate material and parts in spring and\nfall.<\/li>
  4. Janitorial \/ General Maintenance:\nRe-balance janitorial and general maintenance staff to optimize MTTR, improve\ntheir performance and reduce overtime.<\/li>
  5. Improve communications with a few\nidentified tenants. This will improve their mutual understanding of needs and\nissues.<\/li>
  6. Talk to Acme mechanical\/electrical\nsubcontractor to avoid very high peak in any month.<\/li>
  7. Performing a thorough assessment of the\nHVAC system servicing complaining tenants and review the CAPEX priorities for\nits upgrade or replacement.<\/li><\/ol>\n\n\n\n

    EST realized that the data captured is never thoroughly\nanalyzed and there is an opportunity to continue this work with AiO!<\/p>\n\n\n\n

    The results<\/strong><\/h2>\n\n\n\n

    After 6 months of implementing the solutions triggered\nby the AiO analysis, EST did reach a margin of 15% and felt they could improve\nfurther their performances and reach their objective of 20% (over 5 years) within\n12 months.  The tenants are showing improvements\nin their satisfaction survey. Tenant representative visits of the technical\nrooms found to be a success and did improve the communication in their requests\nwhen calling the Customer Support Centre and their Net Promoter Score (NPS)\nwent up.<\/p>\n\n\n\n

    CBU also achieved their objectives and are now\ngearing up to collect and store their valuable data for further analysis and\nimprovements in their other buildings: BMS, IoT, call centre, financial,\nsecurity and other data will now be made available to AiO, EST\u2019s new\nvalue-added business partner, confirming the now popular quote from Peter\nSondergaard at Gartner Research:<\/p>\n\n\n\n

     \u201cInformation is the oil of the 21st century,\nand analytics is the combustion engine.\u201d<\/strong><\/p>\n\n\n\n

    What Artificial Intelligence from\nAi Outcome can do for real estate owners and service providers<\/strong><\/h2>\n\n\n\n

    Developments\nin IoT, Big Data and AI are happening fast, and experts believe that\nfundamental changes are about to happen in infrastructure operation, creating\nsignificant opportunities for real estate owners and service providers: energy\nmanagement, automation, customer service, etc. Those organizations who will\nstep up and leverage their data and appropriate analytics tools early will\nbecome dominant players in the market. <\/p>\n","protected":false},"excerpt":{"rendered":"

    This case was created to show that even if you think you have limited data, it is possible to find valuable insights and reduce the cost for a service. Data analysis is gaining momentum in creating value for real estate owners and service providers (SP).  Now if Ai Outcome can create value from limited data […]<\/p>\n","protected":false},"author":5,"featured_media":313,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[17],"tags":[],"class_list":["post-642","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ressources-en"],"acf":[],"yoast_head":"\nAI Outcome analytics help reduce cost and improve tenant satisfaction - Ai Outcome<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/aioutcome.ai\/en\/ai-outcome-analytics-help-reduce-cost-and-improve-tenant-satisfaction-2\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"AI Outcome analytics help reduce cost and improve tenant satisfaction - Ai Outcome\" \/>\n<meta property=\"og:description\" content=\"This case was created to show that even if you think you have limited data, it is possible to find valuable insights and reduce the cost for a service. 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