SAP Process Mining by Celonis Use Case: Purchase-to-Pay Public

SAP Process Mining by Celonis Purchase-to-Pay Process The Purchase-to-Pay Process involves an extremely high number of transactions. At the same time, approvals, timelines and many different procedures, requests, suppliers and their conditions make it extremely complex. We help procurement organizations to bring full transparency and more efficiency to their workflows. With SAP Process Mining by Celonis, it is possible to find and eliminate inefficiencies and errors in the transactional process and check for compliance and supplier performance in real time. Flexible insights allow users to increase and monitor automation rates and minimize interruptions caused by manual changes and rework activities. Watch online: Less Maverick Buying Transparency in the P2P process 35% 40% 100% More purchasing activities automated Demo video of SAP Process Mining by Celonis for the Purchase-to-Pay Process Learn more: http://www.celonis.de

SAP Process Mining by Celonis Purchase-to-Pay Process Purchase Orders Purchase Requisitions Approval Workflows SRM Shopping Carts Messages sent to vendors Invoice and payment data Change Logs Master Data (vendor, mat.,..) … SAP Process Mining by Celonis Database

Example Process Variations 1 See the happy paths 2 Explore deviations 3 Get the big picture The level of detail of the process can be seamlessly adjusted. The number of variants displayed can be reduced in order to show only the core process. Increasing the number of variants, i.e. the level of detail, the process will reveal less common paths and activities. Spot deviations and inefficient loops. Going full-throttle on the process by increasing to 100% data coverage. Nothing escapes the watchful eye – especially if this is augmented with drill-down functionalities to spot long- runners, unusual process paths, etc.

Example Rework Activities 1 2 Many steps in the purchasing process are actually rework activities. For example a price change of an already existing purchase order. This often is the root of manual effort and slows the process down. Identify deviations from a to-be process at a single glance. On all those rework activities may be filtered, and the particularities may be followed up, e.g. why there are almost 5,000 purchase orders that are created and deleted later on.

Example Throughput Times 1 2 3 Dealing with purchasing processes, it is particularly interesting to compare throughput times of different parts of the process. The process explorer allows to display throughput times directly in the process graph. This way, bottlenecks and holdups are easily spotted. The process explorer is augmented with numerous options to drill down into interesting process patterns, even to the level of a single document. The process indicates that the “Change price” activity can be a bottleneck in the SRM process. The table shows the amount of price changes by vendor. Additionally, the distribution of the throughput times of the resulting approval workflow is displayed in order to understand the impact even better.

Significant, measurable value for the customer Some examples Examplary savings from three customers made in the different parts of the P2P process using Process Mining (for details see following slides) 2.4 mio. € 500,000 € 5.2 mio. € Avoid Cash Discount Losses Use More Favourable Order Channels Minimize Manual Changes 1 mio. € Avoid Cash Discount Losses Reduce Rework 240,000 € “Relieve” the Organisation Purchase Request Purchase Order Send PO to Vendor Goods Receipt Invoice Receipt Payment 4 mio. € 100,000 € 300,000 € Rework in One SRM 1.7 mio. € Tackle Maverick Buying Task shift to Shared Service Tackle Maverick Buying Increase Automation

Business Case Manual Changes of Purchase Order Items Analysis How often do document changes occur during the purchasing process? Analysis Results We identified 157,143 changes in purchasing documents of which 75% had been conducted manually. This summed up to 117,857 manual changes. Customer Total POV p.a.: 4 bn. € # POs p.a. : 400,000 / # PO Items p.a.: 1,100,000 Measures & Potentials Using SAP Process Mining by Celonis, often occurring changes and their root causes could be identified and analyzed. In comparable use cases, manual effort could be reduced by up to 40%. Document changes in the purchasing process do not only slow down the purchasing process but significantly increase process cost: Overall Potential Results: Duration of manual change: 20 Min. Occurrences per year: 117,857 Potential for optimization: 40 % Before: 117,857 *20 Min. = 2,357,143 Min.= 39,286h Savings: 39,286 h * 40% =15,714 h/Year = 8.36 FTE = 585,106.38€/Year Please note: all screenshots used are merely exemplary and have not been taken from real customer data. Hence, they do not correspond with the calculations made in this business case. 1 FTE = 5 Days/Week * 8h * (52-5) Weeks/Year = 1880 Hours/Year = 70.000€

Business Case Rework Activities in Purchasing Analysis How often do rework activities occur during the purchasing process? Analysis Results All in all, there were 104,644 purchase orders which required manual rework during the process. Customer Total POV p.a.: 4 bn. € # POs p.a. : 400,000 / # PO Items p.a.: 1,100,000 Measures & Potentials Rework activities slowed down the process and led to significant manual effort. Using SAP Process Mining by Celonis, often occurring rework activities could be identified and reduced by up to 50%. Overall Potential Effort of Rework Activities: Savings: 3,455,090 Min * 50% = 28,792 h/Year = 15.32 FTE = 1,072,058.07€/Year Please note: all screenshots used are merely exemplary and have not been taken from real customer data. Hence, they do not correspond with the calculations made in this business case. 1 FTE = 5 Days/Week * 8h * (52-5) Weeks/Year = 1880 Hours/Year = 70.000€

Business Case Automation of the Purchasing Process Analysis How high are automation rates of process activities and can they be improved further? Analysis Results For 68% of the 400,000 purchase orders potential for automation could be identified. Customer Total POV p.a.: 4 bn. € # POs p.a. : 400,000 / # PO Items p.a.: 1,100,000 Measures & Potentials By improving the automation rate of certain activities, the efficiency of the purchasing process could be improved. In particular, the automation rates of the following activities could be optimized (amongst others): Purchase Requisition Creation Purchase Order Creation Goods Receipt Payment Block Removal Purchase Order Approval Overall Potential Results: Cases per year: 400,000 Potential for automation: 68% Time saved per case: 10 Min Savings: 400,000 * 68% * 10 Min = 45,333h/Year = 24.11 FTE/Year = 1,687,943.26€/Year Please note: all screenshots used are merely exemplary and have not been taken from real customer data. Hence, they do not correspond with the calculations made in this business case. 1 FTE = 5 Days/Week * 8h * (52-5) Weeks/Year = 1880 Hours/Year = 70.000€

Business Case Analysis of purchases with discount losses How much discount is lost in the accounting process? Analysis Results For about 14.5% of the purchasing volume, cash discounts had been agreed in the payment terms. For 35% of this purchasing volume, the agreed discount could not be realized. Customer Total POV p.a.: 4 bn. € # POs p.a. : 400,000 / # PO Items p.a.: 1,100,000 Measures & Potentials Common root causes for lost cash discounts were: Late invoice reception Late invoice bookings Late invoice approvals Wrong transfer of agreed discount from PO/Vendor master data to invoice All of these root causes could be identified with SAP Process Mining by Celonis. Overall Potential Results: Cash discount agreed for: 578 mio. € Discount not realized for: 35% Average agreed cash discount: 2.6% Savings: 578,571,428 € * 35% * 2.6% = 5,265,000 €/Year Please note: all screenshots used are merely exemplary and have not been taken from real customer data. Hence, they do not correspond with the calculations made in this business case.

Business Case Analysis of Invoices w/o ref. to PO (Maverick Buying) What is the amount of accounts payable without involvement of the purchasing department? Analysis Results Roughly 5% of the purchasing volume were spent on top as maverick buying expenses. Hence, an overall volume of about € 200 mio. was spent but not ordered via Purchasing. Customer Total POV p.a.: 4 bn. € # POs p.a. : 400,000 / # PO Items p.a.: 1,100,000 Measures & Potentials With SAP Process Mining by Celonis, identifying maverick buying was easy. It was expected that the involvement of a dedicated purchasing department for all accounts payable would lead to significant savings through optimized purchasing terms and higher standardization. By avoiding Maverick Buying, significant savings potential could be generated Overall Potential Results: Affected invoice volume: 200 mio. € Reduction of Maverick buying by 40% (from 5% to 3 % overall) Assumed average savings generated by involving purchasing department: 5% Savings: 200,000,000€ * 40% * 5% = 4,000,000€/Year Please note: all screenshots used are merely exemplary and have not been taken from real customer data. Hence, they do not correspond with the calculations made in this business case.

Business Case Global P2P Process Mining Purchase-to-Pay Reduction of Manual Changes during the Purchasing Process 585,106 € Exemplary savings of a customer with the following P2P volume: Total POV p.a.: 4 bn. € # POs p.a. : 400,000 / # PO Items p.a.: 1,100,000 Reduction of Rework Activities during the Purchasing Process 1,072,058 € Increase of Automation of the Purchasing Process 1,687,943 € Avoidance of Lost Cash Discounts 5,265,000 € Avoidance of Maverick Buying 4,000,000 € TOTAL SAVING p.a. 12,610,107 €

Business Case P2P Process Mining 4 plants of a large manufacturing company Purchase-to-Pay Rework, interruptions, automation & simplification 499,000 € Exemplary savings of a customer with the following P2P volume: Total POV p.a.: 335 mio. € (45 -130 mio. € per plant) Complexity reduction - materials 95,000 € Order and supplier bundling 202,000 € “Relieving the organization” (approvals, users) 240,000 € *improvement, but no concrete number can be assigned Process harmonization for strategic suppliers 349,000 € Procurement tasks shift from plants to Shared Service Center 1,380,000 € Payment terms (favorable payment terms shift, differences agreed vs invoiced terms, standardization) 1,715,000 € Reduce delivery times to drive down inventory 366,000 € Compliance check 0* € TOTAL SAVING p.a. 4,606,000 €

Business Case P2P Process Mining for a business unit of a global corporation Purchase-to-Pay Using more favorable order channels 2,400,000 € Standardization of manual BANF and PO handling 820,000 € Exemplary savings of a customer with the following P2P volume: Total POV p.a.: 5,1 bn. € One SRM - standard processes and deviations/ "second best processes" for catalog and free text 100,000 € SAP: Rework cases total and by root cause (e.g. order change) PO-GR 1,140,000 € Rework in OneSRM (multiple approvals) 100,000 € *improvement, but no concrete number can be assigned Transactions / POV by number of approvals/ approval complexity 240,000 € Automation degree of all process steps including invoice receipt /posted/approved 1,000,000 € Occasional users with <500 activities (excluding approvers and automated processes) 710,000 € Throughput times process total split by SAP MRP, BANF, PO & One SRM catalog & free text 0* € Reduction of avoidable cash discount losses 3,200,000 € Payment terms field maintained in vendor master, PO and invoice and deviations & Different payment terms with one supplier 4,700,000 € TOTAL SAVING p.a. 14,410,000 €

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